Title: | Tools for Single Cell Genomics |
---|---|
Description: | A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>, and Hao, Hao, et al (2020) <doi:10.1101/2020.10.12.335331> for more details. |
Authors: | Andrew Butler [ctb] |
Maintainer: | Paul Hoffman <[email protected]> |
License: | MIT + file LICENSE |
Version: | 4.4.0 |
Built: | 2024-08-13 13:32:40 UTC |
Source: | https://github.com/satijalab/seurat |
A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) doi:10.1038/nbt.3192, Macosko E, Basu A, Satija R, et al (2015) doi:10.1016/j.cell.2015.05.002, Stuart T, Butler A, et al (2019) doi:10.1016/j.cell.2019.05.031, and Hao, Hao, et al (2020) doi:10.1101/2020.10.12.335331 for more details.
Seurat uses the following [options()] to configure behaviour:
Seurat.memsafe
global option to call gc() after many operations. This can be helpful in cleaning up the memory status of the R session and prevent use of swap space. However, it does add to the computational overhead and setting to FALSE can speed things up if you're working in an environment where RAM availability is not a concern.
Seurat.warn.umap.uwot
Show warning about the default backend
for RunUMAP
changing from Python UMAP via reticulate to UWOT
Seurat.checkdots
For functions that have ... as a parameter, this controls the behavior when an item isn't used. Can be one of warn, stop, or silent.
Seurat.limma.wilcox.msg
Show message about more efficient Wilcoxon Rank Sum test available via the limma package
Seurat.Rfast2.msg
Show message about more efficient Moran's I function available via the Rfast2 package
Seurat.warn.vlnplot.split
Show message about changes to default behavior of split/multi violin plots
Maintainer: Paul Hoffman [email protected] (ORCID)
Other contributors:
Andrew Butler [email protected] (ORCID) [contributor]
Saket Choudhary [email protected] (ORCID) [contributor]
Charlotte Darby [email protected] (ORCID) [contributor]
Jeff Farrell [email protected] [contributor]
Christoph Hafemeister [email protected] (ORCID) [contributor]
Yuhan Hao [email protected] (ORCID) [contributor]
Austin Hartman [email protected] (ORCID) [contributor]
Jaison Jain [email protected] (ORCID) [contributor]
Madeline Kowalski [email protected] (ORCID) [contributor]
Efthymia Papalexi [email protected] (ORCID) [contributor]
Patrick Roelli [email protected] [contributor]
Rahul Satija [email protected] (ORCID) [contributor]
Karthik Shekhar [email protected] [contributor]
Avi Srivastava [email protected] (ORCID) [contributor]
Tim Stuart [email protected] (ORCID) [contributor]
Kristof Torkenczy (ORCID) [contributor]
Shiwei Zheng [email protected] (ORCID) [contributor]
Satija Lab and Collaborators [funder]
Useful links:
Report bugs at https://github.com/satijalab/seurat/issues
Add mapping and prediction scores, UMAP embeddings, and imputed assay (if
available)
from Azimuth to an existing or new Seurat
object
AddAzimuthResults(object = NULL, filename)
AddAzimuthResults(object = NULL, filename)
object |
A |
filename |
Path to Azimuth mapping scores file |
object
with Azimuth results added
## Not run: object <- AddAzimuthResults(object, filename = "azimuth_results.Rds") ## End(Not run)
## Not run: object <- AddAzimuthResults(object, filename = "azimuth_results.Rds") ## End(Not run)
Add mapping and prediction scores from Azimuth to a
Seurat
object
AddAzimuthScores(object, filename)
AddAzimuthScores(object, filename)
object |
A |
filename |
Path to Azimuth mapping scores file |
object
with the mapping scores added
## Not run: object <- AddAzimuthScores(object, filename = "azimuth_pred.tsv") ## End(Not run)
## Not run: object <- AddAzimuthScores(object, filename = "azimuth_pred.tsv") ## End(Not run)
Calculate the average expression levels of each program (cluster) on single cell level, subtracted by the aggregated expression of control feature sets. All analyzed features are binned based on averaged expression, and the control features are randomly selected from each bin.
AddModuleScore( object, features, pool = NULL, nbin = 24, ctrl = 100, k = FALSE, assay = NULL, name = "Cluster", seed = 1, search = FALSE, ... )
AddModuleScore( object, features, pool = NULL, nbin = 24, ctrl = 100, k = FALSE, assay = NULL, name = "Cluster", seed = 1, search = FALSE, ... )
object |
Seurat object |
features |
A list of vectors of features for expression programs; each entry should be a vector of feature names |
pool |
List of features to check expression levels against, defaults to
|
nbin |
Number of bins of aggregate expression levels for all analyzed features |
ctrl |
Number of control features selected from the same bin per analyzed feature |
k |
Use feature clusters returned from DoKMeans |
assay |
Name of assay to use |
name |
Name for the expression programs; will append a number to the
end for each entry in |
seed |
Set a random seed. If NULL, seed is not set. |
search |
Search for symbol synonyms for features in |
... |
Extra parameters passed to |
Returns a Seurat object with module scores added to object meta data;
each module is stored as name#
for each module program present in
features
Tirosh et al, Science (2016)
## Not run: data("pbmc_small") cd_features <- list(c( 'CD79B', 'CD79A', 'CD19', 'CD180', 'CD200', 'CD3D', 'CD2', 'CD3E', 'CD7', 'CD8A', 'CD14', 'CD1C', 'CD68', 'CD9', 'CD247' )) pbmc_small <- AddModuleScore( object = pbmc_small, features = cd_features, ctrl = 5, name = 'CD_Features' ) head(x = pbmc_small[]) ## End(Not run)
## Not run: data("pbmc_small") cd_features <- list(c( 'CD79B', 'CD79A', 'CD19', 'CD180', 'CD200', 'CD3D', 'CD2', 'CD3E', 'CD7', 'CD8A', 'CD14', 'CD1C', 'CD68', 'CD9', 'CD247' )) pbmc_small <- AddModuleScore( object = pbmc_small, features = cd_features, ctrl = 5, name = 'CD_Features' ) head(x = pbmc_small[]) ## End(Not run)
Returns aggregated (summed) expression values for each identity class
AggregateExpression( object, assays = NULL, features = NULL, return.seurat = FALSE, group.by = "ident", add.ident = NULL, slot = "data", verbose = TRUE, ... )
AggregateExpression( object, assays = NULL, features = NULL, return.seurat = FALSE, group.by = "ident", add.ident = NULL, slot = "data", verbose = TRUE, ... )
object |
Seurat object |
assays |
Which assays to use. Default is all assays |
features |
Features to analyze. Default is all features in the assay |
return.seurat |
Whether to return the data as a Seurat object. Default is FALSE |
group.by |
Categories for grouping (e.g, ident, replicate, celltype); 'ident' by default |
add.ident |
(Deprecated) Place an additional label on each cell prior to pseudobulking (very useful if you want to observe cluster pseudobulk values, separated by replicate, for example) |
slot |
Slot(s) to use; if multiple slots are given, assumed to follow the order of 'assays' (if specified) or object's assays |
verbose |
Print messages and show progress bar |
... |
Arguments to be passed to methods such as |
If slot is set to 'data', this function assumes that the data has been log
normalized and therefore feature values are exponentiated prior to aggregating
so that sum is done in non-log space. Otherwise, if slot is set to
either 'counts' or 'scale.data', no exponentiation is performed prior to
aggregating
If return.seurat = TRUE
and slot is not 'scale.data', aggregated values
are placed in the 'counts' slot of the returned object and the log of aggregated values
are placed in the 'data' slot. For the ScaleData
is then run on the default assay
before returning the object.
If return.seurat = TRUE
and slot is 'scale.data', the 'counts' slot is left empty,
the 'data' slot is filled with NA, and 'scale.data' is set to the aggregated values.
Returns a matrix with genes as rows, identity classes as columns.
If return.seurat is TRUE, returns an object of class Seurat
.
data("pbmc_small") head(AggregateExpression(object = pbmc_small))
data("pbmc_small") head(AggregateExpression(object = pbmc_small))
The AnchorSet class is an intermediate data storage class that stores the anchors and other
related information needed for performing downstream analyses - namely data integration
(IntegrateData
) and data transfer (TransferData
).
object.list
List of objects used to create anchors
reference.cells
List of cell names in the reference dataset - needed when performing data transfer.
reference.objects
Position of reference object/s in object.list
query.cells
List of cell names in the query dataset - needed when performing data transfer
anchors
The anchor matrix. This contains the cell indices of both anchor pair cells, the anchor score, and the index of the original dataset in the object.list for cell1 and cell2 of the anchor.
offsets
The offsets used to enable cell look up in downstream functions
anchor.features
The features used when performing anchor finding.
neighbors
List containing Neighbor objects for reuse later (e.g. mapping)
command
Store log of parameters that were used
Add info to anchor matrix
AnnotateAnchors(anchors, vars, slot, ...) ## Default S3 method: AnnotateAnchors( anchors, vars = NULL, slot = NULL, object.list, assay = NULL, ... ) ## S3 method for class 'IntegrationAnchorSet' AnnotateAnchors( anchors, vars = NULL, slot = NULL, object.list = NULL, assay = NULL, ... ) ## S3 method for class 'TransferAnchorSet' AnnotateAnchors( anchors, vars = NULL, slot = NULL, reference = NULL, query = NULL, assay = NULL, ... )
AnnotateAnchors(anchors, vars, slot, ...) ## Default S3 method: AnnotateAnchors( anchors, vars = NULL, slot = NULL, object.list, assay = NULL, ... ) ## S3 method for class 'IntegrationAnchorSet' AnnotateAnchors( anchors, vars = NULL, slot = NULL, object.list = NULL, assay = NULL, ... ) ## S3 method for class 'TransferAnchorSet' AnnotateAnchors( anchors, vars = NULL, slot = NULL, reference = NULL, query = NULL, assay = NULL, ... )
anchors |
An |
vars |
Variables to pull for each object via FetchData |
slot |
Slot to pull feature data for |
... |
Arguments passed to other methods |
object.list |
List of Seurat objects |
assay |
Specify the Assay per object if annotating with expression data |
reference |
Reference object used in |
query |
Query object used in |
Returns the anchor dataframe with additional columns for annotation metadata
Convert objects to CellDataSet objects
as.CellDataSet(x, ...) ## S3 method for class 'Seurat' as.CellDataSet(x, assay = NULL, reduction = NULL, ...)
as.CellDataSet(x, ...) ## S3 method for class 'Seurat' as.CellDataSet(x, assay = NULL, reduction = NULL, ...)
x |
An object to convert to class |
... |
Arguments passed to other methods |
assay |
Assay to convert |
reduction |
Name of DimReduc to set to main reducedDim in cds |
Seurat
objectsConvert objects to Seurat
objects
## S3 method for class 'CellDataSet' as.Seurat(x, slot = "counts", assay = "RNA", verbose = TRUE, ...) ## S3 method for class 'SingleCellExperiment' as.Seurat( x, counts = "counts", data = "logcounts", assay = NULL, project = "SingleCellExperiment", ... )
## S3 method for class 'CellDataSet' as.Seurat(x, slot = "counts", assay = "RNA", verbose = TRUE, ...) ## S3 method for class 'SingleCellExperiment' as.Seurat( x, counts = "counts", data = "logcounts", assay = NULL, project = "SingleCellExperiment", ... )
x |
An object to convert to class |
slot |
Slot to store expression data as |
assay |
Name of assays to convert; set to |
verbose |
Show progress updates |
... |
Arguments passed to other methods |
counts |
name of the SingleCellExperiment assay to store as |
data |
name of the SingleCellExperiment assay to slot as |
project |
Project name for new Seurat object |
A Seurat
object generated from x
Convert objects to SingleCellExperiment objects
as.SingleCellExperiment(x, ...) ## S3 method for class 'Seurat' as.SingleCellExperiment(x, assay = NULL, ...)
as.SingleCellExperiment(x, ...) ## S3 method for class 'Seurat' as.SingleCellExperiment(x, assay = NULL, ...)
x |
An object to convert to class |
... |
Arguments passed to other methods |
assay |
Assays to convert |
Cast to Sparse
## S3 method for class 'H5Group' as.sparse(x, ...) ## S3 method for class 'Matrix' as.data.frame( x, row.names = NULL, optional = FALSE, ..., stringsAsFactors = getOption(x = "stringsAsFactors", default = FALSE) )
## S3 method for class 'H5Group' as.sparse(x, ...) ## S3 method for class 'Matrix' as.data.frame( x, row.names = NULL, optional = FALSE, ..., stringsAsFactors = getOption(x = "stringsAsFactors", default = FALSE) )
x |
An object |
... |
Arguments passed to other methods |
row.names |
|
optional |
logical. If |
stringsAsFactors |
logical: should the character vector be converted to a factor? |
as.data.frame.Matrix
: A data frame representation of the S4 Matrix
The Assay
object is the basic unit of Seurat; for more details, please
see the documentation in SeuratObject
Creates "vector-friendly" plots. Does this by saving a copy of the plot as a PNG file,
then adding the PNG image with annotation_raster
to a blank plot
of the same dimensions as plot
. Please note: original legends and axes will be lost
during augmentation.
AugmentPlot(plot, width = 10, height = 10, dpi = 100)
AugmentPlot(plot, width = 10, height = 10, dpi = 100)
plot |
A ggplot object |
width , height
|
Width and height of PNG version of plot |
dpi |
Plot resolution |
A ggplot object
## Not run: data("pbmc_small") plot <- DimPlot(object = pbmc_small) AugmentPlot(plot = plot) ## End(Not run)
## Not run: data("pbmc_small") plot <- DimPlot(object = pbmc_small) AugmentPlot(plot = plot) ## End(Not run)
It happens to look good
AutoPointSize(data, raster = NULL)
AutoPointSize(data, raster = NULL)
data |
A data frame being passed to ggplot2 |
raster |
If TRUE, point size is set to 1 |
The "optimal" point size for visualizing these data
df <- data.frame(x = rnorm(n = 10000), y = runif(n = 10000)) AutoPointSize(data = df)
df <- data.frame(x = rnorm(n = 10000), y = runif(n = 10000)) AutoPointSize(data = df)
Returns averaged expression values for each identity class
AverageExpression( object, assays = NULL, features = NULL, return.seurat = FALSE, group.by = "ident", add.ident = NULL, slot = "data", verbose = TRUE, ... )
AverageExpression( object, assays = NULL, features = NULL, return.seurat = FALSE, group.by = "ident", add.ident = NULL, slot = "data", verbose = TRUE, ... )
object |
Seurat object |
assays |
Which assays to use. Default is all assays |
features |
Features to analyze. Default is all features in the assay |
return.seurat |
Whether to return the data as a Seurat object. Default is FALSE |
group.by |
Categories for grouping (e.g, ident, replicate, celltype); 'ident' by default |
add.ident |
(Deprecated) Place an additional label on each cell prior to pseudobulking (very useful if you want to observe cluster pseudobulk values, separated by replicate, for example) |
slot |
Slot(s) to use; if multiple slots are given, assumed to follow the order of 'assays' (if specified) or object's assays |
verbose |
Print messages and show progress bar |
... |
Arguments to be passed to methods such as |
If slot is set to 'data', this function assumes that the data has been log
normalized and therefore feature values are exponentiated prior to averaging
so that averaging is done in non-log space. Otherwise, if slot is set to
either 'counts' or 'scale.data', no exponentiation is performed prior to
averaging
If return.seurat = TRUE
and slot is not 'scale.data', averaged values
are placed in the 'counts' slot of the returned object and the log of averaged values
are placed in the 'data' slot. ScaleData
is then run on the default assay
before returning the object.
If return.seurat = TRUE
and slot is 'scale.data', the 'counts' slot is left empty,
the 'data' slot is filled with NA, and 'scale.data' is set to the aggregated values.
Returns a matrix with genes as rows, identity classes as columns.
If return.seurat is TRUE, returns an object of class Seurat
.
data("pbmc_small") head(AverageExpression(object = pbmc_small))
data("pbmc_small") head(AverageExpression(object = pbmc_small))
This function plots the calculated inflection points derived from the barcode-rank distribution.
BarcodeInflectionsPlot(object)
BarcodeInflectionsPlot(object)
object |
Seurat object |
See [CalculateBarcodeInflections()] to calculate inflection points and [SubsetByBarcodeInflections()] to subsequently subset the Seurat object.
Returns a 'ggplot2' object showing the by-group inflection points and provided (or default) rank threshold values in grey.
Robert A. Amezquita, [email protected]
CalculateBarcodeInflections
SubsetByBarcodeInflections
data("pbmc_small") pbmc_small <- CalculateBarcodeInflections(pbmc_small, group.column = 'groups') BarcodeInflectionsPlot(pbmc_small)
data("pbmc_small") pbmc_small <- CalculateBarcodeInflections(pbmc_small, group.column = 'groups') BarcodeInflectionsPlot(pbmc_small)
Determine text color based on background color
BGTextColor( background, threshold = 186, w3c = FALSE, dark = "black", light = "white" )
BGTextColor( background, threshold = 186, w3c = FALSE, dark = "black", light = "white" )
background |
A vector of background colors; supports R color names and hexadecimal codes |
threshold |
Intensity threshold for light/dark cutoff; intensities
greater than |
w3c |
Use W3C formula for calculating
background text color; ignores |
dark |
Color for dark text |
light |
Color for light text |
A named vector of either dark
or light
, depending on
background
; names of vector are background
BGTextColor(background = c('black', 'white', '#E76BF3'))
BGTextColor(background = c('black', 'white', '#E76BF3'))
Creates a custom color palette based on low, middle, and high color values
BlackAndWhite(mid = NULL, k = 50) BlueAndRed(k = 50) CustomPalette(low = "white", high = "red", mid = NULL, k = 50) PurpleAndYellow(k = 50)
BlackAndWhite(mid = NULL, k = 50) BlueAndRed(k = 50) CustomPalette(low = "white", high = "red", mid = NULL, k = 50) PurpleAndYellow(k = 50)
mid |
middle color. Optional. |
k |
number of steps (colors levels) to include between low and high values |
low |
low color |
high |
high color |
A color palette for plotting
df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2)) plot(df, col = BlackAndWhite()) df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2)) plot(df, col = BlueAndRed()) myPalette <- CustomPalette() myPalette df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2)) plot(df, col = PurpleAndYellow())
df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2)) plot(df, col = BlackAndWhite()) df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2)) plot(df, col = BlueAndRed()) myPalette <- CustomPalette() myPalette df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2)) plot(df, col = PurpleAndYellow())
Constructs a phylogenetic tree relating the 'average' cell from each identity class. Tree is estimated based on a distance matrix constructed in either gene expression space or PCA space.
BuildClusterTree( object, assay = NULL, features = NULL, dims = NULL, reduction = "pca", graph = NULL, slot = "data", reorder = FALSE, reorder.numeric = FALSE, verbose = TRUE )
BuildClusterTree( object, assay = NULL, features = NULL, dims = NULL, reduction = "pca", graph = NULL, slot = "data", reorder = FALSE, reorder.numeric = FALSE, verbose = TRUE )
object |
Seurat object |
assay |
Assay to use for the analysis. |
features |
Genes to use for the analysis. Default is the set of
variable genes ( |
dims |
If set, tree is calculated in dimension reduction space;
overrides |
reduction |
Name of dimension reduction to use. Only used if |
graph |
If graph is passed, build tree based on graph connectivity between
clusters; overrides |
slot |
Slot(s) to use; if multiple slots are given, assumed to follow the order of 'assays' (if specified) or object's assays |
reorder |
Re-order identity classes (factor ordering), according to position on the tree. This groups similar classes together which can be helpful, for example, when drawing violin plots. |
reorder.numeric |
Re-order identity classes according to position on the tree, assigning a numeric value ('1' is the leftmost node) |
verbose |
Show progress updates |
Note that the tree is calculated for an 'average' cell, so gene expression or PC scores are averaged across all cells in an identity class before the tree is constructed.
A Seurat object where the cluster tree can be accessed with Tool
if (requireNamespace("ape", quietly = TRUE)) { data("pbmc_small") pbmc_small pbmc_small <- BuildClusterTree(object = pbmc_small) Tool(object = pbmc_small, slot = 'BuildClusterTree') }
if (requireNamespace("ape", quietly = TRUE)) { data("pbmc_small") pbmc_small pbmc_small <- BuildClusterTree(object = pbmc_small) Tool(object = pbmc_small, slot = 'BuildClusterTree') }
Function to calculate perturbation signature for pooled CRISPR screen datasets. For each target cell (expressing one target gRNA), we identified 20 cells from the control pool (non-targeting cells) with the most similar mRNA expression profiles. The perturbation signature is calculated by subtracting the averaged mRNA expression profile of the non-targeting neighbors from the mRNA expression profile of the target cell.
CalcPerturbSig( object, assay = NULL, features = NULL, slot = "data", gd.class = "guide_ID", nt.cell.class = "NT", split.by = NULL, num.neighbors = NULL, reduction = "pca", ndims = 15, new.assay.name = "PRTB", verbose = TRUE )
CalcPerturbSig( object, assay = NULL, features = NULL, slot = "data", gd.class = "guide_ID", nt.cell.class = "NT", split.by = NULL, num.neighbors = NULL, reduction = "pca", ndims = 15, new.assay.name = "PRTB", verbose = TRUE )
object |
An object of class Seurat. |
assay |
Name of Assay PRTB signature is being calculated on. |
features |
Features to compute PRTB signature for. Defaults to the variable features set in the assay specified. |
slot |
Data slot to use for PRTB signature calculation. |
gd.class |
Metadata column containing target gene classification. |
nt.cell.class |
Non-targeting gRNA cell classification identity. |
split.by |
Provide metadata column if multiple biological replicates exist to calculate PRTB signature for every replicate separately. |
num.neighbors |
Number of nearest neighbors to consider. |
reduction |
Reduction method used to calculate nearest neighbors. |
ndims |
Number of dimensions to use from dimensionality reduction method. |
new.assay.name |
Name for the new assay. |
verbose |
Display progress + messages |
Returns a Seurat object with a new assay added containing the perturbation signature for all cells in the data slot.
This function calculates an adaptive inflection point ("knee") of the barcode distribution for each sample group. This is useful for determining a threshold for removing low-quality samples.
CalculateBarcodeInflections( object, barcode.column = "nCount_RNA", group.column = "orig.ident", threshold.low = NULL, threshold.high = NULL )
CalculateBarcodeInflections( object, barcode.column = "nCount_RNA", group.column = "orig.ident", threshold.low = NULL, threshold.high = NULL )
object |
Seurat object |
barcode.column |
Column to use as proxy for barcodes ("nCount_RNA" by default) |
group.column |
Column to group by ("orig.ident" by default) |
threshold.low |
Ignore barcodes of rank below this threshold in inflection calculation |
threshold.high |
Ignore barcodes of rank above thisf threshold in inflection calculation |
The function operates by calculating the slope of the barcode number vs. rank distribution, and then finding the point at which the distribution changes most steeply (the "knee"). Of note, this calculation often must be restricted as to the range at which it performs, so 'threshold' parameters are provided to restrict the range of the calculation based on the rank of the barcodes. [BarcodeInflectionsPlot()] is provided as a convenience function to visualize and test different thresholds and thus provide more sensical end results.
See [BarcodeInflectionsPlot()] to visualize the calculated inflection points and [SubsetByBarcodeInflections()] to subsequently subset the Seurat object.
Returns Seurat object with a new list in the 'tools' slot, 'CalculateBarcodeInflections' with values:
* 'barcode_distribution' - contains the full barcode distribution across the entire dataset * 'inflection_points' - the calculated inflection points within the thresholds * 'threshold_values' - the provided (or default) threshold values to search within for inflections * 'cells_pass' - the cells that pass the inflection point calculation
Robert A. Amezquita, [email protected]
BarcodeInflectionsPlot
SubsetByBarcodeInflections
data("pbmc_small") CalculateBarcodeInflections(pbmc_small, group.column = 'groups')
data("pbmc_small") CalculateBarcodeInflections(pbmc_small, group.column = 'groups')
Match the case of character vectors
CaseMatch(search, match)
CaseMatch(search, match)
search |
A vector of search terms |
match |
A vector of characters whose case should be matched |
Values from search present in match with the case of match
data("pbmc_small") cd_genes <- c('Cd79b', 'Cd19', 'Cd200') CaseMatch(search = cd_genes, match = rownames(x = pbmc_small))
data("pbmc_small") cd_genes <- c('Cd79b', 'Cd19', 'Cd200') CaseMatch(search = cd_genes, match = rownames(x = pbmc_small))
A list of genes used in cell-cycle regression
cc.genes
cc.genes
A list of two vectors
Genes associated with S-phase
Genes associated with G2M-phase
https://www.science.org/doi/abs/10.1126/science.aad0501
A list of genes used in cell-cycle regression, updated with 2019 symbols
cc.genes.updated.2019
cc.genes.updated.2019
A list of two vectors
Genes associated with S-phase
Genes associated with G2M-phase
The following symbols were updated from cc.genes
MCM2: MCM7
MLF1IP: CENPU
RPA2: POLR1B
BRIP1: MRPL36
FAM64A: PIMREG
HN1: JPT1
https://www.science.org/doi/abs/10.1126/science.aad0501
## Not run: cc.genes.updated.2019 <- cc.genes cc.genes.updated.2019$s.genes <- UpdateSymbolList(symbols = cc.genes.updated.2019$s.genes) cc.genes.updated.2019$g2m.genes <- UpdateSymbolList(symbols = cc.genes.updated.2019$g2m.genes) ## End(Not run)
## Not run: cc.genes.updated.2019 <- cc.genes cc.genes.updated.2019$s.genes <- UpdateSymbolList(symbols = cc.genes.updated.2019$s.genes) cc.genes.updated.2019$g2m.genes <- UpdateSymbolList(symbols = cc.genes.updated.2019$g2m.genes) ## End(Not run)
Score cell cycle phases
CellCycleScoring( object, s.features, g2m.features, ctrl = NULL, set.ident = FALSE, ... )
CellCycleScoring( object, s.features, g2m.features, ctrl = NULL, set.ident = FALSE, ... )
object |
A Seurat object |
s.features |
A vector of features associated with S phase |
g2m.features |
A vector of features associated with G2M phase |
ctrl |
Number of control features selected from the same bin per
analyzed feature supplied to |
set.ident |
If true, sets identity to phase assignments Stashes old identities in 'old.ident' |
... |
Arguments to be passed to |
A Seurat object with the following columns added to object meta data: S.Score, G2M.Score, and Phase
AddModuleScore
## Not run: data("pbmc_small") # pbmc_small doesn't have any cell-cycle genes # To run CellCycleScoring, please use a dataset with cell-cycle genes # An example is available at http://satijalab.org/seurat/cell_cycle_vignette.html pbmc_small <- CellCycleScoring( object = pbmc_small, g2m.features = cc.genes$g2m.genes, s.features = cc.genes$s.genes ) head(x = [email protected]) ## End(Not run)
## Not run: data("pbmc_small") # pbmc_small doesn't have any cell-cycle genes # To run CellCycleScoring, please use a dataset with cell-cycle genes # An example is available at http://satijalab.org/seurat/cell_cycle_vignette.html pbmc_small <- CellCycleScoring( object = pbmc_small, g2m.features = cc.genes$g2m.genes, s.features = cc.genes$s.genes ) head(x = pbmc_small@meta.data) ## End(Not run)
Get Cell Names
## S3 method for class 'SCTModel' Cells(x, ...) ## S3 method for class 'SlideSeq' Cells(x, ...) ## S3 method for class 'STARmap' Cells(x, ...) ## S3 method for class 'VisiumV1' Cells(x, ...)
## S3 method for class 'SCTModel' Cells(x, ...) ## S3 method for class 'SlideSeq' Cells(x, ...) ## S3 method for class 'STARmap' Cells(x, ...) ## S3 method for class 'VisiumV1' Cells(x, ...)
x |
An object |
... |
Arguments passed to other methods |
Get a vector of cell names associated with an image (or set of images)
CellsByImage(object, images = NULL, unlist = FALSE)
CellsByImage(object, images = NULL, unlist = FALSE)
object |
Seurat object |
images |
Vector of image names |
unlist |
Return as a single vector of cell names as opposed to a list, named by image name. |
A vector of cell names
## Not run: CellsByImage(object = object, images = "slice1") ## End(Not run)
## Not run: CellsByImage(object = object, images = "slice1") ## End(Not run)
Creates a plot of scatter plot of features across two single cells. Pearson correlation between the two cells is displayed above the plot.
CellScatter( object, cell1, cell2, features = NULL, highlight = NULL, cols = NULL, pt.size = 1, smooth = FALSE, raster = NULL, raster.dpi = c(512, 512) )
CellScatter( object, cell1, cell2, features = NULL, highlight = NULL, cols = NULL, pt.size = 1, smooth = FALSE, raster = NULL, raster.dpi = c(512, 512) )
object |
Seurat object |
cell1 |
Cell 1 name |
cell2 |
Cell 2 name |
features |
Features to plot (default, all features) |
highlight |
Features to highlight |
cols |
Colors to use for identity class plotting. |
pt.size |
Size of the points on the plot |
smooth |
Smooth the graph (similar to smoothScatter) |
raster |
Convert points to raster format, default is |
raster.dpi |
Pixel resolution for rasterized plots, passed to geom_scattermore(). Default is c(512, 512). |
A ggplot object
data("pbmc_small") CellScatter(object = pbmc_small, cell1 = 'ATAGGAGAAACAGA', cell2 = 'CATCAGGATGCACA')
data("pbmc_small") CellScatter(object = pbmc_small, cell1 = 'ATAGGAGAAACAGA', cell2 = 'CATCAGGATGCACA')
Select points on a scatterplot and get information about them
CellSelector(plot, object = NULL, ident = "SelectedCells", ...) FeatureLocator(plot, ...)
CellSelector(plot, object = NULL, ident = "SelectedCells", ...) FeatureLocator(plot, ...)
plot |
A ggplot2 plot |
object |
An optional Seurat object; if passes, will return an object
with the identities of selected cells set to |
ident |
An optional new identity class to assign the selected cells |
... |
Ignored |
If object
is NULL
, the names of the points selected;
otherwise, a Seurat object with the selected cells identity classes set to
ident
## Not run: data("pbmc_small") plot <- DimPlot(object = pbmc_small) # Follow instructions in the terminal to select points cells.located <- CellSelector(plot = plot) cells.located # Automatically set the identity class of selected cells and return a new Seurat object pbmc_small <- CellSelector(plot = plot, object = pbmc_small, ident = 'SelectedCells') ## End(Not run)
## Not run: data("pbmc_small") plot <- DimPlot(object = pbmc_small) # Follow instructions in the terminal to select points cells.located <- CellSelector(plot = plot) cells.located # Automatically set the identity class of selected cells and return a new Seurat object pbmc_small <- CellSelector(plot = plot, object = pbmc_small, ident = 'SelectedCells') ## End(Not run)
Move outliers towards center on dimension reduction plot
CollapseEmbeddingOutliers( object, reduction = "umap", dims = 1:2, group.by = "ident", outlier.sd = 2, reduction.key = "UMAP_" )
CollapseEmbeddingOutliers( object, reduction = "umap", dims = 1:2, group.by = "ident", outlier.sd = 2, reduction.key = "UMAP_" )
object |
Seurat object |
reduction |
Name of DimReduc to adjust |
dims |
Dimensions to visualize |
group.by |
Group (color) cells in different ways (for example, orig.ident) |
outlier.sd |
Controls the outlier distance |
reduction.key |
Key for DimReduc that is returned |
Returns a DimReduc object with the modified embeddings
## Not run: data("pbmc_small") pbmc_small <- FindClusters(pbmc_small, resolution = 1.1) pbmc_small <- RunUMAP(pbmc_small, dims = 1:5) DimPlot(pbmc_small, reduction = "umap") pbmc_small[["umap_new"]] <- CollapseEmbeddingOutliers(pbmc_small, reduction = "umap", reduction.key = 'umap_', outlier.sd = 0.5) DimPlot(pbmc_small, reduction = "umap_new") ## End(Not run)
## Not run: data("pbmc_small") pbmc_small <- FindClusters(pbmc_small, resolution = 1.1) pbmc_small <- RunUMAP(pbmc_small, dims = 1:5) DimPlot(pbmc_small, reduction = "umap") pbmc_small[["umap_new"]] <- CollapseEmbeddingOutliers(pbmc_small, reduction = "umap", reduction.key = 'umap_', outlier.sd = 0.5) DimPlot(pbmc_small, reduction = "umap_new") ## End(Not run)
Valuable for CITE-seq analyses, where we typically spike in rare populations of 'negative control' cells from a different species.
CollapseSpeciesExpressionMatrix( object, prefix = "HUMAN_", controls = "MOUSE_", ncontrols = 100 )
CollapseSpeciesExpressionMatrix( object, prefix = "HUMAN_", controls = "MOUSE_", ncontrols = 100 )
object |
A UMI count matrix. Should contain rownames that start with the ensuing arguments prefix.1 or prefix.2 |
prefix |
The prefix denoting rownames for the species of interest. Default is "HUMAN_". These rownames will have this prefix removed in the returned matrix. |
controls |
The prefix denoting rownames for the species of 'negative control' cells. Default is "MOUSE_". |
ncontrols |
How many of the most highly expressed (average) negative control features (by default, 100 mouse genes), should be kept? All other rownames starting with prefix.2 are discarded. |
A UMI count matrix. Rownames that started with prefix
have this
prefix discarded. For rownames starting with controls
, only the
ncontrols
most highly expressed features are kept, and the
prefix is kept. All other rows are retained.
## Not run: cbmc.rna.collapsed <- CollapseSpeciesExpressionMatrix(cbmc.rna) ## End(Not run)
## Not run: cbmc.rna.collapsed <- CollapseSpeciesExpressionMatrix(cbmc.rna) ## End(Not run)
Returns a DimPlot colored based on whether the cells fall in clusters to the left or to the right of a node split in the cluster tree.
ColorDimSplit( object, node, left.color = "red", right.color = "blue", other.color = "grey50", ... )
ColorDimSplit( object, node, left.color = "red", right.color = "blue", other.color = "grey50", ... )
object |
Seurat object |
node |
Node in cluster tree on which to base the split |
left.color |
Color for the left side of the split |
right.color |
Color for the right side of the split |
other.color |
Color for all other cells |
... |
Arguments passed on to
|
Returns a DimPlot
if (requireNamespace("ape", quietly = TRUE)) { data("pbmc_small") pbmc_small <- BuildClusterTree(object = pbmc_small, verbose = FALSE) PlotClusterTree(pbmc_small) ColorDimSplit(pbmc_small, node = 5) }
if (requireNamespace("ape", quietly = TRUE)) { data("pbmc_small") pbmc_small <- BuildClusterTree(object = pbmc_small, verbose = FALSE) PlotClusterTree(pbmc_small) ColorDimSplit(pbmc_small, node = 5) }
Combine ggplot2-based plots into a single plot
CombinePlots(plots, ncol = NULL, legend = NULL, ...)
CombinePlots(plots, ncol = NULL, legend = NULL, ...)
plots |
A list of gg objects |
ncol |
Number of columns |
legend |
Combine legends into a single legend
choose from 'right' or 'bottom'; pass 'none' to remove legends, or |
... |
Extra parameters passed to plot_grid |
A combined plot
data("pbmc_small") pbmc_small[['group']] <- sample( x = c('g1', 'g2'), size = ncol(x = pbmc_small), replace = TRUE ) plot1 <- FeaturePlot( object = pbmc_small, features = 'MS4A1', split.by = 'group' ) plot2 <- FeaturePlot( object = pbmc_small, features = 'FCN1', split.by = 'group' ) CombinePlots( plots = list(plot1, plot2), legend = 'none', nrow = length(x = unique(x = pbmc_small[['group', drop = TRUE]])) )
data("pbmc_small") pbmc_small[['group']] <- sample( x = c('g1', 'g2'), size = ncol(x = pbmc_small), replace = TRUE ) plot1 <- FeaturePlot( object = pbmc_small, features = 'MS4A1', split.by = 'group' ) plot2 <- FeaturePlot( object = pbmc_small, features = 'FCN1', split.by = 'group' ) CombinePlots( plots = list(plot1, plot2), legend = 'none', nrow = length(x = unique(x = pbmc_small[['group', drop = TRUE]])) )
Get the intensity and/or luminance of a color
Intensity(color) Luminance(color)
Intensity(color) Luminance(color)
color |
A vector of colors |
A vector of intensities/luminances for each color
Intensity(color = c('black', 'white', '#E76BF3')) Luminance(color = c('black', 'white', '#E76BF3'))
Intensity(color = c('black', 'white', '#E76BF3')) Luminance(color = c('black', 'white', '#E76BF3'))
Create a SCT object from a feature (e.g. gene) expression matrix and a list of SCTModels. The expected format of the input matrix is features x cells.
CreateSCTAssayObject( counts, data, scale.data = NULL, umi.assay = "RNA", min.cells = 0, min.features = 0, SCTModel.list = NULL )
CreateSCTAssayObject( counts, data, scale.data = NULL, umi.assay = "RNA", min.cells = 0, min.features = 0, SCTModel.list = NULL )
counts |
Unnormalized data such as raw counts or TPMs |
data |
Prenormalized data; if provided, do not pass |
scale.data |
a residual matrix |
umi.assay |
The UMI assay name. Default is RNA |
min.cells |
Include features detected in at least this many cells. Will subset the counts matrix as well. To reintroduce excluded features, create a new object with a lower cutoff. |
min.features |
Include cells where at least this many features are detected. |
SCTModel.list |
list of SCTModels |
Non-unique cell or feature names are not allowed. Please make unique before calling this function.
Run a custom distance function on an input data matrix
CustomDistance(my.mat, my.function, ...)
CustomDistance(my.mat, my.function, ...)
my.mat |
A matrix to calculate distance on |
my.function |
A function to calculate distance |
... |
Extra parameters to my.function |
A distance matrix
Jean Fan
data("pbmc_small") # Define custom distance matrix manhattan.distance <- function(x, y) return(sum(abs(x-y))) input.data <- GetAssayData(pbmc_small, assay.type = "RNA", slot = "scale.data") cell.manhattan.dist <- CustomDistance(input.data, manhattan.distance)
data("pbmc_small") # Define custom distance matrix manhattan.distance <- function(x, y) return(sum(abs(x-y))) input.data <- GetAssayData(pbmc_small, assay.type = "RNA", slot = "scale.data") cell.manhattan.dist <- CustomDistance(input.data, manhattan.distance)
DE and EnrichR pathway visualization barplot
DEenrichRPlot( object, ident.1 = NULL, ident.2 = NULL, balanced = TRUE, logfc.threshold = 0.25, assay = NULL, max.genes, test.use = "wilcox", p.val.cutoff = 0.05, cols = NULL, enrich.database = NULL, num.pathway = 10, return.gene.list = FALSE, ... )
DEenrichRPlot( object, ident.1 = NULL, ident.2 = NULL, balanced = TRUE, logfc.threshold = 0.25, assay = NULL, max.genes, test.use = "wilcox", p.val.cutoff = 0.05, cols = NULL, enrich.database = NULL, num.pathway = 10, return.gene.list = FALSE, ... )
object |
Name of object class Seurat. |
ident.1 |
Cell class identity 1. |
ident.2 |
Cell class identity 2. |
balanced |
Option to display pathway enrichments for both negative and positive DE genes.If false, only positive DE gene will be displayed. |
logfc.threshold |
Limit testing to genes which show, on average, at least X-fold difference (log-scale) between the two groups of cells. Default is 0.25 Increasing logfc.threshold speeds up the function, but can miss weaker signals. |
assay |
Assay to use in differential expression testing |
max.genes |
Maximum number of genes to use as input to enrichR. |
test.use |
Denotes which test to use. Available options are:
|
p.val.cutoff |
Cutoff to select DE genes. |
cols |
A list of colors to use for barplots. |
enrich.database |
Database to use from enrichR. |
num.pathway |
Number of pathways to display in barplot. |
return.gene.list |
Return list of DE genes |
... |
Arguments passed to other methods and to specific DE methods |
Returns one (only enriched) or two (both enriched and depleted) barplots with the top enriched/depleted GO terms from EnrichR.
Keep only certain aspects of the Seurat object. Can be useful in functions that utilize merge as it reduces the amount of data in the merge.
DietSeurat( object, counts = TRUE, data = TRUE, scale.data = FALSE, features = NULL, assays = NULL, dimreducs = NULL, graphs = NULL, misc = TRUE )
DietSeurat( object, counts = TRUE, data = TRUE, scale.data = FALSE, features = NULL, assays = NULL, dimreducs = NULL, graphs = NULL, misc = TRUE )
object |
Seurat object |
counts |
Preserve the count matrices for the assays specified |
data |
Preserve the data slot for the assays specified |
scale.data |
Preserve the scale.data slot for the assays specified |
features |
Only keep a subset of features, defaults to all features |
assays |
Only keep a subset of assays specified here |
dimreducs |
Only keep a subset of DimReducs specified here (if NULL, remove all DimReducs) |
graphs |
Only keep a subset of Graphs specified here (if NULL, remove all Graphs) |
misc |
Preserve the |
Draws a heatmap focusing on a principal component. Both cells and genes are sorted by their principal component scores. Allows for nice visualization of sources of heterogeneity in the dataset.
DimHeatmap( object, dims = 1, nfeatures = 30, cells = NULL, reduction = "pca", disp.min = -2.5, disp.max = NULL, balanced = TRUE, projected = FALSE, ncol = NULL, fast = TRUE, raster = TRUE, slot = "scale.data", assays = NULL, combine = TRUE ) PCHeatmap(object, ...)
DimHeatmap( object, dims = 1, nfeatures = 30, cells = NULL, reduction = "pca", disp.min = -2.5, disp.max = NULL, balanced = TRUE, projected = FALSE, ncol = NULL, fast = TRUE, raster = TRUE, slot = "scale.data", assays = NULL, combine = TRUE ) PCHeatmap(object, ...)
object |
Seurat object |
dims |
Dimensions to plot |
nfeatures |
Number of genes to plot |
cells |
A list of cells to plot. If numeric, just plots the top cells. |
reduction |
Which dimensional reduction to use |
disp.min |
Minimum display value (all values below are clipped) |
disp.max |
Maximum display value (all values above are clipped); defaults to 2.5
if |
balanced |
Plot an equal number of genes with both + and - scores. |
projected |
Use the full projected dimensional reduction |
ncol |
Number of columns to plot |
fast |
If true, use |
raster |
If true, plot with geom_raster, else use geom_tile. geom_raster may look blurry on some viewing applications such as Preview due to how the raster is interpolated. Set this to FALSE if you are encountering that issue (note that plots may take longer to produce/render). |
slot |
Data slot to use, choose from 'raw.data', 'data', or 'scale.data' |
assays |
A vector of assays to pull data from |
combine |
Combine plots into a single |
... |
Extra parameters passed to |
No return value by default. If using fast = FALSE, will return a
patchworked
ggplot object if combine = TRUE, otherwise
returns a list of ggplot objects
data("pbmc_small") DimHeatmap(object = pbmc_small)
data("pbmc_small") DimHeatmap(object = pbmc_small)
Graphs the output of a dimensional reduction technique on a 2D scatter plot where each point is a cell and it's positioned based on the cell embeddings determined by the reduction technique. By default, cells are colored by their identity class (can be changed with the group.by parameter).
DimPlot( object, dims = c(1, 2), cells = NULL, cols = NULL, pt.size = NULL, reduction = NULL, group.by = NULL, split.by = NULL, shape.by = NULL, order = NULL, shuffle = FALSE, seed = 1, label = FALSE, label.size = 4, label.color = "black", label.box = FALSE, repel = FALSE, cells.highlight = NULL, cols.highlight = "#DE2D26", sizes.highlight = 1, na.value = "grey50", ncol = NULL, combine = TRUE, raster = NULL, raster.dpi = c(512, 512) ) PCAPlot(object, ...) TSNEPlot(object, ...) UMAPPlot(object, ...)
DimPlot( object, dims = c(1, 2), cells = NULL, cols = NULL, pt.size = NULL, reduction = NULL, group.by = NULL, split.by = NULL, shape.by = NULL, order = NULL, shuffle = FALSE, seed = 1, label = FALSE, label.size = 4, label.color = "black", label.box = FALSE, repel = FALSE, cells.highlight = NULL, cols.highlight = "#DE2D26", sizes.highlight = 1, na.value = "grey50", ncol = NULL, combine = TRUE, raster = NULL, raster.dpi = c(512, 512) ) PCAPlot(object, ...) TSNEPlot(object, ...) UMAPPlot(object, ...)
object |
Seurat object |
dims |
Dimensions to plot, must be a two-length numeric vector specifying x- and y-dimensions |
cells |
Vector of cells to plot (default is all cells) |
cols |
Vector of colors, each color corresponds to an identity class. This may also be a single character
or numeric value corresponding to a palette as specified by |
pt.size |
Adjust point size for plotting |
reduction |
Which dimensionality reduction to use. If not specified, first searches for umap, then tsne, then pca |
group.by |
Name of one or more metadata columns to group (color) cells by (for example, orig.ident); pass 'ident' to group by identity class |
split.by |
Name of a metadata column to split plot by;
see |
shape.by |
If NULL, all points are circles (default). You can specify any
cell attribute (that can be pulled with FetchData) allowing for both
different colors and different shapes on cells. Only applicable if |
order |
Specify the order of plotting for the idents. This can be useful for crowded plots if points of interest are being buried. Provide either a full list of valid idents or a subset to be plotted last (on top) |
shuffle |
Whether to randomly shuffle the order of points. This can be useful for crowded plots if points of interest are being buried. (default is FALSE) |
seed |
Sets the seed if randomly shuffling the order of points. |
label |
Whether to label the clusters |
label.size |
Sets size of labels |
label.color |
Sets the color of the label text |
label.box |
Whether to put a box around the label text (geom_text vs geom_label) |
repel |
Repel labels |
cells.highlight |
A list of character or numeric vectors of cells to
highlight. If only one group of cells desired, can simply
pass a vector instead of a list. If set, colors selected cells to the color(s)
in |
cols.highlight |
A vector of colors to highlight the cells as; will repeat to the length groups in cells.highlight |
sizes.highlight |
Size of highlighted cells; will repeat to the length groups in cells.highlight |
na.value |
Color value for NA points when using custom scale |
ncol |
Number of columns for display when combining plots |
combine |
Combine plots into a single |
raster |
Convert points to raster format, default is |
raster.dpi |
Pixel resolution for rasterized plots, passed to geom_scattermore(). Default is c(512, 512). |
... |
Extra parameters passed to |
A patchworked
ggplot object if
combine = TRUE
; otherwise, a list of ggplot objects
For the old do.hover
and do.identify
functionality, please see
HoverLocator
and CellSelector
, respectively.
FeaturePlot
HoverLocator
CellSelector
FetchData
data("pbmc_small") DimPlot(object = pbmc_small) DimPlot(object = pbmc_small, split.by = 'ident')
data("pbmc_small") DimPlot(object = pbmc_small) DimPlot(object = pbmc_small, split.by = 'ident')
The DimReduc
object stores a dimensionality reduction taken out in
Seurat; for more details, please see the documentation in
SeuratObject
These are included here because pals depends on a number of compiled packages, and this can lead to increases in run time for Travis, and generally should be avoided when possible.
DiscretePalette(n, palette = NULL, shuffle = FALSE)
DiscretePalette(n, palette = NULL, shuffle = FALSE)
n |
Number of colours to be generated. |
palette |
Options are "alphabet", "alphabet2", "glasbey", "polychrome", "stepped", and "parade". Can be omitted and the function will use the one based on the requested n. |
shuffle |
Shuffle the colors in the selected palette. |
These palettes are a much better default for data with many classes than the default ggplot2 palette.
Many thanks to Kevin Wright for writing the pals package.
Taken from the pals package (Licence: GPL-3). https://cran.r-project.org/package=pals Credit: Kevin Wright
A vector of colors
Draws a heatmap of single cell feature expression.
DoHeatmap( object, features = NULL, cells = NULL, group.by = "ident", group.bar = TRUE, group.colors = NULL, disp.min = -2.5, disp.max = NULL, slot = "scale.data", assay = NULL, label = TRUE, size = 5.5, hjust = 0, angle = 45, raster = TRUE, draw.lines = TRUE, lines.width = NULL, group.bar.height = 0.02, combine = TRUE )
DoHeatmap( object, features = NULL, cells = NULL, group.by = "ident", group.bar = TRUE, group.colors = NULL, disp.min = -2.5, disp.max = NULL, slot = "scale.data", assay = NULL, label = TRUE, size = 5.5, hjust = 0, angle = 45, raster = TRUE, draw.lines = TRUE, lines.width = NULL, group.bar.height = 0.02, combine = TRUE )
object |
Seurat object |
features |
A vector of features to plot, defaults to |
cells |
A vector of cells to plot |
group.by |
A vector of variables to group cells by; pass 'ident' to group by cell identity classes |
group.bar |
Add a color bar showing group status for cells |
group.colors |
Colors to use for the color bar |
disp.min |
Minimum display value (all values below are clipped) |
disp.max |
Maximum display value (all values above are clipped); defaults to 2.5
if |
slot |
Data slot to use, choose from 'raw.data', 'data', or 'scale.data' |
assay |
Assay to pull from |
label |
Label the cell identies above the color bar |
size |
Size of text above color bar |
hjust |
Horizontal justification of text above color bar |
angle |
Angle of text above color bar |
raster |
If true, plot with geom_raster, else use geom_tile. geom_raster may look blurry on some viewing applications such as Preview due to how the raster is interpolated. Set this to FALSE if you are encountering that issue (note that plots may take longer to produce/render). |
draw.lines |
Include white lines to separate the groups |
lines.width |
Integer number to adjust the width of the separating white lines. Corresponds to the number of "cells" between each group. |
group.bar.height |
Scale the height of the color bar |
combine |
Combine plots into a single |
A patchworked
ggplot object if
combine = TRUE
; otherwise, a list of ggplot objects
data("pbmc_small") DoHeatmap(object = pbmc_small)
data("pbmc_small") DoHeatmap(object = pbmc_small)
Intuitive way of visualizing how feature expression changes across different identity classes (clusters). The size of the dot encodes the percentage of cells within a class, while the color encodes the AverageExpression level across all cells within a class (blue is high).
DotPlot( object, assay = NULL, features, cols = c("lightgrey", "blue"), col.min = -2.5, col.max = 2.5, dot.min = 0, dot.scale = 6, idents = NULL, group.by = NULL, split.by = NULL, cluster.idents = FALSE, scale = TRUE, scale.by = "radius", scale.min = NA, scale.max = NA )
DotPlot( object, assay = NULL, features, cols = c("lightgrey", "blue"), col.min = -2.5, col.max = 2.5, dot.min = 0, dot.scale = 6, idents = NULL, group.by = NULL, split.by = NULL, cluster.idents = FALSE, scale = TRUE, scale.by = "radius", scale.min = NA, scale.max = NA )
object |
Seurat object |
assay |
Name of assay to use, defaults to the active assay |
features |
Input vector of features, or named list of feature vectors if feature-grouped panels are desired (replicates the functionality of the old SplitDotPlotGG) |
cols |
Colors to plot: the name of a palette from
|
col.min |
Minimum scaled average expression threshold (everything smaller will be set to this) |
col.max |
Maximum scaled average expression threshold (everything larger will be set to this) |
dot.min |
The fraction of cells at which to draw the smallest dot (default is 0). All cell groups with less than this expressing the given gene will have no dot drawn. |
dot.scale |
Scale the size of the points, similar to cex |
idents |
Identity classes to include in plot (default is all) |
group.by |
Factor to group the cells by |
split.by |
Factor to split the groups by (replicates the functionality
of the old SplitDotPlotGG);
see |
cluster.idents |
Whether to order identities by hierarchical clusters based on given features, default is FALSE |
scale |
Determine whether the data is scaled, TRUE for default |
scale.by |
Scale the size of the points by 'size' or by 'radius' |
scale.min |
Set lower limit for scaling, use NA for default |
scale.max |
Set upper limit for scaling, use NA for default |
A ggplot object
RColorBrewer::brewer.pal.info
data("pbmc_small") cd_genes <- c("CD247", "CD3E", "CD9") DotPlot(object = pbmc_small, features = cd_genes) pbmc_small[['groups']] <- sample(x = c('g1', 'g2'), size = ncol(x = pbmc_small), replace = TRUE) DotPlot(object = pbmc_small, features = cd_genes, split.by = 'groups')
data("pbmc_small") cd_genes <- c("CD247", "CD3E", "CD9") DotPlot(object = pbmc_small, features = cd_genes) pbmc_small[['groups']] <- sample(x = c('g1', 'g2'), size = ncol(x = pbmc_small), replace = TRUE) DotPlot(object = pbmc_small, features = cd_genes, split.by = 'groups')
Plots the standard deviations (or approximate singular values if running PCAFast) of the principle components for easy identification of an elbow in the graph. This elbow often corresponds well with the significant dims and is much faster to run than Jackstraw
ElbowPlot(object, ndims = 20, reduction = "pca")
ElbowPlot(object, ndims = 20, reduction = "pca")
object |
Seurat object |
ndims |
Number of dimensions to plot standard deviation for |
reduction |
Reduction technique to plot standard deviation for |
A ggplot object
data("pbmc_small") ElbowPlot(object = pbmc_small)
data("pbmc_small") ElbowPlot(object = pbmc_small)
Calculate mean of logged values in non-log space (return answer in log-space)
ExpMean(x, ...)
ExpMean(x, ...)
x |
A vector of values |
... |
Other arguments (not used) |
Returns the mean in log-space
ExpMean(x = c(1, 2, 3))
ExpMean(x = c(1, 2, 3))
Calculate SD of logged values in non-log space (return answer in log-space)
ExpSD(x)
ExpSD(x)
x |
A vector of values |
Returns the standard deviation in log-space
ExpSD(x = c(1, 2, 3))
ExpSD(x = c(1, 2, 3))
Calculate variance of logged values in non-log space (return answer in log-space)
ExpVar(x)
ExpVar(x)
x |
A vector of values |
Returns the variance in log-space
ExpVar(x = c(1, 2, 3))
ExpVar(x = c(1, 2, 3))
Performs row scaling and/or centering. Equivalent to using t(scale(t(mat))) in R except in the case of NA values.
FastRowScale(mat, center = TRUE, scale = TRUE, scale_max = 10)
FastRowScale(mat, center = TRUE, scale = TRUE, scale_max = 10)
mat |
A matrix |
center |
a logical value indicating whether to center the rows |
scale |
a logical value indicating whether to scale the rows |
scale_max |
clip all values greater than scale_max to scale_max. Don't clip if Inf. |
Returns the center/scaled matrix
Colors single cells on a dimensional reduction plot according to a 'feature' (i.e. gene expression, PC scores, number of genes detected, etc.)
FeaturePlot( object, features, dims = c(1, 2), cells = NULL, cols = if (blend) { c("lightgrey", "#ff0000", "#00ff00") } else { c("lightgrey", "blue") }, pt.size = NULL, order = FALSE, min.cutoff = NA, max.cutoff = NA, reduction = NULL, split.by = NULL, keep.scale = "feature", shape.by = NULL, slot = "data", blend = FALSE, blend.threshold = 0.5, label = FALSE, label.size = 4, label.color = "black", repel = FALSE, ncol = NULL, coord.fixed = FALSE, by.col = TRUE, sort.cell = NULL, interactive = FALSE, combine = TRUE, raster = NULL, raster.dpi = c(512, 512) )
FeaturePlot( object, features, dims = c(1, 2), cells = NULL, cols = if (blend) { c("lightgrey", "#ff0000", "#00ff00") } else { c("lightgrey", "blue") }, pt.size = NULL, order = FALSE, min.cutoff = NA, max.cutoff = NA, reduction = NULL, split.by = NULL, keep.scale = "feature", shape.by = NULL, slot = "data", blend = FALSE, blend.threshold = 0.5, label = FALSE, label.size = 4, label.color = "black", repel = FALSE, ncol = NULL, coord.fixed = FALSE, by.col = TRUE, sort.cell = NULL, interactive = FALSE, combine = TRUE, raster = NULL, raster.dpi = c(512, 512) )
object |
Seurat object |
features |
Vector of features to plot. Features can come from:
|
dims |
Dimensions to plot, must be a two-length numeric vector specifying x- and y-dimensions |
cells |
Vector of cells to plot (default is all cells) |
cols |
The two colors to form the gradient over. Provide as string vector with
the first color corresponding to low values, the second to high. Also accepts a Brewer
color scale or vector of colors. Note: this will bin the data into number of colors provided.
When blend is
|
pt.size |
Adjust point size for plotting |
order |
Boolean determining whether to plot cells in order of expression. Can be useful if cells expressing given feature are getting buried. |
min.cutoff , max.cutoff
|
Vector of minimum and maximum cutoff values for each feature, may specify quantile in the form of 'q##' where '##' is the quantile (eg, 'q1', 'q10') |
reduction |
Which dimensionality reduction to use. If not specified, first searches for umap, then tsne, then pca |
split.by |
A factor in object metadata to split the feature plot by, pass 'ident'
to split by cell identity'; similar to the old |
keep.scale |
How to handle the color scale across multiple plots. Options are:
|
shape.by |
If NULL, all points are circles (default). You can specify any
cell attribute (that can be pulled with FetchData) allowing for both
different colors and different shapes on cells. Only applicable if |
slot |
Which slot to pull expression data from? |
blend |
Scale and blend expression values to visualize coexpression of two features |
blend.threshold |
The color cutoff from weak signal to strong signal; ranges from 0 to 1. |
label |
Whether to label the clusters |
label.size |
Sets size of labels |
label.color |
Sets the color of the label text |
repel |
Repel labels |
ncol |
Number of columns to combine multiple feature plots to, ignored if |
coord.fixed |
Plot cartesian coordinates with fixed aspect ratio |
by.col |
If splitting by a factor, plot the splits per column with the features as rows; ignored if |
sort.cell |
Redundant with |
interactive |
Launch an interactive |
combine |
Combine plots into a single |
raster |
Convert points to raster format, default is |
raster.dpi |
Pixel resolution for rasterized plots, passed to geom_scattermore(). Default is c(512, 512). |
A patchworked
ggplot object if
combine = TRUE
; otherwise, a list of ggplot objects
For the old do.hover
and do.identify
functionality, please see
HoverLocator
and CellSelector
, respectively.
DimPlot
HoverLocator
CellSelector
data("pbmc_small") FeaturePlot(object = pbmc_small, features = 'PC_1')
data("pbmc_small") FeaturePlot(object = pbmc_small, features = 'PC_1')
Creates a scatter plot of two features (typically feature expression), across a set of single cells. Cells are colored by their identity class. Pearson correlation between the two features is displayed above the plot.
FeatureScatter( object, feature1, feature2, cells = NULL, shuffle = FALSE, seed = 1, group.by = NULL, cols = NULL, pt.size = 1, shape.by = NULL, span = NULL, smooth = FALSE, combine = TRUE, slot = "data", plot.cor = TRUE, raster = NULL, raster.dpi = c(512, 512), jitter = FALSE )
FeatureScatter( object, feature1, feature2, cells = NULL, shuffle = FALSE, seed = 1, group.by = NULL, cols = NULL, pt.size = 1, shape.by = NULL, span = NULL, smooth = FALSE, combine = TRUE, slot = "data", plot.cor = TRUE, raster = NULL, raster.dpi = c(512, 512), jitter = FALSE )
object |
Seurat object |
feature1 |
First feature to plot. Typically feature expression but can also be metrics, PC scores, etc. - anything that can be retreived with FetchData |
feature2 |
Second feature to plot. |
cells |
Cells to include on the scatter plot. |
shuffle |
Whether to randomly shuffle the order of points. This can be useful for crowded plots if points of interest are being buried. (default is FALSE) |
seed |
Sets the seed if randomly shuffling the order of points. |
group.by |
Name of one or more metadata columns to group (color) cells by (for example, orig.ident); pass 'ident' to group by identity class |
cols |
Colors to use for identity class plotting. |
pt.size |
Size of the points on the plot |
shape.by |
Ignored for now |
span |
Spline span in loess function call, if |
smooth |
Smooth the graph (similar to smoothScatter) |
combine |
Combine plots into a single |
slot |
Slot to pull data from, should be one of 'counts', 'data', or 'scale.data' |
plot.cor |
Display correlation in plot title |
raster |
Convert points to raster format, default is |
raster.dpi |
Pixel resolution for rasterized plots, passed to geom_scattermore(). Default is c(512, 512). |
jitter |
Jitter for easier visualization of crowded points (default is FALSE) |
A ggplot object
data("pbmc_small") FeatureScatter(object = pbmc_small, feature1 = 'CD9', feature2 = 'CD3E')
data("pbmc_small") FeatureScatter(object = pbmc_small, feature1 = 'CD9', feature2 = 'CD3E')
This function is useful for removing stray beads that fall outside the main
Slide-seq puck area. Essentially, it's a circular filter where you set a
center and radius defining a circle of beads to keep. If the center is not
set, it will be estimated from the bead coordinates (removing the 1st and
99th quantile to avoid skewing the center by the stray beads). By default,
this function will display a SpatialDimPlot
showing which cells
were removed for easy adjustment of the center and/or radius.
FilterSlideSeq( object, image = "image", center = NULL, radius = NULL, do.plot = TRUE )
FilterSlideSeq( object, image = "image", center = NULL, radius = NULL, do.plot = TRUE )
object |
Seurat object with slide-seq data |
image |
Name of the image where the coordinates are stored |
center |
Vector specifying the x and y coordinates for the center of the inclusion circle |
radius |
Radius of the circle of inclusion |
do.plot |
Display a |
Returns a Seurat object with only the subset of cells that pass the circular filter
## Not run: # This example uses the ssHippo dataset which you can download # using the SeuratData package. library(SeuratData) data('ssHippo') # perform filtering of beads ssHippo.filtered <- FilterSlideSeq(ssHippo, radius = 2300) # This radius looks to small so increase and repeat until satisfied ## End(Not run)
## Not run: # This example uses the ssHippo dataset which you can download # using the SeuratData package. library(SeuratData) data('ssHippo') # perform filtering of beads ssHippo.filtered <- FilterSlideSeq(ssHippo, radius = 2300) # This radius looks to small so increase and repeat until satisfied ## End(Not run)
Finds markers (differentially expressed genes) for each of the identity classes in a dataset
FindAllMarkers( object, assay = NULL, features = NULL, logfc.threshold = 0.25, test.use = "wilcox", slot = "data", min.pct = 0.1, min.diff.pct = -Inf, node = NULL, verbose = TRUE, only.pos = FALSE, max.cells.per.ident = Inf, random.seed = 1, latent.vars = NULL, min.cells.feature = 3, min.cells.group = 3, mean.fxn = NULL, fc.name = NULL, base = 2, return.thresh = 0.01, densify = FALSE, ... )
FindAllMarkers( object, assay = NULL, features = NULL, logfc.threshold = 0.25, test.use = "wilcox", slot = "data", min.pct = 0.1, min.diff.pct = -Inf, node = NULL, verbose = TRUE, only.pos = FALSE, max.cells.per.ident = Inf, random.seed = 1, latent.vars = NULL, min.cells.feature = 3, min.cells.group = 3, mean.fxn = NULL, fc.name = NULL, base = 2, return.thresh = 0.01, densify = FALSE, ... )
object |
An object |
assay |
Assay to use in differential expression testing |
features |
Genes to test. Default is to use all genes |
logfc.threshold |
Limit testing to genes which show, on average, at least X-fold difference (log-scale) between the two groups of cells. Default is 0.25 Increasing logfc.threshold speeds up the function, but can miss weaker signals. |
test.use |
Denotes which test to use. Available options are:
|
slot |
Slot to pull data from; note that if |
min.pct |
only test genes that are detected in a minimum fraction of min.pct cells in either of the two populations. Meant to speed up the function by not testing genes that are very infrequently expressed. Default is 0.1 |
min.diff.pct |
only test genes that show a minimum difference in the fraction of detection between the two groups. Set to -Inf by default |
node |
A node to find markers for and all its children; requires
|
verbose |
Print a progress bar once expression testing begins |
only.pos |
Only return positive markers (FALSE by default) |
max.cells.per.ident |
Down sample each identity class to a max number. Default is no downsampling. Not activated by default (set to Inf) |
random.seed |
Random seed for downsampling |
latent.vars |
Variables to test, used only when |
min.cells.feature |
Minimum number of cells expressing the feature in at least one of the two groups, currently only used for poisson and negative binomial tests |
min.cells.group |
Minimum number of cells in one of the groups |
mean.fxn |
Function to use for fold change or average difference calculation. If NULL, the appropriate function will be chose according to the slot used |
fc.name |
Name of the fold change, average difference, or custom function column in the output data.frame. If NULL, the fold change column will be named according to the logarithm base (eg, "avg_log2FC"), or if using the scale.data slot "avg_diff". |
base |
The base with respect to which logarithms are computed. |
return.thresh |
Only return markers that have a p-value < return.thresh, or a power > return.thresh (if the test is ROC) |
densify |
Convert the sparse matrix to a dense form before running the DE test. This can provide speedups but might require higher memory; default is FALSE |
... |
Arguments passed to other methods and to specific DE methods |
Matrix containing a ranked list of putative markers, and associated statistics (p-values, ROC score, etc.)
data("pbmc_small") # Find markers for all clusters all.markers <- FindAllMarkers(object = pbmc_small) head(x = all.markers) ## Not run: # Pass a value to node as a replacement for FindAllMarkersNode pbmc_small <- BuildClusterTree(object = pbmc_small) all.markers <- FindAllMarkers(object = pbmc_small, node = 4) head(x = all.markers) ## End(Not run)
data("pbmc_small") # Find markers for all clusters all.markers <- FindAllMarkers(object = pbmc_small) head(x = all.markers) ## Not run: # Pass a value to node as a replacement for FindAllMarkersNode pbmc_small <- BuildClusterTree(object = pbmc_small) all.markers <- FindAllMarkers(object = pbmc_small, node = 4) head(x = all.markers) ## End(Not run)
Identify clusters of cells by a shared nearest neighbor (SNN) modularity optimization based clustering algorithm. First calculate k-nearest neighbors and construct the SNN graph. Then optimize the modularity function to determine clusters. For a full description of the algorithms, see Waltman and van Eck (2013) The European Physical Journal B. Thanks to Nigel Delaney (evolvedmicrobe@github) for the rewrite of the Java modularity optimizer code in Rcpp!
FindClusters(object, ...) ## Default S3 method: FindClusters( object, modularity.fxn = 1, initial.membership = NULL, node.sizes = NULL, resolution = 0.8, method = "matrix", algorithm = 1, n.start = 10, n.iter = 10, random.seed = 0, group.singletons = TRUE, temp.file.location = NULL, edge.file.name = NULL, verbose = TRUE, ... ) ## S3 method for class 'Seurat' FindClusters( object, graph.name = NULL, modularity.fxn = 1, initial.membership = NULL, node.sizes = NULL, resolution = 0.8, method = "matrix", algorithm = 1, n.start = 10, n.iter = 10, random.seed = 0, group.singletons = TRUE, temp.file.location = NULL, edge.file.name = NULL, verbose = TRUE, ... )
FindClusters(object, ...) ## Default S3 method: FindClusters( object, modularity.fxn = 1, initial.membership = NULL, node.sizes = NULL, resolution = 0.8, method = "matrix", algorithm = 1, n.start = 10, n.iter = 10, random.seed = 0, group.singletons = TRUE, temp.file.location = NULL, edge.file.name = NULL, verbose = TRUE, ... ) ## S3 method for class 'Seurat' FindClusters( object, graph.name = NULL, modularity.fxn = 1, initial.membership = NULL, node.sizes = NULL, resolution = 0.8, method = "matrix", algorithm = 1, n.start = 10, n.iter = 10, random.seed = 0, group.singletons = TRUE, temp.file.location = NULL, edge.file.name = NULL, verbose = TRUE, ... )
object |
An object |
... |
Arguments passed to other methods |
modularity.fxn |
Modularity function (1 = standard; 2 = alternative). |
initial.membership , node.sizes
|
Parameters to pass to the Python leidenalg function. |
resolution |
Value of the resolution parameter, use a value above (below) 1.0 if you want to obtain a larger (smaller) number of communities. |
method |
Method for running leiden (defaults to matrix which is fast for small datasets). Enable method = "igraph" to avoid casting large data to a dense matrix. |
algorithm |
Algorithm for modularity optimization (1 = original Louvain algorithm; 2 = Louvain algorithm with multilevel refinement; 3 = SLM algorithm; 4 = Leiden algorithm). Leiden requires the leidenalg python. |
n.start |
Number of random starts. |
n.iter |
Maximal number of iterations per random start. |
random.seed |
Seed of the random number generator. |
group.singletons |
Group singletons into nearest cluster. If FALSE, assign all singletons to a "singleton" group |
temp.file.location |
Directory where intermediate files will be written. Specify the ABSOLUTE path. |
edge.file.name |
Edge file to use as input for modularity optimizer jar. |
verbose |
Print output |
graph.name |
Name of graph to use for the clustering algorithm |
To run Leiden algorithm, you must first install the leidenalg python package (e.g. via pip install leidenalg), see Traag et al (2018).
Returns a Seurat object where the idents have been updated with new cluster info; latest clustering results will be stored in object metadata under 'seurat_clusters'. Note that 'seurat_clusters' will be overwritten everytime FindClusters is run
Finds markers that are conserved between the groups
FindConservedMarkers( object, ident.1, ident.2 = NULL, grouping.var, assay = "RNA", slot = "data", min.cells.group = 3, meta.method = metap::minimump, verbose = TRUE, ... )
FindConservedMarkers( object, ident.1, ident.2 = NULL, grouping.var, assay = "RNA", slot = "data", min.cells.group = 3, meta.method = metap::minimump, verbose = TRUE, ... )
object |
An object |
ident.1 |
Identity class to define markers for |
ident.2 |
A second identity class for comparison. If NULL (default) - use all other cells for comparison. |
grouping.var |
grouping variable |
assay |
of assay to fetch data for (default is RNA) |
slot |
Slot to pull data from; note that if |
min.cells.group |
Minimum number of cells in one of the groups |
meta.method |
method for combining p-values. Should be a function from the metap package (NOTE: pass the function, not a string) |
verbose |
Print a progress bar once expression testing begins |
... |
parameters to pass to FindMarkers |
data.frame containing a ranked list of putative conserved markers, and associated statistics (p-values within each group and a combined p-value (such as Fishers combined p-value or others from the metap package), percentage of cells expressing the marker, average differences). Name of group is appended to each associated output column (e.g. CTRL_p_val). If only one group is tested in the grouping.var, max and combined p-values are not returned.
## Not run: data("pbmc_small") pbmc_small # Create a simulated grouping variable pbmc_small[['groups']] <- sample(x = c('g1', 'g2'), size = ncol(x = pbmc_small), replace = TRUE) FindConservedMarkers(pbmc_small, ident.1 = 0, ident.2 = 1, grouping.var = "groups") ## End(Not run)
## Not run: data("pbmc_small") pbmc_small # Create a simulated grouping variable pbmc_small[['groups']] <- sample(x = c('g1', 'g2'), size = ncol(x = pbmc_small), replace = TRUE) FindConservedMarkers(pbmc_small, ident.1 = 0, ident.2 = 1, grouping.var = "groups") ## End(Not run)
Find a set of anchors between a list of Seurat
objects.
These anchors can later be used to integrate the objects using the
IntegrateData
function.
FindIntegrationAnchors( object.list = NULL, assay = NULL, reference = NULL, anchor.features = 2000, scale = TRUE, normalization.method = c("LogNormalize", "SCT"), sct.clip.range = NULL, reduction = c("cca", "rpca", "rlsi"), l2.norm = TRUE, dims = 1:30, k.anchor = 5, k.filter = 200, k.score = 30, max.features = 200, nn.method = "annoy", n.trees = 50, eps = 0, verbose = TRUE )
FindIntegrationAnchors( object.list = NULL, assay = NULL, reference = NULL, anchor.features = 2000, scale = TRUE, normalization.method = c("LogNormalize", "SCT"), sct.clip.range = NULL, reduction = c("cca", "rpca", "rlsi"), l2.norm = TRUE, dims = 1:30, k.anchor = 5, k.filter = 200, k.score = 30, max.features = 200, nn.method = "annoy", n.trees = 50, eps = 0, verbose = TRUE )
object.list |
A list of |
assay |
A vector of assay names specifying which assay to use when constructing anchors. If NULL, the current default assay for each object is used. |
reference |
A vector specifying the object/s to be used as a reference
during integration. If NULL (default), all pairwise anchors are found (no
reference/s). If not NULL, the corresponding objects in |
anchor.features |
Can be either:
|
scale |
Whether or not to scale the features provided. Only set to FALSE if you have previously scaled the features you want to use for each object in the object.list |
normalization.method |
Name of normalization method used: LogNormalize or SCT |
sct.clip.range |
Numeric of length two specifying the min and max values the Pearson residual will be clipped to |
reduction |
Dimensional reduction to perform when finding anchors. Can be one of:
|
l2.norm |
Perform L2 normalization on the CCA cell embeddings after dimensional reduction |
dims |
Which dimensions to use from the CCA to specify the neighbor search space |
k.anchor |
How many neighbors (k) to use when picking anchors |
k.filter |
How many neighbors (k) to use when filtering anchors |
k.score |
How many neighbors (k) to use when scoring anchors |
max.features |
The maximum number of features to use when specifying the neighborhood search space in the anchor filtering |
nn.method |
Method for nearest neighbor finding. Options include: rann, annoy |
n.trees |
More trees gives higher precision when using annoy approximate nearest neighbor search |
eps |
Error bound on the neighbor finding algorithm (from RANN/Annoy) |
verbose |
Print progress bars and output |
The main steps of this procedure are outlined below. For a more detailed description of the methodology, please see Stuart, Butler, et al Cell 2019: doi:10.1016/j.cell.2019.05.031; doi:10.1101/460147
First, determine anchor.features if not explicitly specified using
SelectIntegrationFeatures
. Then for all pairwise combinations
of reference and query datasets:
Perform dimensional reduction on the dataset pair as specified via
the reduction
parameter. If l2.norm
is set to TRUE
,
perform L2 normalization of the embedding vectors.
Identify anchors - pairs of cells from each dataset that are contained within each other's neighborhoods (also known as mutual nearest neighbors).
Filter low confidence anchors to ensure anchors in the low dimension
space are in broad agreement with the high dimensional measurements. This
is done by looking at the neighbors of each query cell in the reference
dataset using max.features
to define this space. If the reference
cell isn't found within the first k.filter
neighbors, remove the
anchor.
Assign each remaining anchor a score. For each anchor cell, determine
the nearest k.score
anchors within its own dataset and within its
pair's dataset. Based on these neighborhoods, construct an overall neighbor
graph and then compute the shared neighbor overlap between anchor and query
cells (analogous to an SNN graph). We use the 0.01 and 0.90 quantiles on
these scores to dampen outlier effects and rescale to range between 0-1.
Returns an AnchorSet
object that can be used as input to
IntegrateData
.
Stuart T, Butler A, et al. Comprehensive Integration of Single-Cell Data. Cell. 2019;177:1888-1902 doi:10.1016/j.cell.2019.05.031
## Not run: # to install the SeuratData package see https://github.com/satijalab/seurat-data library(SeuratData) data("panc8") # panc8 is a merged Seurat object containing 8 separate pancreas datasets # split the object by dataset pancreas.list <- SplitObject(panc8, split.by = "tech") # perform standard preprocessing on each object for (i in 1:length(pancreas.list)) { pancreas.list[[i]] <- NormalizeData(pancreas.list[[i]], verbose = FALSE) pancreas.list[[i]] <- FindVariableFeatures( pancreas.list[[i]], selection.method = "vst", nfeatures = 2000, verbose = FALSE ) } # find anchors anchors <- FindIntegrationAnchors(object.list = pancreas.list) # integrate data integrated <- IntegrateData(anchorset = anchors) ## End(Not run)
## Not run: # to install the SeuratData package see https://github.com/satijalab/seurat-data library(SeuratData) data("panc8") # panc8 is a merged Seurat object containing 8 separate pancreas datasets # split the object by dataset pancreas.list <- SplitObject(panc8, split.by = "tech") # perform standard preprocessing on each object for (i in 1:length(pancreas.list)) { pancreas.list[[i]] <- NormalizeData(pancreas.list[[i]], verbose = FALSE) pancreas.list[[i]] <- FindVariableFeatures( pancreas.list[[i]], selection.method = "vst", nfeatures = 2000, verbose = FALSE ) } # find anchors anchors <- FindIntegrationAnchors(object.list = pancreas.list) # integrate data integrated <- IntegrateData(anchorset = anchors) ## End(Not run)
Finds markers (differentially expressed genes) for identity classes
FindMarkers(object, ...) ## Default S3 method: FindMarkers( object, slot = "data", counts = numeric(), cells.1 = NULL, cells.2 = NULL, features = NULL, logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1, min.diff.pct = -Inf, verbose = TRUE, only.pos = FALSE, max.cells.per.ident = Inf, random.seed = 1, latent.vars = NULL, min.cells.feature = 3, min.cells.group = 3, pseudocount.use = 1, fc.results = NULL, densify = FALSE, ... ) ## S3 method for class 'Assay' FindMarkers( object, slot = "data", cells.1 = NULL, cells.2 = NULL, features = NULL, logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1, min.diff.pct = -Inf, verbose = TRUE, only.pos = FALSE, max.cells.per.ident = Inf, random.seed = 1, latent.vars = NULL, min.cells.feature = 3, min.cells.group = 3, pseudocount.use = 1, mean.fxn = NULL, fc.name = NULL, base = 2, densify = FALSE, norm.method = NULL, ... ) ## S3 method for class 'SCTAssay' FindMarkers( object, slot = "data", cells.1 = NULL, cells.2 = NULL, features = NULL, logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1, min.diff.pct = -Inf, verbose = TRUE, only.pos = FALSE, max.cells.per.ident = Inf, random.seed = 1, latent.vars = NULL, min.cells.feature = 3, min.cells.group = 3, pseudocount.use = 1, mean.fxn = NULL, fc.name = NULL, base = 2, densify = FALSE, recorrect_umi = TRUE, ... ) ## S3 method for class 'DimReduc' FindMarkers( object, cells.1 = NULL, cells.2 = NULL, features = NULL, logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1, min.diff.pct = -Inf, verbose = TRUE, only.pos = FALSE, max.cells.per.ident = Inf, random.seed = 1, latent.vars = NULL, min.cells.feature = 3, min.cells.group = 3, pseudocount.use = 1, mean.fxn = rowMeans, fc.name = NULL, densify = FALSE, ... ) ## S3 method for class 'Seurat' FindMarkers( object, ident.1 = NULL, ident.2 = NULL, group.by = NULL, subset.ident = NULL, assay = NULL, slot = "data", reduction = NULL, features = NULL, logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1, min.diff.pct = -Inf, verbose = TRUE, only.pos = FALSE, max.cells.per.ident = Inf, random.seed = 1, latent.vars = NULL, min.cells.feature = 3, min.cells.group = 3, mean.fxn = NULL, fc.name = NULL, base = 2, densify = FALSE, ... )
FindMarkers(object, ...) ## Default S3 method: FindMarkers( object, slot = "data", counts = numeric(), cells.1 = NULL, cells.2 = NULL, features = NULL, logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1, min.diff.pct = -Inf, verbose = TRUE, only.pos = FALSE, max.cells.per.ident = Inf, random.seed = 1, latent.vars = NULL, min.cells.feature = 3, min.cells.group = 3, pseudocount.use = 1, fc.results = NULL, densify = FALSE, ... ) ## S3 method for class 'Assay' FindMarkers( object, slot = "data", cells.1 = NULL, cells.2 = NULL, features = NULL, logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1, min.diff.pct = -Inf, verbose = TRUE, only.pos = FALSE, max.cells.per.ident = Inf, random.seed = 1, latent.vars = NULL, min.cells.feature = 3, min.cells.group = 3, pseudocount.use = 1, mean.fxn = NULL, fc.name = NULL, base = 2, densify = FALSE, norm.method = NULL, ... ) ## S3 method for class 'SCTAssay' FindMarkers( object, slot = "data", cells.1 = NULL, cells.2 = NULL, features = NULL, logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1, min.diff.pct = -Inf, verbose = TRUE, only.pos = FALSE, max.cells.per.ident = Inf, random.seed = 1, latent.vars = NULL, min.cells.feature = 3, min.cells.group = 3, pseudocount.use = 1, mean.fxn = NULL, fc.name = NULL, base = 2, densify = FALSE, recorrect_umi = TRUE, ... ) ## S3 method for class 'DimReduc' FindMarkers( object, cells.1 = NULL, cells.2 = NULL, features = NULL, logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1, min.diff.pct = -Inf, verbose = TRUE, only.pos = FALSE, max.cells.per.ident = Inf, random.seed = 1, latent.vars = NULL, min.cells.feature = 3, min.cells.group = 3, pseudocount.use = 1, mean.fxn = rowMeans, fc.name = NULL, densify = FALSE, ... ) ## S3 method for class 'Seurat' FindMarkers( object, ident.1 = NULL, ident.2 = NULL, group.by = NULL, subset.ident = NULL, assay = NULL, slot = "data", reduction = NULL, features = NULL, logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1, min.diff.pct = -Inf, verbose = TRUE, only.pos = FALSE, max.cells.per.ident = Inf, random.seed = 1, latent.vars = NULL, min.cells.feature = 3, min.cells.group = 3, mean.fxn = NULL, fc.name = NULL, base = 2, densify = FALSE, ... )
object |
An object |
... |
Arguments passed to other methods and to specific DE methods |
slot |
Slot to pull data from; note that if |
counts |
Count matrix if using scale.data for DE tests. This is used for computing pct.1 and pct.2 and for filtering features based on fraction expressing |
cells.1 |
Vector of cell names belonging to group 1 |
cells.2 |
Vector of cell names belonging to group 2 |
features |
Genes to test. Default is to use all genes |
logfc.threshold |
Limit testing to genes which show, on average, at least X-fold difference (log-scale) between the two groups of cells. Default is 0.25 Increasing logfc.threshold speeds up the function, but can miss weaker signals. |
test.use |
Denotes which test to use. Available options are:
|
min.pct |
only test genes that are detected in a minimum fraction of min.pct cells in either of the two populations. Meant to speed up the function by not testing genes that are very infrequently expressed. Default is 0.1 |
min.diff.pct |
only test genes that show a minimum difference in the fraction of detection between the two groups. Set to -Inf by default |
verbose |
Print a progress bar once expression testing begins |
only.pos |
Only return positive markers (FALSE by default) |
max.cells.per.ident |
Down sample each identity class to a max number. Default is no downsampling. Not activated by default (set to Inf) |
random.seed |
Random seed for downsampling |
latent.vars |
Variables to test, used only when |
min.cells.feature |
Minimum number of cells expressing the feature in at least one of the two groups, currently only used for poisson and negative binomial tests |
min.cells.group |
Minimum number of cells in one of the groups |
pseudocount.use |
Pseudocount to add to averaged expression values when calculating logFC. 1 by default. |
fc.results |
data.frame from FoldChange |
densify |
Convert the sparse matrix to a dense form before running the DE test. This can provide speedups but might require higher memory; default is FALSE |
mean.fxn |
Function to use for fold change or average difference calculation. If NULL, the appropriate function will be chose according to the slot used |
fc.name |
Name of the fold change, average difference, or custom function column in the output data.frame. If NULL, the fold change column will be named according to the logarithm base (eg, "avg_log2FC"), or if using the scale.data slot "avg_diff". |
base |
The base with respect to which logarithms are computed. |
norm.method |
Normalization method for fold change calculation when
|
recorrect_umi |
Recalculate corrected UMI counts using minimum of the median UMIs when performing DE using multiple SCT objects; default is TRUE |
ident.1 |
Identity class to define markers for; pass an object of class
|
ident.2 |
A second identity class for comparison; if |
group.by |
Regroup cells into a different identity class prior to performing differential expression (see example) |
subset.ident |
Subset a particular identity class prior to regrouping. Only relevant if group.by is set (see example) |
assay |
Assay to use in differential expression testing |
reduction |
Reduction to use in differential expression testing - will test for DE on cell embeddings |
p-value adjustment is performed using bonferroni correction based on the total number of genes in the dataset. Other correction methods are not recommended, as Seurat pre-filters genes using the arguments above, reducing the number of tests performed. Lastly, as Aaron Lun has pointed out, p-values should be interpreted cautiously, as the genes used for clustering are the same genes tested for differential expression.
data.frame with a ranked list of putative markers as rows, and associated
statistics as columns (p-values, ROC score, etc., depending on the test used (test.use
)). The following columns are always present:
avg_logFC
: log fold-chage of the average expression between the two groups. Positive values indicate that the gene is more highly expressed in the first group
pct.1
: The percentage of cells where the gene is detected in the first group
pct.2
: The percentage of cells where the gene is detected in the second group
p_val_adj
: Adjusted p-value, based on bonferroni correction using all genes in the dataset
McDavid A, Finak G, Chattopadyay PK, et al. Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments. Bioinformatics. 2013;29(4):461-467. doi:10.1093/bioinformatics/bts714
Trapnell C, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature Biotechnology volume 32, pages 381-386 (2014)
Andrew McDavid, Greg Finak and Masanao Yajima (2017). MAST: Model-based Analysis of Single Cell Transcriptomics. R package version 1.2.1. https://github.com/RGLab/MAST/
Love MI, Huber W and Anders S (2014). "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2." Genome Biology. https://bioconductor.org/packages/release/bioc/html/DESeq2.html
FoldChange
data("pbmc_small") # Find markers for cluster 2 markers <- FindMarkers(object = pbmc_small, ident.1 = 2) head(x = markers) # Take all cells in cluster 2, and find markers that separate cells in the 'g1' group (metadata # variable 'group') markers <- FindMarkers(pbmc_small, ident.1 = "g1", group.by = 'groups', subset.ident = "2") head(x = markers) # Pass 'clustertree' or an object of class phylo to ident.1 and # a node to ident.2 as a replacement for FindMarkersNode if (requireNamespace("ape", quietly = TRUE)) { pbmc_small <- BuildClusterTree(object = pbmc_small) markers <- FindMarkers(object = pbmc_small, ident.1 = 'clustertree', ident.2 = 5) head(x = markers) }
data("pbmc_small") # Find markers for cluster 2 markers <- FindMarkers(object = pbmc_small, ident.1 = 2) head(x = markers) # Take all cells in cluster 2, and find markers that separate cells in the 'g1' group (metadata # variable 'group') markers <- FindMarkers(pbmc_small, ident.1 = "g1", group.by = 'groups', subset.ident = "2") head(x = markers) # Pass 'clustertree' or an object of class phylo to ident.1 and # a node to ident.2 as a replacement for FindMarkersNode if (requireNamespace("ape", quietly = TRUE)) { pbmc_small <- BuildClusterTree(object = pbmc_small) markers <- FindMarkers(object = pbmc_small, ident.1 = 'clustertree', ident.2 = 5) head(x = markers) }
This function will construct a weighted nearest neighbor (WNN) graph. For each cell, we identify the nearest neighbors based on a weighted combination of two modalities. Takes as input two dimensional reductions, one computed for each modality.Other parameters are listed for debugging, but can be left as default values.
FindMultiModalNeighbors( object, reduction.list, dims.list, k.nn = 20, l2.norm = TRUE, knn.graph.name = "wknn", snn.graph.name = "wsnn", weighted.nn.name = "weighted.nn", modality.weight.name = NULL, knn.range = 200, prune.SNN = 1/15, sd.scale = 1, cross.contant.list = NULL, smooth = FALSE, return.intermediate = FALSE, modality.weight = NULL, verbose = TRUE )
FindMultiModalNeighbors( object, reduction.list, dims.list, k.nn = 20, l2.norm = TRUE, knn.graph.name = "wknn", snn.graph.name = "wsnn", weighted.nn.name = "weighted.nn", modality.weight.name = NULL, knn.range = 200, prune.SNN = 1/15, sd.scale = 1, cross.contant.list = NULL, smooth = FALSE, return.intermediate = FALSE, modality.weight = NULL, verbose = TRUE )
object |
A Seurat object |
reduction.list |
A list of two dimensional reductions, one for each of the modalities to be integrated |
dims.list |
A list containing the dimensions for each reduction to use |
k.nn |
the number of multimodal neighbors to compute. 20 by default |
l2.norm |
Perform L2 normalization on the cell embeddings after dimensional reduction. TRUE by default. |
knn.graph.name |
Multimodal knn graph name |
snn.graph.name |
Multimodal snn graph name |
weighted.nn.name |
Multimodal neighbor object name |
modality.weight.name |
Variable name to store modality weight in object meta data |
knn.range |
The number of approximate neighbors to compute |
prune.SNN |
Cutoff not to discard edge in SNN graph |
sd.scale |
The scaling factor for kernel width. 1 by default |
cross.contant.list |
Constant used to avoid divide-by-zero errors. 1e-4 by default |
smooth |
Smoothing modality score across each individual modality neighbors. FALSE by default |
return.intermediate |
Store intermediate results in misc |
modality.weight |
A |
verbose |
Print progress bars and output |
Seurat object containing a nearest-neighbor object, KNN graph, and SNN graph - each based on a weighted combination of modalities.
Computes the k.param
nearest neighbors for a given dataset. Can also
optionally (via compute.SNN
), construct a shared nearest neighbor
graph by calculating the neighborhood overlap (Jaccard index) between every
cell and its k.param
nearest neighbors.
FindNeighbors(object, ...) ## Default S3 method: FindNeighbors( object, query = NULL, distance.matrix = FALSE, k.param = 20, return.neighbor = FALSE, compute.SNN = !return.neighbor, prune.SNN = 1/15, nn.method = "annoy", n.trees = 50, annoy.metric = "euclidean", nn.eps = 0, verbose = TRUE, force.recalc = FALSE, l2.norm = FALSE, cache.index = FALSE, index = NULL, ... ) ## S3 method for class 'Assay' FindNeighbors( object, features = NULL, k.param = 20, return.neighbor = FALSE, compute.SNN = !return.neighbor, prune.SNN = 1/15, nn.method = "annoy", n.trees = 50, annoy.metric = "euclidean", nn.eps = 0, verbose = TRUE, force.recalc = FALSE, l2.norm = FALSE, cache.index = FALSE, ... ) ## S3 method for class 'dist' FindNeighbors( object, k.param = 20, return.neighbor = FALSE, compute.SNN = !return.neighbor, prune.SNN = 1/15, nn.method = "annoy", n.trees = 50, annoy.metric = "euclidean", nn.eps = 0, verbose = TRUE, force.recalc = FALSE, l2.norm = FALSE, cache.index = FALSE, ... ) ## S3 method for class 'Seurat' FindNeighbors( object, reduction = "pca", dims = 1:10, assay = NULL, features = NULL, k.param = 20, return.neighbor = FALSE, compute.SNN = !return.neighbor, prune.SNN = 1/15, nn.method = "annoy", n.trees = 50, annoy.metric = "euclidean", nn.eps = 0, verbose = TRUE, force.recalc = FALSE, do.plot = FALSE, graph.name = NULL, l2.norm = FALSE, cache.index = FALSE, ... )
FindNeighbors(object, ...) ## Default S3 method: FindNeighbors( object, query = NULL, distance.matrix = FALSE, k.param = 20, return.neighbor = FALSE, compute.SNN = !return.neighbor, prune.SNN = 1/15, nn.method = "annoy", n.trees = 50, annoy.metric = "euclidean", nn.eps = 0, verbose = TRUE, force.recalc = FALSE, l2.norm = FALSE, cache.index = FALSE, index = NULL, ... ) ## S3 method for class 'Assay' FindNeighbors( object, features = NULL, k.param = 20, return.neighbor = FALSE, compute.SNN = !return.neighbor, prune.SNN = 1/15, nn.method = "annoy", n.trees = 50, annoy.metric = "euclidean", nn.eps = 0, verbose = TRUE, force.recalc = FALSE, l2.norm = FALSE, cache.index = FALSE, ... ) ## S3 method for class 'dist' FindNeighbors( object, k.param = 20, return.neighbor = FALSE, compute.SNN = !return.neighbor, prune.SNN = 1/15, nn.method = "annoy", n.trees = 50, annoy.metric = "euclidean", nn.eps = 0, verbose = TRUE, force.recalc = FALSE, l2.norm = FALSE, cache.index = FALSE, ... ) ## S3 method for class 'Seurat' FindNeighbors( object, reduction = "pca", dims = 1:10, assay = NULL, features = NULL, k.param = 20, return.neighbor = FALSE, compute.SNN = !return.neighbor, prune.SNN = 1/15, nn.method = "annoy", n.trees = 50, annoy.metric = "euclidean", nn.eps = 0, verbose = TRUE, force.recalc = FALSE, do.plot = FALSE, graph.name = NULL, l2.norm = FALSE, cache.index = FALSE, ... )
object |
An object |
... |
Arguments passed to other methods |
query |
Matrix of data to query against object. If missing, defaults to object. |
distance.matrix |
Boolean value of whether the provided matrix is a
distance matrix; note, for objects of class |
k.param |
Defines k for the k-nearest neighbor algorithm |
return.neighbor |
Return result as |
compute.SNN |
also compute the shared nearest neighbor graph |
prune.SNN |
Sets the cutoff for acceptable Jaccard index when computing the neighborhood overlap for the SNN construction. Any edges with values less than or equal to this will be set to 0 and removed from the SNN graph. Essentially sets the stringency of pruning (0 — no pruning, 1 — prune everything). |
nn.method |
Method for nearest neighbor finding. Options include: rann, annoy |
n.trees |
More trees gives higher precision when using annoy approximate nearest neighbor search |
annoy.metric |
Distance metric for annoy. Options include: euclidean, cosine, manhattan, and hamming |
nn.eps |
Error bound when performing nearest neighbor seach using RANN; default of 0.0 implies exact nearest neighbor search |
verbose |
Whether or not to print output to the console |
force.recalc |
Force recalculation of (S)NN. |
l2.norm |
Take L2Norm of the data |
cache.index |
Include cached index in returned Neighbor object (only relevant if return.neighbor = TRUE) |
index |
Precomputed index. Useful if querying new data against existing index to avoid recomputing. |
features |
Features to use as input for building the (S)NN; used only when
|
reduction |
Reduction to use as input for building the (S)NN |
dims |
Dimensions of reduction to use as input |
assay |
Assay to use in construction of (S)NN; used only when |
do.plot |
Plot SNN graph on tSNE coordinates |
graph.name |
Optional naming parameter for stored (S)NN graph
(or Neighbor object, if return.neighbor = TRUE). Default is assay.name_(s)nn.
To store both the neighbor graph and the shared nearest neighbor (SNN) graph,
you must supply a vector containing two names to the |
This function can either return a Neighbor
object
with the KNN information or a list of Graph
objects with
the KNN and SNN depending on the settings of return.neighbor
and
compute.SNN
. When running on a Seurat
object, this
returns the Seurat
object with the Graphs or Neighbor objects
stored in their respective slots. Names of the Graph or Neighbor object can
be found with Graphs
or Neighbors
.
data("pbmc_small") pbmc_small # Compute an SNN on the gene expression level pbmc_small <- FindNeighbors(pbmc_small, features = VariableFeatures(object = pbmc_small)) # More commonly, we build the SNN on a dimensionally reduced form of the data # such as the first 10 principle components. pbmc_small <- FindNeighbors(pbmc_small, reduction = "pca", dims = 1:10)
data("pbmc_small") pbmc_small # Compute an SNN on the gene expression level pbmc_small <- FindNeighbors(pbmc_small, features = VariableFeatures(object = pbmc_small)) # More commonly, we build the SNN on a dimensionally reduced form of the data # such as the first 10 principle components. pbmc_small <- FindNeighbors(pbmc_small, reduction = "pca", dims = 1:10)
Identify features whose variability in expression can be explained to some degree by spatial location.
FindSpatiallyVariableFeatures(object, ...) ## Default S3 method: FindSpatiallyVariableFeatures( object, spatial.location, selection.method = c("markvariogram", "moransi"), r.metric = 5, x.cuts = NULL, y.cuts = NULL, verbose = TRUE, ... ) ## S3 method for class 'Assay' FindSpatiallyVariableFeatures( object, slot = "scale.data", spatial.location, selection.method = c("markvariogram", "moransi"), features = NULL, r.metric = 5, x.cuts = NULL, y.cuts = NULL, nfeatures = nfeatures, verbose = TRUE, ... ) ## S3 method for class 'Seurat' FindSpatiallyVariableFeatures( object, assay = NULL, slot = "scale.data", features = NULL, image = NULL, selection.method = c("markvariogram", "moransi"), r.metric = 5, x.cuts = NULL, y.cuts = NULL, nfeatures = 2000, verbose = TRUE, ... )
FindSpatiallyVariableFeatures(object, ...) ## Default S3 method: FindSpatiallyVariableFeatures( object, spatial.location, selection.method = c("markvariogram", "moransi"), r.metric = 5, x.cuts = NULL, y.cuts = NULL, verbose = TRUE, ... ) ## S3 method for class 'Assay' FindSpatiallyVariableFeatures( object, slot = "scale.data", spatial.location, selection.method = c("markvariogram", "moransi"), features = NULL, r.metric = 5, x.cuts = NULL, y.cuts = NULL, nfeatures = nfeatures, verbose = TRUE, ... ) ## S3 method for class 'Seurat' FindSpatiallyVariableFeatures( object, assay = NULL, slot = "scale.data", features = NULL, image = NULL, selection.method = c("markvariogram", "moransi"), r.metric = 5, x.cuts = NULL, y.cuts = NULL, nfeatures = 2000, verbose = TRUE, ... )
object |
A Seurat object, assay, or expression matrix |
... |
Arguments passed to other methods |
spatial.location |
Coordinates for each cell/spot/bead |
selection.method |
Method for selecting spatially variable features.
|
r.metric |
r value at which to report the "trans" value of the mark variogram |
x.cuts |
Number of divisions to make in the x direction, helps define the grid over which binning is performed |
y.cuts |
Number of divisions to make in the y direction, helps define the grid over which binning is performed |
verbose |
Print messages and progress |
slot |
Slot in the Assay to pull data from |
features |
If provided, only compute on given features. Otherwise, compute for all features. |
nfeatures |
Number of features to mark as the top spatially variable. |
assay |
Assay to pull the features (marks) from |
image |
Name of image to pull the coordinates from |
Find subclusters under one cluster
FindSubCluster( object, cluster, graph.name, subcluster.name = "sub.cluster", resolution = 0.5, algorithm = 1 )
FindSubCluster( object, cluster, graph.name, subcluster.name = "sub.cluster", resolution = 0.5, algorithm = 1 )
object |
An object |
cluster |
the cluster to be sub-clustered |
graph.name |
Name of graph to use for the clustering algorithm |
subcluster.name |
the name of sub cluster added in the meta.data |
resolution |
Value of the resolution parameter, use a value above (below) 1.0 if you want to obtain a larger (smaller) number of communities. |
algorithm |
Algorithm for modularity optimization (1 = original Louvain algorithm; 2 = Louvain algorithm with multilevel refinement; 3 = SLM algorithm; 4 = Leiden algorithm). Leiden requires the leidenalg python. |
return a object with sub cluster labels in the sub-cluster.name variable
Find a set of anchors between a reference and query object. These
anchors can later be used to transfer data from the reference to
query object using the TransferData
object.
FindTransferAnchors( reference, query, normalization.method = "LogNormalize", recompute.residuals = TRUE, reference.assay = NULL, reference.neighbors = NULL, query.assay = NULL, reduction = "pcaproject", reference.reduction = NULL, project.query = FALSE, features = NULL, scale = TRUE, npcs = 30, l2.norm = TRUE, dims = 1:30, k.anchor = 5, k.filter = 200, k.score = 30, max.features = 200, nn.method = "annoy", n.trees = 50, eps = 0, approx.pca = TRUE, mapping.score.k = NULL, verbose = TRUE )
FindTransferAnchors( reference, query, normalization.method = "LogNormalize", recompute.residuals = TRUE, reference.assay = NULL, reference.neighbors = NULL, query.assay = NULL, reduction = "pcaproject", reference.reduction = NULL, project.query = FALSE, features = NULL, scale = TRUE, npcs = 30, l2.norm = TRUE, dims = 1:30, k.anchor = 5, k.filter = 200, k.score = 30, max.features = 200, nn.method = "annoy", n.trees = 50, eps = 0, approx.pca = TRUE, mapping.score.k = NULL, verbose = TRUE )
reference |
|
query |
|
normalization.method |
Name of normalization method used: LogNormalize or SCT. |
recompute.residuals |
If using SCT as a normalization method, compute query Pearson residuals using the reference SCT model parameters. |
reference.assay |
Name of the Assay to use from reference |
reference.neighbors |
Name of the Neighbor to use from the reference. Optionally enables reuse of precomputed neighbors. |
query.assay |
Name of the Assay to use from query |
reduction |
Dimensional reduction to perform when finding anchors. Options are:
|
reference.reduction |
Name of dimensional reduction to use from the reference if running the pcaproject workflow. Optionally enables reuse of precomputed reference dimensional reduction. If NULL (default), use a PCA computed on the reference object. |
project.query |
Project the PCA from the query dataset onto the reference. Use only in rare cases where the query dataset has a much larger cell number, but the reference dataset has a unique assay for transfer. In this case, the default features will be set to the variable features of the query object that are alos present in the reference. |
features |
Features to use for dimensional reduction. If not specified, set as variable features of the reference object which are also present in the query. |
scale |
Scale query data. |
npcs |
Number of PCs to compute on reference if reference.reduction is not provided. |
l2.norm |
Perform L2 normalization on the cell embeddings after dimensional reduction |
dims |
Which dimensions to use from the reduction to specify the neighbor search space |
k.anchor |
How many neighbors (k) to use when finding anchors |
k.filter |
How many neighbors (k) to use when filtering anchors. Set to NA to turn off filtering. |
k.score |
How many neighbors (k) to use when scoring anchors |
max.features |
The maximum number of features to use when specifying the neighborhood search space in the anchor filtering |
nn.method |
Method for nearest neighbor finding. Options include: rann, annoy |
n.trees |
More trees gives higher precision when using annoy approximate nearest neighbor search |
eps |
Error bound on the neighbor finding algorithm (from
|
approx.pca |
Use truncated singular value decomposition to approximate PCA |
mapping.score.k |
Compute and store nearest k query neighbors in the AnchorSet object that is returned. You can optionally set this if you plan on computing the mapping score and want to enable reuse of some downstream neighbor calculations to make the mapping score function more efficient. |
verbose |
Print progress bars and output |
The main steps of this procedure are outlined below. For a more detailed description of the methodology, please see Stuart, Butler, et al Cell 2019. doi:10.1016/j.cell.2019.05.031; doi:10.1101/460147
Perform dimensional reduction. Exactly what is done here depends on
the values set for the reduction
and project.query
parameters. If reduction = "pcaproject"
, a PCA is performed on
either the reference (if project.query = FALSE
) or the query (if
project.query = TRUE
), using the features
specified. The data
from the other dataset is then projected onto this learned PCA structure.
If reduction = "cca"
, then CCA is performed on the reference and
query for this dimensional reduction step. If
reduction = "lsiproject"
, the stored LSI dimension reduction in the
reference object is used to project the query dataset onto the reference.
If l2.norm
is set to TRUE
, perform L2 normalization of the
embedding vectors.
Identify anchors between the reference and query - pairs of cells from each dataset that are contained within each other's neighborhoods (also known as mutual nearest neighbors).
Filter low confidence anchors to ensure anchors in the low dimension
space are in broad agreement with the high dimensional measurements. This
is done by looking at the neighbors of each query cell in the reference
dataset using max.features
to define this space. If the reference
cell isn't found within the first k.filter
neighbors, remove the
anchor.
Assign each remaining anchor a score. For each anchor cell, determine
the nearest k.score
anchors within its own dataset and within its
pair's dataset. Based on these neighborhoods, construct an overall neighbor
graph and then compute the shared neighbor overlap between anchor and query
cells (analogous to an SNN graph). We use the 0.01 and 0.90 quantiles on
these scores to dampen outlier effects and rescale to range between 0-1.
Returns an AnchorSet
object that can be used as input to
TransferData
, IntegrateEmbeddings
and
MapQuery
. The dimension reduction used for finding anchors is
stored in the AnchorSet
object and can be used for computing anchor
weights in downstream functions. Note that only the requested dimensions are
stored in the dimension reduction object in the AnchorSet
. This means
that if dims=2:20
is used, for example, the dimension of the stored
reduction is 1:19
.
Stuart T, Butler A, et al. Comprehensive Integration of Single-Cell Data. Cell. 2019;177:1888-1902 doi:10.1016/j.cell.2019.05.031;
## Not run: # to install the SeuratData package see https://github.com/satijalab/seurat-data library(SeuratData) data("pbmc3k") # for demonstration, split the object into reference and query pbmc.reference <- pbmc3k[, 1:1350] pbmc.query <- pbmc3k[, 1351:2700] # perform standard preprocessing on each object pbmc.reference <- NormalizeData(pbmc.reference) pbmc.reference <- FindVariableFeatures(pbmc.reference) pbmc.reference <- ScaleData(pbmc.reference) pbmc.query <- NormalizeData(pbmc.query) pbmc.query <- FindVariableFeatures(pbmc.query) pbmc.query <- ScaleData(pbmc.query) # find anchors anchors <- FindTransferAnchors(reference = pbmc.reference, query = pbmc.query) # transfer labels predictions <- TransferData( anchorset = anchors, refdata = pbmc.reference$seurat_annotations ) pbmc.query <- AddMetaData(object = pbmc.query, metadata = predictions) ## End(Not run)
## Not run: # to install the SeuratData package see https://github.com/satijalab/seurat-data library(SeuratData) data("pbmc3k") # for demonstration, split the object into reference and query pbmc.reference <- pbmc3k[, 1:1350] pbmc.query <- pbmc3k[, 1351:2700] # perform standard preprocessing on each object pbmc.reference <- NormalizeData(pbmc.reference) pbmc.reference <- FindVariableFeatures(pbmc.reference) pbmc.reference <- ScaleData(pbmc.reference) pbmc.query <- NormalizeData(pbmc.query) pbmc.query <- FindVariableFeatures(pbmc.query) pbmc.query <- ScaleData(pbmc.query) # find anchors anchors <- FindTransferAnchors(reference = pbmc.reference, query = pbmc.query) # transfer labels predictions <- TransferData( anchorset = anchors, refdata = pbmc.reference$seurat_annotations ) pbmc.query <- AddMetaData(object = pbmc.query, metadata = predictions) ## End(Not run)
Identifies features that are outliers on a 'mean variability plot'.
FindVariableFeatures(object, ...) ## Default S3 method: FindVariableFeatures( object, selection.method = "vst", loess.span = 0.3, clip.max = "auto", mean.function = FastExpMean, dispersion.function = FastLogVMR, num.bin = 20, binning.method = "equal_width", verbose = TRUE, ... ) ## S3 method for class 'Assay' FindVariableFeatures( object, selection.method = "vst", loess.span = 0.3, clip.max = "auto", mean.function = FastExpMean, dispersion.function = FastLogVMR, num.bin = 20, binning.method = "equal_width", nfeatures = 2000, mean.cutoff = c(0.1, 8), dispersion.cutoff = c(1, Inf), verbose = TRUE, ... ) ## S3 method for class 'SCTAssay' FindVariableFeatures(object, nfeatures = 2000, ...) ## S3 method for class 'Seurat' FindVariableFeatures( object, assay = NULL, selection.method = "vst", loess.span = 0.3, clip.max = "auto", mean.function = FastExpMean, dispersion.function = FastLogVMR, num.bin = 20, binning.method = "equal_width", nfeatures = 2000, mean.cutoff = c(0.1, 8), dispersion.cutoff = c(1, Inf), verbose = TRUE, ... )
FindVariableFeatures(object, ...) ## Default S3 method: FindVariableFeatures( object, selection.method = "vst", loess.span = 0.3, clip.max = "auto", mean.function = FastExpMean, dispersion.function = FastLogVMR, num.bin = 20, binning.method = "equal_width", verbose = TRUE, ... ) ## S3 method for class 'Assay' FindVariableFeatures( object, selection.method = "vst", loess.span = 0.3, clip.max = "auto", mean.function = FastExpMean, dispersion.function = FastLogVMR, num.bin = 20, binning.method = "equal_width", nfeatures = 2000, mean.cutoff = c(0.1, 8), dispersion.cutoff = c(1, Inf), verbose = TRUE, ... ) ## S3 method for class 'SCTAssay' FindVariableFeatures(object, nfeatures = 2000, ...) ## S3 method for class 'Seurat' FindVariableFeatures( object, assay = NULL, selection.method = "vst", loess.span = 0.3, clip.max = "auto", mean.function = FastExpMean, dispersion.function = FastLogVMR, num.bin = 20, binning.method = "equal_width", nfeatures = 2000, mean.cutoff = c(0.1, 8), dispersion.cutoff = c(1, Inf), verbose = TRUE, ... )
object |
An object |
... |
Arguments passed to other methods |
selection.method |
How to choose top variable features. Choose one of :
|
loess.span |
(vst method) Loess span parameter used when fitting the variance-mean relationship |
clip.max |
(vst method) After standardization values larger than clip.max will be set to clip.max; default is 'auto' which sets this value to the square root of the number of cells |
mean.function |
Function to compute x-axis value (average expression). Default is to take the mean of the detected (i.e. non-zero) values |
dispersion.function |
Function to compute y-axis value (dispersion). Default is to take the standard deviation of all values |
num.bin |
Total number of bins to use in the scaled analysis (default is 20) |
binning.method |
Specifies how the bins should be computed. Available methods are:
|
verbose |
show progress bar for calculations |
nfeatures |
Number of features to select as top variable features;
only used when |
mean.cutoff |
A two-length numeric vector with low- and high-cutoffs for feature means |
dispersion.cutoff |
A two-length numeric vector with low- and high-cutoffs for feature dispersions |
assay |
Assay to use |
For the mean.var.plot method: Exact parameter settings may vary empirically from dataset to dataset, and based on visual inspection of the plot. Setting the y.cutoff parameter to 2 identifies features that are more than two standard deviations away from the average dispersion within a bin. The default X-axis function is the mean expression level, and for Y-axis it is the log(Variance/mean). All mean/variance calculations are not performed in log-space, but the results are reported in log-space - see relevant functions for exact details.
Calculate log fold change and percentage of cells expressing each feature for different identity classes.
FoldChange(object, ...) ## Default S3 method: FoldChange(object, cells.1, cells.2, mean.fxn, fc.name, features = NULL, ...) ## S3 method for class 'Assay' FoldChange( object, cells.1, cells.2, features = NULL, slot = "data", pseudocount.use = 1, fc.name = NULL, mean.fxn = NULL, base = 2, norm.method = NULL, ... ) ## S3 method for class 'SCTAssay' FoldChange( object, cells.1, cells.2, features = NULL, slot = "data", pseudocount.use = 1, fc.name = NULL, mean.fxn = NULL, base = 2, ... ) ## S3 method for class 'DimReduc' FoldChange( object, cells.1, cells.2, features = NULL, slot = NULL, pseudocount.use = 1, fc.name = NULL, mean.fxn = NULL, ... ) ## S3 method for class 'Seurat' FoldChange( object, ident.1 = NULL, ident.2 = NULL, group.by = NULL, subset.ident = NULL, assay = NULL, slot = "data", reduction = NULL, features = NULL, pseudocount.use = NULL, mean.fxn = NULL, base = 2, fc.name = NULL, ... )
FoldChange(object, ...) ## Default S3 method: FoldChange(object, cells.1, cells.2, mean.fxn, fc.name, features = NULL, ...) ## S3 method for class 'Assay' FoldChange( object, cells.1, cells.2, features = NULL, slot = "data", pseudocount.use = 1, fc.name = NULL, mean.fxn = NULL, base = 2, norm.method = NULL, ... ) ## S3 method for class 'SCTAssay' FoldChange( object, cells.1, cells.2, features = NULL, slot = "data", pseudocount.use = 1, fc.name = NULL, mean.fxn = NULL, base = 2, ... ) ## S3 method for class 'DimReduc' FoldChange( object, cells.1, cells.2, features = NULL, slot = NULL, pseudocount.use = 1, fc.name = NULL, mean.fxn = NULL, ... ) ## S3 method for class 'Seurat' FoldChange( object, ident.1 = NULL, ident.2 = NULL, group.by = NULL, subset.ident = NULL, assay = NULL, slot = "data", reduction = NULL, features = NULL, pseudocount.use = NULL, mean.fxn = NULL, base = 2, fc.name = NULL, ... )
object |
A Seurat object |
... |
Arguments passed to other methods |
cells.1 |
Vector of cell names belonging to group 1 |
cells.2 |
Vector of cell names belonging to group 2 |
mean.fxn |
Function to use for fold change or average difference calculation |
fc.name |
Name of the fold change, average difference, or custom function column in the output data.frame |
features |
Features to calculate fold change for. If NULL, use all features |
slot |
Slot to pull data from |
pseudocount.use |
Pseudocount to add to averaged expression values when calculating logFC. |
base |
The base with respect to which logarithms are computed. |
norm.method |
Normalization method for mean function selection
when |
ident.1 |
Identity class to calculate fold change for; pass an object of class
|
ident.2 |
A second identity class for comparison; if |
group.by |
Regroup cells into a different identity class prior to
calculating fold change (see example in |
subset.ident |
Subset a particular identity class prior to regrouping.
Only relevant if group.by is set (see example in |
assay |
Assay to use in fold change calculation |
reduction |
Reduction to use - will calculate average difference on cell embeddings |
If the slot is scale.data
or a reduction is specified, average difference
is returned instead of log fold change and the column is named "avg_diff".
Otherwise, log2 fold change is returned with column named "avg_log2_FC".
Returns a data.frame
FindMarkers
data("pbmc_small") FoldChange(pbmc_small, ident.1 = 1)
data("pbmc_small") FoldChange(pbmc_small, ident.1 = 1)
Get an Assay object from a given Seurat object.
GetAssay(object, ...) ## S3 method for class 'Seurat' GetAssay(object, assay = NULL, ...)
GetAssay(object, ...) ## S3 method for class 'Seurat' GetAssay(object, assay = NULL, ...)
object |
An object |
... |
Arguments passed to other methods |
assay |
Assay to get |
Returns an Assay object
data("pbmc_small") GetAssay(object = pbmc_small, assay = "RNA")
data("pbmc_small") GetAssay(object = pbmc_small, assay = "RNA")
Get Image Data
## S3 method for class 'SlideSeq' GetImage(object, mode = c("grob", "raster", "plotly", "raw"), ...) ## S3 method for class 'STARmap' GetImage(object, mode = c("grob", "raster", "plotly", "raw"), ...) ## S3 method for class 'VisiumV1' GetImage(object, mode = c("grob", "raster", "plotly", "raw"), ...)
## S3 method for class 'SlideSeq' GetImage(object, mode = c("grob", "raster", "plotly", "raw"), ...) ## S3 method for class 'STARmap' GetImage(object, mode = c("grob", "raster", "plotly", "raw"), ...) ## S3 method for class 'VisiumV1' GetImage(object, mode = c("grob", "raster", "plotly", "raw"), ...)
object |
An object |
mode |
How to return the image; should accept one of “grob”, “raster”, “plotly”, or “raw” |
... |
Arguments passed to other methods |
Get integration data
GetIntegrationData(object, integration.name, slot)
GetIntegrationData(object, integration.name, slot)
object |
Seurat object |
integration.name |
Name of integration object |
slot |
Which slot in integration object to get |
Returns data from the requested slot within the integrated object
This function calls sctransform::get_residuals.
GetResidual( object, features, assay = NULL, umi.assay = NULL, clip.range = NULL, replace.value = FALSE, na.rm = TRUE, verbose = TRUE )
GetResidual( object, features, assay = NULL, umi.assay = NULL, clip.range = NULL, replace.value = FALSE, na.rm = TRUE, verbose = TRUE )
object |
A seurat object |
features |
Name of features to add into the scale.data |
assay |
Name of the assay of the seurat object generated by SCTransform |
umi.assay |
Name of the assay of the seurat object containing UMI matrix and the default is RNA |
clip.range |
Numeric of length two specifying the min and max values the Pearson residual will be clipped to |
replace.value |
Recalculate residuals for all features, even if they are already present. Useful if you want to change the clip.range. |
na.rm |
For features where there is no feature model stored, return NA for residual value in scale.data when na.rm = FALSE. When na.rm is TRUE, only return residuals for features with a model stored for all cells. |
verbose |
Whether to print messages and progress bars |
Returns a Seurat object containing Pearson residuals of added features in its scale.data
data("pbmc_small") pbmc_small <- SCTransform(object = pbmc_small, variable.features.n = 20) pbmc_small <- GetResidual(object = pbmc_small, features = c('MS4A1', 'TCL1A'))
data("pbmc_small") pbmc_small <- SCTransform(object = pbmc_small, variable.features.n = 20) pbmc_small <- GetResidual(object = pbmc_small, features = c('MS4A1', 'TCL1A'))
Get Tissue Coordinates
## S3 method for class 'SlideSeq' GetTissueCoordinates(object, ...) ## S3 method for class 'STARmap' GetTissueCoordinates(object, qhulls = FALSE, ...) ## S3 method for class 'VisiumV1' GetTissueCoordinates( object, scale = "lowres", cols = c("imagerow", "imagecol"), ... )
## S3 method for class 'SlideSeq' GetTissueCoordinates(object, ...) ## S3 method for class 'STARmap' GetTissueCoordinates(object, qhulls = FALSE, ...) ## S3 method for class 'VisiumV1' GetTissueCoordinates( object, scale = "lowres", cols = c("imagerow", "imagecol"), ... )
object |
An object |
... |
Arguments passed to other methods |
qhulls |
return qhulls instead of centroids |
scale |
A factor to scale the coordinates by; choose from: 'tissue',
'fiducial', 'hires', 'lowres', or |
cols |
Columns of tissue coordinates data.frame to pull |
SeuratObject::GetTissueCoordinates
Utility function to easily pull out the name of the class with the maximum
prediction. This is useful if you've set prediction.assay = TRUE
in
TransferData
and want to have a vector with the predicted class.
GetTransferPredictions( object, assay = "predictions", slot = "data", score.filter = 0.75 )
GetTransferPredictions( object, assay = "predictions", slot = "data", score.filter = 0.75 )
object |
Seurat object |
assay |
Name of the assay holding the predictions |
slot |
Slot of the assay in which the prediction scores are stored |
score.filter |
Return "Unassigned" for any cell with a score less than this value |
Returns a vector of predicted class names
## Not run: prediction.assay <- TransferData(anchorset = anchors, refdata = reference$class) query[["predictions"]] <- prediction.assay query$predicted.id <- GetTransferPredictions(query) ## End(Not run)
## Not run: prediction.assay <- TransferData(anchorset = anchors, refdata = reference$class) query[["predictions"]] <- prediction.assay query$predicted.id <- GetTransferPredictions(query) ## End(Not run)
Compute the correlation of features broken down by groups with another covariate
GroupCorrelation( object, assay = NULL, slot = "scale.data", var = NULL, group.assay = NULL, min.cells = 5, ngroups = 6, do.plot = TRUE )
GroupCorrelation( object, assay = NULL, slot = "scale.data", var = NULL, group.assay = NULL, min.cells = 5, ngroups = 6, do.plot = TRUE )
object |
Seurat object |
assay |
Assay to pull the data from |
slot |
Slot in the assay to pull feature expression data from (counts, data, or scale.data) |
var |
Variable with which to correlate the features |
group.assay |
Compute the gene groups based off the data in this assay. |
min.cells |
Only compute for genes in at least this many cells |
ngroups |
Number of groups to split into |
do.plot |
Display the group correlation boxplot (via
|
A Seurat object with the correlation stored in metafeatures
Boxplot of correlation of a variable (e.g. number of UMIs) with expression data
GroupCorrelationPlot( object, assay = NULL, feature.group = "feature.grp", cor = "nCount_RNA_cor" )
GroupCorrelationPlot( object, assay = NULL, feature.group = "feature.grp", cor = "nCount_RNA_cor" )
object |
Seurat object |
assay |
Assay where the feature grouping info and correlations are stored |
feature.group |
Name of the column in meta.features where the feature grouping info is stored |
cor |
Name of the column in meta.features where correlation info is stored |
Returns a ggplot boxplot of correlations split by group
Get quick information from a scatterplot by hovering over points
HoverLocator(plot, information = NULL, axes = TRUE, dark.theme = FALSE, ...)
HoverLocator(plot, information = NULL, axes = TRUE, dark.theme = FALSE, ...)
plot |
A ggplot2 plot |
information |
An optional dataframe or matrix of extra information to be displayed on hover |
axes |
Display or hide x- and y-axes |
dark.theme |
Plot using a dark theme? |
... |
Extra parameters to be passed to |
layout
ggplot_build
DimPlot
FeaturePlot
## Not run: data("pbmc_small") plot <- DimPlot(object = pbmc_small) HoverLocator(plot = plot, information = FetchData(object = pbmc_small, vars = 'percent.mito')) ## End(Not run)
## Not run: data("pbmc_small") plot <- DimPlot(object = pbmc_small) HoverLocator(plot = plot, information = FetchData(object = pbmc_small, vars = 'percent.mito')) ## End(Not run)
Assign sample-of-origin for each cell, annotate doublets.
HTODemux( object, assay = "HTO", positive.quantile = 0.99, init = NULL, nstarts = 100, kfunc = "clara", nsamples = 100, seed = 42, verbose = TRUE )
HTODemux( object, assay = "HTO", positive.quantile = 0.99, init = NULL, nstarts = 100, kfunc = "clara", nsamples = 100, seed = 42, verbose = TRUE )
object |
Seurat object. Assumes that the hash tag oligo (HTO) data has been added and normalized. |
assay |
Name of the Hashtag assay (HTO by default) |
positive.quantile |
The quantile of inferred 'negative' distribution for each hashtag - over which the cell is considered 'positive'. Default is 0.99 |
init |
Initial number of clusters for hashtags. Default is the # of hashtag oligo names + 1 (to account for negatives) |
nstarts |
nstarts value for k-means clustering (for kfunc = "kmeans"). 100 by default |
kfunc |
Clustering function for initial hashtag grouping. Default is "clara" for fast k-medoids clustering on large applications, also support "kmeans" for kmeans clustering |
nsamples |
Number of samples to be drawn from the dataset used for clustering, for kfunc = "clara" |
seed |
Sets the random seed. If NULL, seed is not set |
verbose |
Prints the output |
The Seurat object with the following demultiplexed information stored in the meta data:
Name of hashtag with the highest signal
Name of hashtag with the second highest signal
The difference between signals for hash.maxID and hash.secondID
Classification result, with doublets/multiplets named by the top two highest hashtags
Global classification result (singlet, doublet or negative)
Classification result where doublet IDs are collapsed
## Not run: object <- HTODemux(object) ## End(Not run)
## Not run: object <- HTODemux(object) ## End(Not run)
Draws a heatmap of hashtag oligo signals across singlets/doublets/negative cells. Allows for the visualization of HTO demultiplexing results.
HTOHeatmap( object, assay = "HTO", classification = paste0(assay, "_classification"), global.classification = paste0(assay, "_classification.global"), ncells = 5000, singlet.names = NULL, raster = TRUE )
HTOHeatmap( object, assay = "HTO", classification = paste0(assay, "_classification"), global.classification = paste0(assay, "_classification.global"), ncells = 5000, singlet.names = NULL, raster = TRUE )
object |
Seurat object. Assumes that the hash tag oligo (HTO) data has been added and normalized, and demultiplexing has been run with HTODemux(). |
assay |
Hashtag assay name. |
classification |
The naming for metadata column with classification result from HTODemux(). |
global.classification |
The slot for metadata column specifying a cell as singlet/doublet/negative. |
ncells |
Number of cells to plot. Default is to choose 5000 cells by random subsampling, to avoid having to draw exceptionally large heatmaps. |
singlet.names |
Namings for the singlets. Default is to use the same names as HTOs. |
raster |
If true, plot with geom_raster, else use geom_tile. geom_raster may look blurry on some viewing applications such as Preview due to how the raster is interpolated. Set this to FALSE if you are encountering that issue (note that plots may take longer to produce/render). |
Returns a ggplot2 plot object.
## Not run: object <- HTODemux(object) HTOHeatmap(object) ## End(Not run)
## Not run: object <- HTODemux(object) HTOHeatmap(object) ## End(Not run)
Get variable feature information from SCTAssay
objects
## S3 method for class 'SCTAssay' HVFInfo(object, selection.method, status = FALSE, ...)
## S3 method for class 'SCTAssay' HVFInfo(object, selection.method, status = FALSE, ...)
object |
An object |
selection.method |
Which method to pull. For
For
|
status |
Add variable status to the resulting data frame |
... |
Arguments passed to other methods |
# Get the HVF info directly from an SCTAssay object pbmc_small <- SCTransform(pbmc_small) HVFInfo(pbmc_small[["SCT"]], selection.method = 'sct')[1:5, ]
# Get the HVF info directly from an SCTAssay object pbmc_small <- SCTransform(pbmc_small) HVFInfo(pbmc_small[["SCT"]], selection.method = 'sct')[1:5, ]
Visualize features in dimensional reduction space interactively
IFeaturePlot(object, feature, dims = c(1, 2), reduction = NULL, slot = "data")
IFeaturePlot(object, feature, dims = c(1, 2), reduction = NULL, slot = "data")
object |
Seurat object |
feature |
Feature to plot |
dims |
Dimensions to plot, must be a two-length numeric vector specifying x- and y-dimensions |
reduction |
Which dimensionality reduction to use. If not specified, first searches for umap, then tsne, then pca |
slot |
Which slot to pull expression data from? |
Returns the final plot as a ggplot object
Visualize clusters or other categorical groupings in a spatial context
ImageDimPlot( object, fov = NULL, boundaries = NULL, group.by = NULL, split.by = NULL, cols = NULL, shuffle.cols = FALSE, size = 0.5, molecules = NULL, mols.size = 0.1, mols.cols = NULL, mols.alpha = 1, nmols = 1000, alpha = 1, border.color = "white", border.size = NULL, na.value = "grey50", dark.background = TRUE, crop = FALSE, cells = NULL, overlap = FALSE, axes = FALSE, combine = TRUE, coord.fixed = TRUE )
ImageDimPlot( object, fov = NULL, boundaries = NULL, group.by = NULL, split.by = NULL, cols = NULL, shuffle.cols = FALSE, size = 0.5, molecules = NULL, mols.size = 0.1, mols.cols = NULL, mols.alpha = 1, nmols = 1000, alpha = 1, border.color = "white", border.size = NULL, na.value = "grey50", dark.background = TRUE, crop = FALSE, cells = NULL, overlap = FALSE, axes = FALSE, combine = TRUE, coord.fixed = TRUE )
object |
A |
fov |
Name of FOV to plot |
boundaries |
A vector of segmentation boundaries per image to plot; can be a character vector, a named character vector, or a named list. Names should be the names of FOVs and values should be the names of segmentation boundaries |
group.by |
Name of one or more metadata columns to group (color) cells by (for example, orig.ident); pass 'ident' to group by identity class |
split.by |
Name of a metadata column to split plot by;
see |
cols |
Vector of colors, each color corresponds to an identity class. This may also be a single character
or numeric value corresponding to a palette as specified by |
shuffle.cols |
Randomly shuffle colors when a palette or
vector of colors is provided to |
size |
Point size for cells when plotting centroids |
molecules |
A vector of molecules to plot |
mols.size |
Point size for molecules |
mols.cols |
A vector of color for molecules. The "Set1" palette from RColorBrewer is used by default. |
mols.alpha |
Alpha value for molecules, should be between 0 and 1 |
nmols |
Max number of each molecule specified in 'molecules' to plot |
alpha |
Alpha value, should be between 0 and 1; when plotting multiple
boundaries, |
border.color |
Color of cell segmentation border; pass |
border.size |
Thickness of cell segmentation borders; pass |
na.value |
Color value for NA points when using custom scale |
dark.background |
Set plot background to black |
crop |
Crop the plots to area with cells only |
cells |
Vector of cells to plot (default is all cells) |
overlap |
Overlay boundaries from a single image to create a single
plot; if |
axes |
Keep axes and panel background |
combine |
Combine plots into a single
|
coord.fixed |
Plot cartesian coordinates with fixed aspect ratio |
If combine = TRUE
, a patchwork
ggplot object; otherwise, a list of ggplot objects
Visualize expression in a spatial context
ImageFeaturePlot( object, features, fov = NULL, boundaries = NULL, cols = if (isTRUE(x = blend)) { c("lightgrey", "#ff0000", "#00ff00") } else { c("lightgrey", "firebrick1") }, size = 0.5, min.cutoff = NA, max.cutoff = NA, split.by = NULL, molecules = NULL, mols.size = 0.1, mols.cols = NULL, nmols = 1000, alpha = 1, border.color = "white", border.size = NULL, dark.background = TRUE, blend = FALSE, blend.threshold = 0.5, crop = FALSE, cells = NULL, scale = c("feature", "all", "none"), overlap = FALSE, axes = FALSE, combine = TRUE, coord.fixed = TRUE )
ImageFeaturePlot( object, features, fov = NULL, boundaries = NULL, cols = if (isTRUE(x = blend)) { c("lightgrey", "#ff0000", "#00ff00") } else { c("lightgrey", "firebrick1") }, size = 0.5, min.cutoff = NA, max.cutoff = NA, split.by = NULL, molecules = NULL, mols.size = 0.1, mols.cols = NULL, nmols = 1000, alpha = 1, border.color = "white", border.size = NULL, dark.background = TRUE, blend = FALSE, blend.threshold = 0.5, crop = FALSE, cells = NULL, scale = c("feature", "all", "none"), overlap = FALSE, axes = FALSE, combine = TRUE, coord.fixed = TRUE )
object |
Seurat object |
features |
Vector of features to plot. Features can come from:
|
fov |
Name of FOV to plot |
boundaries |
A vector of segmentation boundaries per image to plot; can be a character vector, a named character vector, or a named list. Names should be the names of FOVs and values should be the names of segmentation boundaries |
cols |
The two colors to form the gradient over. Provide as string vector with
the first color corresponding to low values, the second to high. Also accepts a Brewer
color scale or vector of colors. Note: this will bin the data into number of colors provided.
When blend is
|
size |
Point size for cells when plotting centroids |
min.cutoff , max.cutoff
|
Vector of minimum and maximum cutoff values for each feature, may specify quantile in the form of 'q##' where '##' is the quantile (eg, 'q1', 'q10') |
split.by |
A factor in object metadata to split the feature plot by, pass 'ident'
to split by cell identity'; similar to the old |
molecules |
A vector of molecules to plot |
mols.size |
Point size for molecules |
mols.cols |
A vector of color for molecules. The "Set1" palette from RColorBrewer is used by default. |
nmols |
Max number of each molecule specified in 'molecules' to plot |
alpha |
Alpha value, should be between 0 and 1; when plotting multiple
boundaries, |
border.color |
Color of cell segmentation border; pass |
border.size |
Thickness of cell segmentation borders; pass |
dark.background |
Set plot background to black |
blend |
Scale and blend expression values to visualize coexpression of two features |
blend.threshold |
The color cutoff from weak signal to strong signal; ranges from 0 to 1. |
crop |
Crop the plots to area with cells only |
cells |
Vector of cells to plot (default is all cells) |
scale |
Set color scaling across multiple plots; choose from:
Ignored if |
overlap |
Overlay boundaries from a single image to create a single
plot; if |
axes |
Keep axes and panel background |
combine |
Combine plots into a single |
coord.fixed |
Plot cartesian coordinates with fixed aspect ratio |
If combine = TRUE
, a patchwork
ggplot object; otherwise, a list of ggplot objects
Perform dataset integration using a pre-computed AnchorSet
.
IntegrateData( anchorset, new.assay.name = "integrated", normalization.method = c("LogNormalize", "SCT"), features = NULL, features.to.integrate = NULL, dims = 1:30, k.weight = 100, weight.reduction = NULL, sd.weight = 1, sample.tree = NULL, preserve.order = FALSE, eps = 0, verbose = TRUE )
IntegrateData( anchorset, new.assay.name = "integrated", normalization.method = c("LogNormalize", "SCT"), features = NULL, features.to.integrate = NULL, dims = 1:30, k.weight = 100, weight.reduction = NULL, sd.weight = 1, sample.tree = NULL, preserve.order = FALSE, eps = 0, verbose = TRUE )
anchorset |
An |
new.assay.name |
Name for the new assay containing the integrated data |
normalization.method |
Name of normalization method used: LogNormalize or SCT |
features |
Vector of features to use when computing the PCA to determine the weights. Only set if you want a different set from those used in the anchor finding process |
features.to.integrate |
Vector of features to integrate. By default, will use the features used in anchor finding. |
dims |
Number of dimensions to use in the anchor weighting procedure |
k.weight |
Number of neighbors to consider when weighting anchors |
weight.reduction |
Dimension reduction to use when calculating anchor weights. This can be one of:
Note that, if specified, the requested dimension reduction will only be used for calculating anchor weights in the first merge between reference and query, as the merged object will subsequently contain more cells than was in query, and weights will need to be calculated for all cells in the object. |
sd.weight |
Controls the bandwidth of the Gaussian kernel for weighting |
sample.tree |
Specify the order of integration. Order of integration
should be encoded in a matrix, where each row represents one of the pairwise
integration steps. Negative numbers specify a dataset, positive numbers
specify the integration results from a given row (the format of the merge
matrix included in the [,1] [,2] [1,] -2 -3 [2,] 1 -1 Which would cause dataset 2 and 3 to be integrated first, then the resulting object integrated with dataset 1. If NULL, the sample tree will be computed automatically. |
preserve.order |
Do not reorder objects based on size for each pairwise integration. |
eps |
Error bound on the neighbor finding algorithm (from
|
verbose |
Print progress bars and output |
The main steps of this procedure are outlined below. For a more detailed description of the methodology, please see Stuart, Butler, et al Cell 2019. doi:10.1016/j.cell.2019.05.031; doi:10.1101/460147
For pairwise integration:
Construct a weights matrix that defines the association between each
query cell and each anchor. These weights are computed as 1 - the distance
between the query cell and the anchor divided by the distance of the query
cell to the k.weight
th anchor multiplied by the anchor score
computed in FindIntegrationAnchors
. We then apply a Gaussian
kernel width a bandwidth defined by sd.weight
and normalize across
all k.weight
anchors.
Compute the anchor integration matrix as the difference between the two expression matrices for every pair of anchor cells
Compute the transformation matrix as the product of the integration matrix and the weights matrix.
Subtract the transformation matrix from the original expression matrix.
For multiple dataset integration, we perform iterative pairwise integration.
To determine the order of integration (if not specified via
sample.tree
), we
Define a distance between datasets as the total number of cells in the smaller dataset divided by the total number of anchors between the two datasets.
Compute all pairwise distances between datasets
Cluster this distance matrix to determine a guide tree
Returns a Seurat
object with a new integrated
Assay
. If normalization.method = "LogNormalize"
, the
integrated data is returned to the data
slot and can be treated as
log-normalized, corrected data. If normalization.method = "SCT"
, the
integrated data is returned to the scale.data
slot and can be treated
as centered, corrected Pearson residuals.
Stuart T, Butler A, et al. Comprehensive Integration of Single-Cell Data. Cell. 2019;177:1888-1902 doi:10.1016/j.cell.2019.05.031
## Not run: # to install the SeuratData package see https://github.com/satijalab/seurat-data library(SeuratData) data("panc8") # panc8 is a merged Seurat object containing 8 separate pancreas datasets # split the object by dataset pancreas.list <- SplitObject(panc8, split.by = "tech") # perform standard preprocessing on each object for (i in 1:length(pancreas.list)) { pancreas.list[[i]] <- NormalizeData(pancreas.list[[i]], verbose = FALSE) pancreas.list[[i]] <- FindVariableFeatures( pancreas.list[[i]], selection.method = "vst", nfeatures = 2000, verbose = FALSE ) } # find anchors anchors <- FindIntegrationAnchors(object.list = pancreas.list) # integrate data integrated <- IntegrateData(anchorset = anchors) ## End(Not run)
## Not run: # to install the SeuratData package see https://github.com/satijalab/seurat-data library(SeuratData) data("panc8") # panc8 is a merged Seurat object containing 8 separate pancreas datasets # split the object by dataset pancreas.list <- SplitObject(panc8, split.by = "tech") # perform standard preprocessing on each object for (i in 1:length(pancreas.list)) { pancreas.list[[i]] <- NormalizeData(pancreas.list[[i]], verbose = FALSE) pancreas.list[[i]] <- FindVariableFeatures( pancreas.list[[i]], selection.method = "vst", nfeatures = 2000, verbose = FALSE ) } # find anchors anchors <- FindIntegrationAnchors(object.list = pancreas.list) # integrate data integrated <- IntegrateData(anchorset = anchors) ## End(Not run)
Perform dataset integration using a pre-computed Anchorset of specified low dimensional representations.
IntegrateEmbeddings(anchorset, ...) ## S3 method for class 'IntegrationAnchorSet' IntegrateEmbeddings( anchorset, new.reduction.name = "integrated_dr", reductions = NULL, dims.to.integrate = NULL, k.weight = 100, weight.reduction = NULL, sd.weight = 1, sample.tree = NULL, preserve.order = FALSE, verbose = TRUE, ... ) ## S3 method for class 'TransferAnchorSet' IntegrateEmbeddings( anchorset, reference, query, new.reduction.name = "integrated_dr", reductions = "pcaproject", dims.to.integrate = NULL, k.weight = 100, weight.reduction = NULL, reuse.weights.matrix = TRUE, sd.weight = 1, preserve.order = FALSE, verbose = TRUE, ... )
IntegrateEmbeddings(anchorset, ...) ## S3 method for class 'IntegrationAnchorSet' IntegrateEmbeddings( anchorset, new.reduction.name = "integrated_dr", reductions = NULL, dims.to.integrate = NULL, k.weight = 100, weight.reduction = NULL, sd.weight = 1, sample.tree = NULL, preserve.order = FALSE, verbose = TRUE, ... ) ## S3 method for class 'TransferAnchorSet' IntegrateEmbeddings( anchorset, reference, query, new.reduction.name = "integrated_dr", reductions = "pcaproject", dims.to.integrate = NULL, k.weight = 100, weight.reduction = NULL, reuse.weights.matrix = TRUE, sd.weight = 1, preserve.order = FALSE, verbose = TRUE, ... )
anchorset |
An AnchorSet object |
... |
Reserved for internal use |
new.reduction.name |
Name for new integrated dimensional reduction. |
reductions |
Name of reductions to be integrated. For a
TransferAnchorSet, this should be the name of a reduction present in the
anchorset object (for example, "pcaproject"). For an IntegrationAnchorSet,
this should be a |
dims.to.integrate |
Number of dimensions to return integrated values for |
k.weight |
Number of neighbors to consider when weighting anchors |
weight.reduction |
Dimension reduction to use when calculating anchor weights. This can be one of:
|
sd.weight |
Controls the bandwidth of the Gaussian kernel for weighting |
sample.tree |
Specify the order of integration. Order of integration
should be encoded in a matrix, where each row represents one of the pairwise
integration steps. Negative numbers specify a dataset, positive numbers
specify the integration results from a given row (the format of the merge
matrix included in the [,1] [,2] [1,] -2 -3 [2,] 1 -1 Which would cause dataset 2 and 3 to be integrated first, then the resulting object integrated with dataset 1. If NULL, the sample tree will be computed automatically. |
preserve.order |
Do not reorder objects based on size for each pairwise integration. |
verbose |
Print progress bars and output |
reference |
Reference object used in anchorset construction |
query |
Query object used in anchorset construction |
reuse.weights.matrix |
Can be used in conjunction with the store.weights parameter in TransferData to reuse a precomputed weights matrix. |
The main steps of this procedure are identical to IntegrateData
with one key distinction. When computing the weights matrix, the distance
calculations are performed in the full space of integrated embeddings when
integrating more than two datasets, as opposed to a reduced PCA space which
is the default behavior in IntegrateData
.
When called on a TransferAnchorSet (from FindTransferAnchors), this will return the query object with the integrated embeddings stored in a new reduction. When called on an IntegrationAnchorSet (from IntegrateData), this will return a merged object with the integrated reduction stored.
Inherits from the Anchorset class. Implemented mainly for method dispatch
purposes. See AnchorSet
for slot details.
The IntegrationData object is an intermediate storage container used internally throughout the integration procedure to hold bits of data that are useful downstream.
neighbors
List of neighborhood information for cells (outputs of RANN::nn2
)
weights
Anchor weight matrix
integration.matrix
Integration matrix
anchors
Anchor matrix
offsets
The offsets used to enable cell look up in downstream functions
objects.ncell
Number of cells in each object in the object.list
sample.tree
Sample tree used for ordering multi-dataset integration
Visualize clusters spatially and interactively
ISpatialDimPlot(object, image = NULL, group.by = NULL, alpha = c(0.3, 1))
ISpatialDimPlot(object, image = NULL, group.by = NULL, alpha = c(0.3, 1))
object |
Seurat object |
image |
Name of the image to use in the plot |
group.by |
Name of one or more metadata columns to group (color) cells by (for example, orig.ident); pass 'ident' to group by identity class |
alpha |
Controls opacity of spots. Provide as a vector specifying the min and max for SpatialFeaturePlot. For SpatialDimPlot, provide a single alpha value for each plot. |
Returns final plot as a ggplot object
Visualize features spatially and interactively
ISpatialFeaturePlot( object, feature, image = NULL, slot = "data", alpha = c(0.1, 1) )
ISpatialFeaturePlot( object, feature, image = NULL, slot = "data", alpha = c(0.1, 1) )
object |
Seurat object |
feature |
Feature to visualize |
image |
Name of the image to use in the plot |
slot |
Which slot to pull expression data from? |
alpha |
Controls opacity of spots. Provide as a vector specifying the min and max for SpatialFeaturePlot. For SpatialDimPlot, provide a single alpha value for each plot. |
Returns final plot as a ggplot object
Randomly permutes a subset of data, and calculates projected PCA scores for these 'random' genes. Then compares the PCA scores for the 'random' genes with the observed PCA scores to determine statistical signifance. End result is a p-value for each gene's association with each principal component.
JackStraw( object, reduction = "pca", assay = NULL, dims = 20, num.replicate = 100, prop.freq = 0.01, verbose = TRUE, maxit = 1000 )
JackStraw( object, reduction = "pca", assay = NULL, dims = 20, num.replicate = 100, prop.freq = 0.01, verbose = TRUE, maxit = 1000 )
object |
Seurat object |
reduction |
DimReduc to use. ONLY PCA CURRENTLY SUPPORTED. |
assay |
Assay used to calculate reduction. |
dims |
Number of PCs to compute significance for |
num.replicate |
Number of replicate samplings to perform |
prop.freq |
Proportion of the data to randomly permute for each replicate |
verbose |
Print progress bar showing the number of replicates that have been processed. |
maxit |
maximum number of iterations to be performed by the irlba function of RunPCA |
Returns a Seurat object where JS(object = object[['pca']], slot = 'empirical') represents p-values for each gene in the PCA analysis. If ProjectPCA is subsequently run, JS(object = object[['pca']], slot = 'full') then represents p-values for all genes.
Inspired by Chung et al, Bioinformatics (2014)
## Not run: data("pbmc_small") pbmc_small = suppressWarnings(JackStraw(pbmc_small)) head(JS(object = pbmc_small[['pca']], slot = 'empirical')) ## End(Not run)
## Not run: data("pbmc_small") pbmc_small = suppressWarnings(JackStraw(pbmc_small)) head(JS(object = pbmc_small[['pca']], slot = 'empirical')) ## End(Not run)
For more details, please see the documentation in
SeuratObject
SeuratObject::JackStrawData-class
Plots the results of the JackStraw analysis for PCA significance. For each PC, plots a QQ-plot comparing the distribution of p-values for all genes across each PC, compared with a uniform distribution. Also determines a p-value for the overall significance of each PC (see Details).
JackStrawPlot( object, dims = 1:5, cols = NULL, reduction = "pca", xmax = 0.1, ymax = 0.3 )
JackStrawPlot( object, dims = 1:5, cols = NULL, reduction = "pca", xmax = 0.1, ymax = 0.3 )
object |
Seurat object |
dims |
Dims to plot |
cols |
Vector of colors, each color corresponds to an individual PC. This may also be a single character
or numeric value corresponding to a palette as specified by |
reduction |
reduction to pull jackstraw info from |
xmax |
X-axis maximum on each QQ plot. |
ymax |
Y-axis maximum on each QQ plot. |
Significant PCs should show a p-value distribution (black curve) that is strongly skewed to the left compared to the null distribution (dashed line) The p-value for each PC is based on a proportion test comparing the number of genes with a p-value below a particular threshold (score.thresh), compared with the proportion of genes expected under a uniform distribution of p-values.
A ggplot object
Omri Wurtzel
data("pbmc_small") JackStrawPlot(object = pbmc_small)
data("pbmc_small") JackStrawPlot(object = pbmc_small)
Perform l2 normalization on CCs
L2CCA(object, ...)
L2CCA(object, ...)
object |
Seurat object |
... |
Additional parameters to L2Dim. |
Perform l2 normalization on given dimensional reduction
L2Dim(object, reduction, new.dr = NULL, new.key = NULL)
L2Dim(object, reduction, new.dr = NULL, new.key = NULL)
object |
Seurat object |
reduction |
Dimensional reduction to normalize |
new.dr |
name of new dimensional reduction to store (default is olddr.l2) |
new.key |
name of key for new dimensional reduction |
Returns a Seurat
object
Label clusters on a ggplot2-based scatter plot
LabelClusters( plot, id, clusters = NULL, labels = NULL, split.by = NULL, repel = TRUE, box = FALSE, geom = "GeomPoint", position = "median", ... )
LabelClusters( plot, id, clusters = NULL, labels = NULL, split.by = NULL, repel = TRUE, box = FALSE, geom = "GeomPoint", position = "median", ... )
plot |
A ggplot2-based scatter plot |
id |
Name of variable used for coloring scatter plot |
clusters |
Vector of cluster ids to label |
labels |
Custom labels for the clusters |
split.by |
Split labels by some grouping label, useful when using
|
repel |
Use |
box |
Use geom_label/geom_label_repel (includes a box around the text labels) |
geom |
Name of geom to get X/Y aesthetic names for |
position |
How to place the label if repel = FALSE. If "median", place the label at the median position. If "nearest" place the label at the position of the nearest data point to the median. |
... |
Extra parameters to |
A ggplot2-based scatter plot with cluster labels
data("pbmc_small") plot <- DimPlot(object = pbmc_small) LabelClusters(plot = plot, id = 'ident')
data("pbmc_small") plot <- DimPlot(object = pbmc_small) LabelClusters(plot = plot, id = 'ident')
Add text labels to a ggplot2 plot
LabelPoints( plot, points, labels = NULL, repel = FALSE, xnudge = 0.3, ynudge = 0.05, ... )
LabelPoints( plot, points, labels = NULL, repel = FALSE, xnudge = 0.3, ynudge = 0.05, ... )
plot |
A ggplot2 plot with a GeomPoint layer |
points |
A vector of points to label; if |
labels |
A vector of labels for the points; if |
repel |
Use |
xnudge , ynudge
|
Amount to nudge X and Y coordinates of labels by |
... |
Extra parameters passed to |
A ggplot object
data("pbmc_small") ff <- TopFeatures(object = pbmc_small[['pca']]) cc <- TopCells(object = pbmc_small[['pca']]) plot <- FeatureScatter(object = pbmc_small, feature1 = ff[1], feature2 = ff[2]) LabelPoints(plot = plot, points = cc)
data("pbmc_small") ff <- TopFeatures(object = pbmc_small[['pca']]) cc <- TopCells(object = pbmc_small[['pca']]) plot <- FeatureScatter(object = pbmc_small, feature1 = ff[1], feature2 = ff[2]) LabelPoints(plot = plot, points = cc)
Visualize spatial and clustering (dimensional reduction) data in a linked, interactive framework
LinkedDimPlot( object, dims = 1:2, reduction = NULL, image = NULL, group.by = NULL, alpha = c(0.1, 1), combine = TRUE ) LinkedFeaturePlot( object, feature, dims = 1:2, reduction = NULL, image = NULL, slot = "data", alpha = c(0.1, 1), combine = TRUE )
LinkedDimPlot( object, dims = 1:2, reduction = NULL, image = NULL, group.by = NULL, alpha = c(0.1, 1), combine = TRUE ) LinkedFeaturePlot( object, feature, dims = 1:2, reduction = NULL, image = NULL, slot = "data", alpha = c(0.1, 1), combine = TRUE )
object |
Seurat object |
dims |
Dimensions to plot, must be a two-length numeric vector specifying x- and y-dimensions |
reduction |
Which dimensionality reduction to use. If not specified, first searches for umap, then tsne, then pca |
image |
Name of the image to use in the plot |
group.by |
Name of one or more metadata columns to group (color) cells by (for example, orig.ident); pass 'ident' to group by identity class |
alpha |
Controls opacity of spots. Provide as a vector specifying the min and max for SpatialFeaturePlot. For SpatialDimPlot, provide a single alpha value for each plot. |
combine |
Combine plots into a single |
feature |
Feature to visualize |
slot |
Which slot to pull expression data from? |
Returns final plots. If combine
, plots are stiched together
using CombinePlots
; otherwise, returns a list of ggplot objects
## Not run: LinkedDimPlot(seurat.object) LinkedFeaturePlot(seurat.object, feature = 'Hpca') ## End(Not run)
## Not run: LinkedDimPlot(seurat.object) LinkedFeaturePlot(seurat.object, feature = 'Hpca') ## End(Not run)
Seurat
objectLoad a 10x Genomics Visium Spatial Experiment into a Seurat
object
Load10X_Spatial( data.dir, filename = "filtered_feature_bc_matrix.h5", assay = "Spatial", slice = "slice1", filter.matrix = TRUE, to.upper = FALSE, image = NULL, ... )
Load10X_Spatial( data.dir, filename = "filtered_feature_bc_matrix.h5", assay = "Spatial", slice = "slice1", filter.matrix = TRUE, to.upper = FALSE, image = NULL, ... )
data.dir |
Directory containing the H5 file specified by |
filename |
Name of H5 file containing the feature barcode matrix |
assay |
Name of the initial assay |
slice |
Name for the stored image of the tissue slice |
filter.matrix |
Only keep spots that have been determined to be over tissue |
to.upper |
Converts all feature names to upper case. This can provide an approximate conversion of mouse to human gene names which can be useful in an explorative analysis. For cross-species comparisons, orthologous genes should be identified across species and used instead. |
image |
An object of class VisiumV1. Typically, an output from |
... |
Arguments passed to |
A Seurat
object
## Not run: data_dir <- 'path/to/data/directory' list.files(data_dir) # Should show filtered_feature_bc_matrix.h5 Load10X_Spatial(data.dir = data_dir) ## End(Not run)
## Not run: data_dir <- 'path/to/data/directory' list.files(data_dir) # Should show filtered_feature_bc_matrix.h5 Load10X_Spatial(data.dir = data_dir) ## End(Not run)
Load the Annoy index file
LoadAnnoyIndex(object, file)
LoadAnnoyIndex(object, file)
object |
Neighbor object |
file |
Path to file with annoy index |
Returns the Neighbor object with the index stored
Load Curio Seeker data
LoadCurioSeeker(data.dir, assay = "Spatial")
LoadCurioSeeker(data.dir, assay = "Spatial")
data.dir |
location of data directory that contains the counts matrix, gene names, barcodes/beads, and barcodes/bead location files. |
assay |
Name of assay to associate spatial data to |
A Seurat
object
Load STARmap data
LoadSTARmap( data.dir, counts.file = "cell_barcode_count.csv", gene.file = "genes.csv", qhull.file = "qhulls.tsv", centroid.file = "centroids.tsv", assay = "Spatial", image = "image" )
LoadSTARmap( data.dir, counts.file = "cell_barcode_count.csv", gene.file = "genes.csv", qhull.file = "qhulls.tsv", centroid.file = "centroids.tsv", assay = "Spatial", image = "image" )
data.dir |
location of data directory that contains the counts matrix, gene name, qhull, and centroid files. |
counts.file |
name of file containing the counts matrix (csv) |
gene.file |
name of file containing the gene names (csv) |
qhull.file |
name of file containing the hull coordinates (tsv) |
centroid.file |
name of file containing the centroid positions (tsv) |
assay |
Name of assay to associate spatial data to |
image |
Name of "image" object storing spatial coordinates |
A Seurat
object
Read and Load 10x Genomics Xenium in-situ data
LoadXenium(data.dir, fov = "fov", assay = "Xenium") ReadXenium( data.dir, outs = c("matrix", "microns"), type = "centroids", mols.qv.threshold = 20 )
LoadXenium(data.dir, fov = "fov", assay = "Xenium") ReadXenium( data.dir, outs = c("matrix", "microns"), type = "centroids", mols.qv.threshold = 20 )
data.dir |
Directory containing all Xenium output files with default filenames |
fov |
FOV name |
assay |
Assay name |
outs |
Types of molecular outputs to read; choose one or more of:
|
type |
Type of cell spatial coordinate matrices to read; choose one or more of:
|
mols.qv.threshold |
Remove transcript molecules with a QV less than this threshold. QV >= 20 is the standard threshold used to construct the cell x gene count matrix. |
LoadXenium
: A Seurat
object
ReadXenium
: A list with some combination of the
following values:
“matrix
”: a
sparse matrix with expression data; cells
are columns and features are rows
“centroids
”: a data frame with cell centroid
coordinates in three columns: “x”, “y”, and “cell”
“pixels
”: a data frame with molecule pixel coordinates
in three columns: “x”, “y”, and “gene”
Calculates a metric that describes how well the local structure of each group prior to integration is preserved after integration. This procedure works as follows: For each group, compute a PCA, compute the top num.neighbors in pca space, compute the top num.neighbors in corrected pca space, compute the size of the intersection of those two sets of neighbors. Return the average over all groups.
LocalStruct( object, grouping.var, idents = NULL, neighbors = 100, reduction = "pca", reduced.dims = 1:10, orig.dims = 1:10, verbose = TRUE )
LocalStruct( object, grouping.var, idents = NULL, neighbors = 100, reduction = "pca", reduced.dims = 1:10, orig.dims = 1:10, verbose = TRUE )
object |
Seurat object |
grouping.var |
Grouping variable |
idents |
Optionally specify a set of idents to compute metric for |
neighbors |
Number of neighbors to compute in pca/corrected pca space |
reduction |
Dimensional reduction to use for corrected space |
reduced.dims |
Number of reduced dimensions to use |
orig.dims |
Number of PCs to use in original space |
verbose |
Display progress bar |
Returns the average preservation metric
Normalize count data per cell and transform to log scale
LogNormalize(data, scale.factor = 10000, verbose = TRUE)
LogNormalize(data, scale.factor = 10000, verbose = TRUE)
data |
Matrix with the raw count data |
scale.factor |
Scale the data. Default is 1e4 |
verbose |
Print progress |
Returns a matrix with the normalize and log transformed data
mat <- matrix(data = rbinom(n = 25, size = 5, prob = 0.2), nrow = 5) mat mat_norm <- LogNormalize(data = mat) mat_norm
mat <- matrix(data = rbinom(n = 25, size = 5, prob = 0.2), nrow = 5) mat mat_norm <- LogNormalize(data = mat) mat_norm
Calculate the variance to mean ratio (VMR) in non-logspace (return answer in log-space)
LogVMR(x, ...)
LogVMR(x, ...)
x |
A vector of values |
... |
Other arguments (not used) |
Returns the VMR in log-space
LogVMR(x = c(1, 2, 3))
LogVMR(x = c(1, 2, 3))
This metric was designed to help identify query cells that aren't well represented in the reference dataset. The intuition for the score is that we are going to project the query cells into a reference-defined space and then project them back onto the query. By comparing the neighborhoods before and after projection, we identify cells who's local neighborhoods are the most affected by this transformation. This could be because there is a population of query cells that aren't present in the reference or the state of the cells in the query is significantly different from the equivalent cell type in the reference.
MappingScore(anchors, ...) ## Default S3 method: MappingScore( anchors, combined.object, query.neighbors, ref.embeddings, query.embeddings, kanchors = 50, ndim = 50, ksmooth = 100, ksnn = 20, snn.prune = 0, subtract.first.nn = TRUE, nn.method = "annoy", n.trees = 50, query.weights = NULL, verbose = TRUE, ... ) ## S3 method for class 'AnchorSet' MappingScore( anchors, kanchors = 50, ndim = 50, ksmooth = 100, ksnn = 20, snn.prune = 0, subtract.first.nn = TRUE, nn.method = "annoy", n.trees = 50, query.weights = NULL, verbose = TRUE, ... )
MappingScore(anchors, ...) ## Default S3 method: MappingScore( anchors, combined.object, query.neighbors, ref.embeddings, query.embeddings, kanchors = 50, ndim = 50, ksmooth = 100, ksnn = 20, snn.prune = 0, subtract.first.nn = TRUE, nn.method = "annoy", n.trees = 50, query.weights = NULL, verbose = TRUE, ... ) ## S3 method for class 'AnchorSet' MappingScore( anchors, kanchors = 50, ndim = 50, ksmooth = 100, ksnn = 20, snn.prune = 0, subtract.first.nn = TRUE, nn.method = "annoy", n.trees = 50, query.weights = NULL, verbose = TRUE, ... )
anchors |
AnchorSet object or just anchor matrix from the Anchorset object returned from FindTransferAnchors |
... |
Reserved for internal use |
combined.object |
Combined object (ref + query) from the Anchorset object returned |
query.neighbors |
Neighbors object computed on query cells |
ref.embeddings |
Reference embeddings matrix |
query.embeddings |
Query embeddings matrix |
kanchors |
Number of anchors to use in projection steps when computing weights |
ndim |
Number of dimensions to use when working with low dimensional projections of the data |
ksmooth |
Number of cells to average over when computing transition probabilities |
ksnn |
Number of cells to average over when determining the kernel bandwidth from the SNN graph |
snn.prune |
Amount of pruning to apply to edges in SNN graph |
subtract.first.nn |
Option to the scoring function when computing distances to subtract the distance to the first nearest neighbor |
nn.method |
Nearest neighbor method to use (annoy or RANN) |
n.trees |
More trees gives higher precision when using annoy approximate nearest neighbor search |
query.weights |
Query weights matrix for reuse |
verbose |
Display messages/progress |
Returns a vector of cell scores
This is a convenience wrapper function around the following three functions
that are often run together when mapping query data to a reference:
TransferData
, IntegrateEmbeddings
,
ProjectUMAP
. Note that by default, the weight.reduction
parameter for all functions will be set to the dimension reduction method
used in the FindTransferAnchors
function call used to construct
the anchor object, and the dims
parameter will be the same dimensions
used to find anchors.
MapQuery( anchorset, query, reference, refdata = NULL, new.reduction.name = NULL, reference.reduction = NULL, reference.dims = NULL, query.dims = NULL, reduction.model = NULL, transferdata.args = list(), integrateembeddings.args = list(), projectumap.args = list(), verbose = TRUE )
MapQuery( anchorset, query, reference, refdata = NULL, new.reduction.name = NULL, reference.reduction = NULL, reference.dims = NULL, query.dims = NULL, reduction.model = NULL, transferdata.args = list(), integrateembeddings.args = list(), projectumap.args = list(), verbose = TRUE )
anchorset |
An AnchorSet object |
query |
Query object used in anchorset construction |
reference |
Reference object used in anchorset construction |
refdata |
Data to transfer. This can be specified in one of two ways:
|
new.reduction.name |
Name for new integrated dimensional reduction. |
reference.reduction |
Name of reduction to use from the reference for neighbor finding |
reference.dims |
Dimensions (columns) to use from reference |
query.dims |
Dimensions (columns) to use from query |
reduction.model |
|
transferdata.args |
A named list of additional arguments to
|
integrateembeddings.args |
A named list of additional arguments to
|
projectumap.args |
A named list of additional arguments to
|
verbose |
Print progress bars and output |
Returns a modified query Seurat object containing:
New Assays corresponding to the features transferred and/or their
corresponding prediction scores from TransferData
An integrated reduction from IntegrateEmbeddings
A projected UMAP reduction of the query cells projected into the
reference UMAP using ProjectUMAP
Merge SCTAssay objects
## S3 method for class 'SCTAssay' merge( x = NULL, y = NULL, add.cell.ids = NULL, merge.data = TRUE, na.rm = TRUE, ... )
## S3 method for class 'SCTAssay' merge( x = NULL, y = NULL, add.cell.ids = NULL, merge.data = TRUE, na.rm = TRUE, ... )
x |
A |
y |
A single |
add.cell.ids |
A character vector of |
merge.data |
Merge the data slots instead of just merging the counts (which requires renormalization); this is recommended if the same normalization approach was applied to all objects |
na.rm |
If na.rm = TRUE, this will only preserve residuals that are present in all SCTAssays being merged. Otherwise, missing residuals will be populated with NAs. |
... |
Arguments passed to other methods |
Calculates relative contribution of each feature to each cell for given set of features.
MetaFeature( object, features, meta.name = "metafeature", cells = NULL, assay = NULL, slot = "data" )
MetaFeature( object, features, meta.name = "metafeature", cells = NULL, assay = NULL, slot = "data" )
object |
A Seurat object |
features |
List of features to aggregate |
meta.name |
Name of column in metadata to store metafeature |
cells |
List of cells to use (default all cells) |
assay |
Which assay to use |
slot |
Which slot to take data from (default data) |
Returns a Seurat
object with metafeature stored in objct metadata
data("pbmc_small") pbmc_small <- MetaFeature( object = pbmc_small, features = c("LTB", "EAF2"), meta.name = 'var.aggregate' ) head(pbmc_small[[]])
data("pbmc_small") pbmc_small <- MetaFeature( object = pbmc_small, features = c("LTB", "EAF2"), meta.name = 'var.aggregate' ) head(pbmc_small[[]])
Apply a ceiling and floor to all values in a matrix
MinMax(data, min, max)
MinMax(data, min, max)
data |
Matrix or data frame |
min |
all values below this min value will be replaced with min |
max |
all values above this max value will be replaced with max |
Returns matrix after performing these floor and ceil operations
mat <- matrix(data = rbinom(n = 25, size = 20, prob = 0.2 ), nrow = 5) mat MinMax(data = mat, min = 4, max = 5)
mat <- matrix(data = rbinom(n = 25, size = 20, prob = 0.2 ), nrow = 5) mat MinMax(data = mat, min = 4, max = 5)
Here we compute a measure of how well mixed a composite dataset is. To compute, we first examine the local neighborhood for each cell (looking at max.k neighbors) and determine for each group (could be the dataset after integration) the k nearest neighbor and what rank that neighbor was in the overall neighborhood. We then take the median across all groups as the mixing metric per cell.
MixingMetric( object, grouping.var, reduction = "pca", dims = 1:2, k = 5, max.k = 300, eps = 0, verbose = TRUE )
MixingMetric( object, grouping.var, reduction = "pca", dims = 1:2, k = 5, max.k = 300, eps = 0, verbose = TRUE )
object |
Seurat object |
grouping.var |
Grouping variable for dataset |
reduction |
Which dimensionally reduced space to use |
dims |
Dimensions to use |
k |
Neighbor number to examine per group |
max.k |
Maximum size of local neighborhood to compute |
eps |
Error bound on the neighbor finding algorithm (from RANN) |
verbose |
Displays progress bar |
Returns a vector of values of the mixing metric for each cell
Draws a heatmap of single cell feature expression with cells ordered by their mixscape ko probabilities.
MixscapeHeatmap( object, ident.1 = NULL, ident.2 = NULL, balanced = TRUE, logfc.threshold = 0.25, assay = "RNA", max.genes = 100, test.use = "wilcox", max.cells.group = NULL, order.by.prob = TRUE, group.by = NULL, mixscape.class = "mixscape_class", prtb.type = "KO", fc.name = "avg_log2FC", pval.cutoff = 0.05, ... )
MixscapeHeatmap( object, ident.1 = NULL, ident.2 = NULL, balanced = TRUE, logfc.threshold = 0.25, assay = "RNA", max.genes = 100, test.use = "wilcox", max.cells.group = NULL, order.by.prob = TRUE, group.by = NULL, mixscape.class = "mixscape_class", prtb.type = "KO", fc.name = "avg_log2FC", pval.cutoff = 0.05, ... )
object |
An object |
ident.1 |
Identity class to define markers for; pass an object of class
|
ident.2 |
A second identity class for comparison; if |
balanced |
Plot an equal number of genes with both groups of cells. |
logfc.threshold |
Limit testing to genes which show, on average, at least X-fold difference (log-scale) between the two groups of cells. Default is 0.25 Increasing logfc.threshold speeds up the function, but can miss weaker signals. |
assay |
Assay to use in differential expression testing |
max.genes |
Total number of DE genes to plot. |
test.use |
Denotes which test to use. Available options are:
|
max.cells.group |
Number of cells per identity to plot. |
order.by.prob |
Order cells on heatmap based on their mixscape knockout probability from highest to lowest score. |
group.by |
(Deprecated) Option to split densities based on mixscape classification. Please use mixscape.class instead |
mixscape.class |
metadata column with mixscape classifications. |
prtb.type |
specify type of CRISPR perturbation expected for labeling mixscape classifications. Default is KO. |
fc.name |
Name of the fold change, average difference, or custom function column in the output data.frame. Default is avg_log2FC |
pval.cutoff |
P-value cut-off for selection of significantly DE genes. |
... |
Arguments passed to other methods and to specific DE methods |
A ggplot object.
This function performs unsupervised PCA on each mixscape class separately and projects each subspace onto all cells in the data. Finally, it uses the first 10 principle components from each projection as input to lda in MASS package together with mixscape class labels.
MixscapeLDA( object, assay = NULL, ndims.print = 1:5, nfeatures.print = 30, reduction.key = "LDA_", seed = 42, pc.assay = "PRTB", labels = "gene", nt.label = "NT", npcs = 10, verbose = TRUE, logfc.threshold = 0.25 )
MixscapeLDA( object, assay = NULL, ndims.print = 1:5, nfeatures.print = 30, reduction.key = "LDA_", seed = 42, pc.assay = "PRTB", labels = "gene", nt.label = "NT", npcs = 10, verbose = TRUE, logfc.threshold = 0.25 )
object |
An object of class Seurat. |
assay |
Assay to use for performing Linear Discriminant Analysis (LDA). |
ndims.print |
Number of LDA dimensions to print. |
nfeatures.print |
Number of features to print for each LDA component. |
reduction.key |
Reduction key name. |
seed |
Value for random seed |
pc.assay |
Assay to use for running Principle components analysis. |
labels |
Meta data column with target gene class labels. |
nt.label |
Name of non-targeting cell class. |
npcs |
Number of principle components to use. |
verbose |
Print progress bar. |
logfc.threshold |
Limit testing to genes which show, on average, at least X-fold difference (log-scale) between the two groups of cells. Default is 0.25 Increasing logfc.threshold speeds up the function, but can miss weaker signals. |
Returns a Seurat object with LDA added in the reduction slot.
The ModalityWeights class is an intermediate data storage class that stores the modality weight and other
related information needed for performing downstream analyses - namely data integration
(FindModalityWeights
) and data transfer (FindMultiModalNeighbors
).
modality.weight.list
A list of modality weights value from all modalities
modality.assay
Names of assays for the list of dimensional reductions
params
A list of parameters used in the FindModalityWeights
score.matrix
a list of score matrices representing cross and within-modality prediction score, and kernel value
command
Store log of parameters that were used
Identify singlets, doublets and negative cells from multiplexing experiments. Annotate singlets by tags.
MULTIseqDemux( object, assay = "HTO", quantile = 0.7, autoThresh = FALSE, maxiter = 5, qrange = seq(from = 0.1, to = 0.9, by = 0.05), verbose = TRUE )
MULTIseqDemux( object, assay = "HTO", quantile = 0.7, autoThresh = FALSE, maxiter = 5, qrange = seq(from = 0.1, to = 0.9, by = 0.05), verbose = TRUE )
object |
Seurat object. Assumes that the specified assay data has been added |
assay |
Name of the multiplexing assay (HTO by default) |
quantile |
The quantile to use for classification |
autoThresh |
Whether to perform automated threshold finding to define the best quantile. Default is FALSE |
maxiter |
Maximum number of iterations if autoThresh = TRUE. Default is 5 |
qrange |
A range of possible quantile values to try if autoThresh = TRUE |
verbose |
Prints the output |
A Seurat object with demultiplexing results stored at object$MULTI_ID
https://www.biorxiv.org/content/10.1101/387241v1
## Not run: object <- MULTIseqDemux(object) ## End(Not run)
## Not run: object <- MULTIseqDemux(object) ## End(Not run)
For more details, please see the documentation in
SeuratObject
It will color the query cells and the neighbors of the query cells in the DimPlot
NNPlot( object, reduction, nn.idx, query.cells, dims = 1:2, label = FALSE, label.size = 4, repel = FALSE, sizes.highlight = 2, pt.size = 1, cols.highlight = c("#377eb8", "#e41a1c"), na.value = "#bdbdbd", order = c("self", "neighbors", "other"), show.all.cells = TRUE, ... )
NNPlot( object, reduction, nn.idx, query.cells, dims = 1:2, label = FALSE, label.size = 4, repel = FALSE, sizes.highlight = 2, pt.size = 1, cols.highlight = c("#377eb8", "#e41a1c"), na.value = "#bdbdbd", order = c("self", "neighbors", "other"), show.all.cells = TRUE, ... )
object |
Seurat object |
reduction |
Which dimensionality reduction to use. If not specified, first searches for umap, then tsne, then pca |
nn.idx |
the neighbor index of all cells |
query.cells |
cells used to find their neighbors |
dims |
Dimensions to plot, must be a two-length numeric vector specifying x- and y-dimensions |
label |
Whether to label the clusters |
label.size |
Sets size of labels |
repel |
Repel labels |
sizes.highlight |
Size of highlighted cells; will repeat to the length groups in cells.highlight |
pt.size |
Adjust point size for plotting |
cols.highlight |
A vector of colors to highlight the cells as; will repeat to the length groups in cells.highlight |
na.value |
Color value for NA points when using custom scale |
order |
Specify the order of plotting for the idents. This can be useful for crowded plots if points of interest are being buried. Provide either a full list of valid idents or a subset to be plotted last (on top) |
show.all.cells |
Show all cells or only query and neighbor cells |
... |
Extra parameters passed to |
A patchworked
ggplot object if
combine = TRUE
; otherwise, a list of ggplot objects
Normalize the count data present in a given assay.
NormalizeData(object, ...) ## Default S3 method: NormalizeData( object, normalization.method = "LogNormalize", scale.factor = 10000, margin = 1, block.size = NULL, verbose = TRUE, ... ) ## S3 method for class 'Assay' NormalizeData( object, normalization.method = "LogNormalize", scale.factor = 10000, margin = 1, verbose = TRUE, ... ) ## S3 method for class 'Seurat' NormalizeData( object, assay = NULL, normalization.method = "LogNormalize", scale.factor = 10000, margin = 1, verbose = TRUE, ... )
NormalizeData(object, ...) ## Default S3 method: NormalizeData( object, normalization.method = "LogNormalize", scale.factor = 10000, margin = 1, block.size = NULL, verbose = TRUE, ... ) ## S3 method for class 'Assay' NormalizeData( object, normalization.method = "LogNormalize", scale.factor = 10000, margin = 1, verbose = TRUE, ... ) ## S3 method for class 'Seurat' NormalizeData( object, assay = NULL, normalization.method = "LogNormalize", scale.factor = 10000, margin = 1, verbose = TRUE, ... )
object |
An object |
... |
Arguments passed to other methods |
normalization.method |
Method for normalization.
|
scale.factor |
Sets the scale factor for cell-level normalization |
margin |
If performing CLR normalization, normalize across features (1) or cells (2) |
block.size |
How many cells should be run in each chunk, will try to split evenly across threads |
verbose |
display progress bar for normalization procedure |
assay |
Name of assay to use |
Returns object after normalization
## Not run: data("pbmc_small") pbmc_small pmbc_small <- NormalizeData(object = pbmc_small) ## End(Not run)
## Not run: data("pbmc_small") pbmc_small pmbc_small <- NormalizeData(object = pbmc_small) ## End(Not run)
Returns a set of genes, based on the JackStraw analysis, that have statistically significant associations with a set of PCs.
PCASigGenes( object, pcs.use, pval.cut = 0.1, use.full = FALSE, max.per.pc = NULL )
PCASigGenes( object, pcs.use, pval.cut = 0.1, use.full = FALSE, max.per.pc = NULL )
object |
Seurat object |
pcs.use |
PCS to use. |
pval.cut |
P-value cutoff |
use.full |
Use the full list of genes (from the projected PCA). Assumes
that |
max.per.pc |
Maximum number of genes to return per PC. Used to avoid genes from one PC dominating the entire analysis. |
A vector of genes whose p-values are statistically significant for at least one of the given PCs.
data("pbmc_small") PCASigGenes(pbmc_small, pcs.use = 1:2)
data("pbmc_small") PCASigGenes(pbmc_small, pcs.use = 1:2)
Calculate the percentage of a vector above some threshold
PercentAbove(x, threshold)
PercentAbove(x, threshold)
x |
Vector of values |
threshold |
Threshold to use when calculating percentage |
Returns the percentage of x
values above the given threshold
set.seed(42) PercentAbove(sample(1:100, 10), 75)
set.seed(42) PercentAbove(sample(1:100, 10), 75)
This function enables you to easily calculate the percentage of all the counts belonging to a subset of the possible features for each cell. This is useful when trying to compute the percentage of transcripts that map to mitochondrial genes for example. The calculation here is simply the column sum of the matrix present in the counts slot for features belonging to the set divided by the column sum for all features times 100.
PercentageFeatureSet( object, pattern = NULL, features = NULL, col.name = NULL, assay = NULL )
PercentageFeatureSet( object, pattern = NULL, features = NULL, col.name = NULL, assay = NULL )
object |
A Seurat object |
pattern |
A regex pattern to match features against |
features |
A defined feature set. If features provided, will ignore the pattern matching |
col.name |
Name in meta.data column to assign. If this is not null, returns a Seurat object with the proportion of the feature set stored in metadata. |
assay |
Assay to use |
Returns a vector with the proportion of the feature set or if md.name is set, returns a Seurat object with the proportion of the feature set stored in metadata.
data("pbmc_small") # Calculate the proportion of transcripts mapping to mitochondrial genes # NOTE: The pattern provided works for human gene names. You may need to adjust depending on your # system of interest pbmc_small[["percent.mt"]] <- PercentageFeatureSet(object = pbmc_small, pattern = "^MT-")
data("pbmc_small") # Calculate the proportion of transcripts mapping to mitochondrial genes # NOTE: The pattern provided works for human gene names. You may need to adjust depending on your # system of interest pbmc_small[["percent.mt"]] <- PercentageFeatureSet(object = pbmc_small, pattern = "^MT-")
Plots previously computed tree (from BuildClusterTree)
PlotClusterTree(object, direction = "downwards", ...)
PlotClusterTree(object, direction = "downwards", ...)
object |
Seurat object |
direction |
A character string specifying the direction of the tree (default is downwards) Possible options: "rightwards", "leftwards", "upwards", and "downwards". |
... |
Additional arguments to
|
Plots dendogram (must be precomputed using BuildClusterTree), returns no value
if (requireNamespace("ape", quietly = TRUE)) { data("pbmc_small") pbmc_small <- BuildClusterTree(object = pbmc_small) PlotClusterTree(object = pbmc_small) }
if (requireNamespace("ape", quietly = TRUE)) { data("pbmc_small") pbmc_small <- BuildClusterTree(object = pbmc_small) PlotClusterTree(object = pbmc_small) }
Density plots to visualize perturbation scores calculated from RunMixscape function.
PlotPerturbScore( object, target.gene.class = "gene", target.gene.ident = NULL, mixscape.class = "mixscape_class", col = "orange2", split.by = NULL, before.mixscape = FALSE, prtb.type = "KO" )
PlotPerturbScore( object, target.gene.class = "gene", target.gene.ident = NULL, mixscape.class = "mixscape_class", col = "orange2", split.by = NULL, before.mixscape = FALSE, prtb.type = "KO" )
object |
An object of class Seurat. |
target.gene.class |
meta data column specifying all target gene names in the experiment. |
target.gene.ident |
Target gene name to visualize perturbation scores for. |
mixscape.class |
meta data column specifying mixscape classifications. |
col |
Specify color of target gene class or knockout cell class. For control non-targeting and non-perturbed cells, colors are set to different shades of grey. |
split.by |
For datasets with more than one cell type. Set equal TRUE to visualize perturbation scores for each cell type separately. |
before.mixscape |
Option to split densities based on mixscape classification (default) or original target gene classification. Default is set to NULL and plots cells by original class ID. |
prtb.type |
specify type of CRISPR perturbation expected for labeling mixscape classifications. Default is KO. |
A ggplot object.
Plot cells as polygons, rather than single points. Color cells by identity, or a categorical variable in metadata
PolyDimPlot( object, group.by = NULL, cells = NULL, poly.data = "spatial", flip.coords = FALSE )
PolyDimPlot( object, group.by = NULL, cells = NULL, poly.data = "spatial", flip.coords = FALSE )
object |
Seurat object |
group.by |
A grouping variable present in the metadata. Default is to use the groupings present
in the current cell identities ( |
cells |
Vector of cells to plot (default is all cells) |
poly.data |
Name of the polygon dataframe in the misc slot |
flip.coords |
Flip x and y coordinates |
Returns a ggplot object
Plot cells as polygons, rather than single points. Color cells by any value
accessible by FetchData
.
PolyFeaturePlot( object, features, cells = NULL, poly.data = "spatial", ncol = ceiling(x = length(x = features)/2), min.cutoff = 0, max.cutoff = NA, common.scale = TRUE, flip.coords = FALSE )
PolyFeaturePlot( object, features, cells = NULL, poly.data = "spatial", ncol = ceiling(x = length(x = features)/2), min.cutoff = 0, max.cutoff = NA, common.scale = TRUE, flip.coords = FALSE )
object |
Seurat object |
features |
Vector of features to plot. Features can come from:
|
cells |
Vector of cells to plot (default is all cells) |
poly.data |
Name of the polygon dataframe in the misc slot |
ncol |
Number of columns to split the plot into |
min.cutoff , max.cutoff
|
Vector of minimum and maximum cutoff values for each feature, may specify quantile in the form of 'q##' where '##' is the quantile (eg, 'q1', 'q10') |
common.scale |
... |
flip.coords |
Flip x and y coordinates |
Returns a ggplot object
This function will predict expression or cell embeddings from its k nearest neighbors index. For each cell, it will average its k neighbors value to get its new imputed value. It can average expression value in assays and cell embeddings from dimensional reductions.
PredictAssay( object, nn.idx, assay, reduction = NULL, dims = NULL, return.assay = TRUE, slot = "scale.data", features = NULL, mean.function = rowMeans, seed = 4273, verbose = TRUE )
PredictAssay( object, nn.idx, assay, reduction = NULL, dims = NULL, return.assay = TRUE, slot = "scale.data", features = NULL, mean.function = rowMeans, seed = 4273, verbose = TRUE )
object |
The object used to calculate knn |
nn.idx |
k near neighbour indices. A cells x k matrix. |
assay |
Assay used for prediction |
reduction |
Cell embedding of the reduction used for prediction |
dims |
Number of dimensions of cell embedding |
return.assay |
Return an assay or a predicted matrix |
slot |
slot used for prediction |
features |
features used for prediction |
mean.function |
the function used to calculate row mean |
seed |
Sets the random seed to check if the nearest neighbor is query cell |
verbose |
Print progress |
return an assay containing predicted expression value in the data slot
This function performs unsupervised PCA on each mixscape class separately and projects each subspace onto all cells in the data.
PrepLDA( object, de.assay = "RNA", pc.assay = "PRTB", labels = "gene", nt.label = "NT", npcs = 10, verbose = TRUE, logfc.threshold = 0.25 )
PrepLDA( object, de.assay = "RNA", pc.assay = "PRTB", labels = "gene", nt.label = "NT", npcs = 10, verbose = TRUE, logfc.threshold = 0.25 )
object |
An object of class Seurat. |
de.assay |
Assay to use for selection of DE genes. |
pc.assay |
Assay to use for running Principle components analysis. |
labels |
Meta data column with target gene class labels. |
nt.label |
Name of non-targeting cell class. |
npcs |
Number of principle components to use. |
verbose |
Print progress bar. |
logfc.threshold |
Limit testing to genes which show, on average, at least X-fold difference (log-scale) between the two groups of cells. Default is 0.25 Increasing logfc.threshold speeds up the function, but can miss weaker signals. |
Returns a list of the first 10 PCs from each projection.
Given a merged object with multiple SCT models, this function uses minimum of the median UMI (calculated using the raw UMI counts) of individual objects to reverse the individual SCT regression model using minimum of median UMI as the sequencing depth covariate. The counts slot of the SCT assay is replaced with recorrected counts and the data slot is replaced with log1p of recorrected counts.
PrepSCTFindMarkers(object, assay = "SCT", verbose = TRUE)
PrepSCTFindMarkers(object, assay = "SCT", verbose = TRUE)
object |
Seurat object with SCT assays |
assay |
Assay name where for SCT objects are stored; Default is 'SCT' |
verbose |
Print messages and progress |
Returns a Seurat object with recorrected counts and data in the SCT assay.
This function uses
progressr to
render status updates and progress bars. To enable progress updates, wrap
the function call in with_progress
or run
handlers(global = TRUE)
before running
this function. For more details about progressr, please read
vignette("progressr-intro")
This function uses
future to enable
parallelization. Parallelization strategies can be set using
plan
. Common plans include “sequential
”
for non-parallelized processing or “multisession
” for parallel
evaluation using multiple R sessions; for other plans, see the
“Implemented evaluation strategies” section of
?future::plan
. For a more thorough introduction
to future, see
vignette("future-1-overview")
data("pbmc_small") pbmc_small1 <- SCTransform(object = pbmc_small, variable.features.n = 20) pbmc_small2 <- SCTransform(object = pbmc_small, variable.features.n = 20) pbmc_merged <- merge(x = pbmc_small1, y = pbmc_small2) pbmc_merged <- PrepSCTFindMarkers(object = pbmc_merged) markers <- FindMarkers( object = pbmc_merged, ident.1 = "0", ident.2 = "1", assay = "SCT" ) pbmc_subset <- subset(pbmc_merged, idents = c("0", "1")) markers_subset <- FindMarkers( object = pbmc_subset, ident.1 = "0", ident.2 = "1", assay = "SCT", recorrect_umi = FALSE )
data("pbmc_small") pbmc_small1 <- SCTransform(object = pbmc_small, variable.features.n = 20) pbmc_small2 <- SCTransform(object = pbmc_small, variable.features.n = 20) pbmc_merged <- merge(x = pbmc_small1, y = pbmc_small2) pbmc_merged <- PrepSCTFindMarkers(object = pbmc_merged) markers <- FindMarkers( object = pbmc_merged, ident.1 = "0", ident.2 = "1", assay = "SCT" ) pbmc_subset <- subset(pbmc_merged, idents = c("0", "1")) markers_subset <- FindMarkers( object = pbmc_subset, ident.1 = "0", ident.2 = "1", assay = "SCT", recorrect_umi = FALSE )
This function takes in a list of objects that have been normalized with the
SCTransform
method and performs the following steps:
If anchor.features is a numeric value, calls SelectIntegrationFeatures
to determine the features to use in the downstream integration procedure.
Ensures that the sctransform residuals for the features specified
to anchor.features are present in each object in the list. This is
necessary because the default behavior of SCTransform
is to
only store the residuals for the features determined to be variable.
Residuals are recomputed for missing features using the stored model
parameters via the GetResidual
function.
Subsets the scale.data
slot to only contain the residuals for
anchor.features for efficiency in downstream processing.
PrepSCTIntegration( object.list, assay = NULL, anchor.features = 2000, sct.clip.range = NULL, verbose = TRUE )
PrepSCTIntegration( object.list, assay = NULL, anchor.features = 2000, sct.clip.range = NULL, verbose = TRUE )
object.list |
A list of |
assay |
The name of the |
anchor.features |
Can be either:
|
sct.clip.range |
Numeric of length two specifying the min and max values the Pearson residual will be clipped to |
verbose |
Display output/messages |
A list of Seurat
objects with the appropriate scale.data
slots
containing only the required anchor.features
.
## Not run: # to install the SeuratData package see https://github.com/satijalab/seurat-data library(SeuratData) data("panc8") # panc8 is a merged Seurat object containing 8 separate pancreas datasets # split the object by dataset and take the first 2 to integrate pancreas.list <- SplitObject(panc8, split.by = "tech")[1:2] # perform SCTransform normalization pancreas.list <- lapply(X = pancreas.list, FUN = SCTransform) # select integration features and prep step features <- SelectIntegrationFeatures(pancreas.list) pancreas.list <- PrepSCTIntegration( pancreas.list, anchor.features = features ) # downstream integration steps anchors <- FindIntegrationAnchors( pancreas.list, normalization.method = "SCT", anchor.features = features ) pancreas.integrated <- IntegrateData(anchors, normalization.method = "SCT") ## End(Not run)
## Not run: # to install the SeuratData package see https://github.com/satijalab/seurat-data library(SeuratData) data("panc8") # panc8 is a merged Seurat object containing 8 separate pancreas datasets # split the object by dataset and take the first 2 to integrate pancreas.list <- SplitObject(panc8, split.by = "tech")[1:2] # perform SCTransform normalization pancreas.list <- lapply(X = pancreas.list, FUN = SCTransform) # select integration features and prep step features <- SelectIntegrationFeatures(pancreas.list) pancreas.list <- PrepSCTIntegration( pancreas.list, anchor.features = features ) # downstream integration steps anchors <- FindIntegrationAnchors( pancreas.list, normalization.method = "SCT", anchor.features = features ) pancreas.integrated <- IntegrateData(anchors, normalization.method = "SCT") ## End(Not run)
Takes a pre-computed dimensional reduction (typically calculated on a subset of genes) and projects this onto the entire dataset (all genes). Note that the cell loadings will remain unchanged, but now there are gene loadings for all genes.
ProjectDim( object, reduction = "pca", assay = NULL, dims.print = 1:5, nfeatures.print = 20, overwrite = FALSE, do.center = FALSE, verbose = TRUE )
ProjectDim( object, reduction = "pca", assay = NULL, dims.print = 1:5, nfeatures.print = 20, overwrite = FALSE, do.center = FALSE, verbose = TRUE )
object |
Seurat object |
reduction |
Reduction to use |
assay |
Assay to use |
dims.print |
Number of dims to print features for |
nfeatures.print |
Number of features with highest/lowest loadings to print for each dimension |
overwrite |
Replace the existing data in feature.loadings |
do.center |
Center the dataset prior to projection (should be set to TRUE) |
verbose |
Print top genes associated with the projected dimensions |
Returns Seurat object with the projected values
data("pbmc_small") pbmc_small pbmc_small <- ProjectDim(object = pbmc_small, reduction = "pca") # Vizualize top projected genes in heatmap DimHeatmap(object = pbmc_small, reduction = "pca", dims = 1, balanced = TRUE)
data("pbmc_small") pbmc_small pbmc_small <- ProjectDim(object = pbmc_small, reduction = "pca") # Vizualize top projected genes in heatmap DimHeatmap(object = pbmc_small, reduction = "pca", dims = 1, balanced = TRUE)
This function will take a query dataset and project it into the coordinates of a provided reference UMAP. This is essentially a wrapper around two steps:
FindNeighbors - Find the nearest reference cell neighbors and their distances for each query cell.
RunUMAP - Perform umap projection by providing the neighbor set calculated above and the umap model previously computed in the reference.
ProjectUMAP(query, ...) ## Default S3 method: ProjectUMAP( query, query.dims = NULL, reference, reference.dims = NULL, k.param = 30, nn.method = "annoy", n.trees = 50, annoy.metric = "cosine", l2.norm = FALSE, cache.index = TRUE, index = NULL, neighbor.name = "query_ref.nn", reduction.model, ... ) ## S3 method for class 'DimReduc' ProjectUMAP( query, query.dims = NULL, reference, reference.dims = NULL, k.param = 30, nn.method = "annoy", n.trees = 50, annoy.metric = "cosine", l2.norm = FALSE, cache.index = TRUE, index = NULL, neighbor.name = "query_ref.nn", reduction.model, ... ) ## S3 method for class 'Seurat' ProjectUMAP( query, query.reduction, query.dims = NULL, reference, reference.reduction, reference.dims = NULL, k.param = 30, nn.method = "annoy", n.trees = 50, annoy.metric = "cosine", l2.norm = FALSE, cache.index = TRUE, index = NULL, neighbor.name = "query_ref.nn", reduction.model, reduction.name = "ref.umap", reduction.key = "refUMAP_", ... )
ProjectUMAP(query, ...) ## Default S3 method: ProjectUMAP( query, query.dims = NULL, reference, reference.dims = NULL, k.param = 30, nn.method = "annoy", n.trees = 50, annoy.metric = "cosine", l2.norm = FALSE, cache.index = TRUE, index = NULL, neighbor.name = "query_ref.nn", reduction.model, ... ) ## S3 method for class 'DimReduc' ProjectUMAP( query, query.dims = NULL, reference, reference.dims = NULL, k.param = 30, nn.method = "annoy", n.trees = 50, annoy.metric = "cosine", l2.norm = FALSE, cache.index = TRUE, index = NULL, neighbor.name = "query_ref.nn", reduction.model, ... ) ## S3 method for class 'Seurat' ProjectUMAP( query, query.reduction, query.dims = NULL, reference, reference.reduction, reference.dims = NULL, k.param = 30, nn.method = "annoy", n.trees = 50, annoy.metric = "cosine", l2.norm = FALSE, cache.index = TRUE, index = NULL, neighbor.name = "query_ref.nn", reduction.model, reduction.name = "ref.umap", reduction.key = "refUMAP_", ... )
query |
Query dataset |
... |
Additional parameters to |
query.dims |
Dimensions (columns) to use from query |
reference |
Reference dataset |
reference.dims |
Dimensions (columns) to use from reference |
k.param |
Defines k for the k-nearest neighbor algorithm |
nn.method |
Method for nearest neighbor finding. Options include: rann, annoy |
n.trees |
More trees gives higher precision when using annoy approximate nearest neighbor search |
annoy.metric |
Distance metric for annoy. Options include: euclidean, cosine, manhattan, and hamming |
l2.norm |
Take L2Norm of the data |
cache.index |
Include cached index in returned Neighbor object (only relevant if return.neighbor = TRUE) |
index |
Precomputed index. Useful if querying new data against existing index to avoid recomputing. |
neighbor.name |
Name to store neighbor information in the query |
reduction.model |
|
query.reduction |
Name of reduction to use from the query for neighbor finding |
reference.reduction |
Name of reduction to use from the reference for neighbor finding |
reduction.name |
Name of projected UMAP to store in the query |
reduction.key |
Value for the projected UMAP key |
Get Spot Radius
## S3 method for class 'SlideSeq' Radius(object) ## S3 method for class 'STARmap' Radius(object) ## S3 method for class 'VisiumV1' Radius(object)
## S3 method for class 'SlideSeq' Radius(object) ## S3 method for class 'STARmap' Radius(object) ## S3 method for class 'VisiumV1' Radius(object)
object |
An image object |
Enables easy loading of sparse data matrices provided by 10X genomics.
Read10X( data.dir, gene.column = 2, cell.column = 1, unique.features = TRUE, strip.suffix = FALSE )
Read10X( data.dir, gene.column = 2, cell.column = 1, unique.features = TRUE, strip.suffix = FALSE )
data.dir |
Directory containing the matrix.mtx, genes.tsv (or features.tsv), and barcodes.tsv files provided by 10X. A vector or named vector can be given in order to load several data directories. If a named vector is given, the cell barcode names will be prefixed with the name. |
gene.column |
Specify which column of genes.tsv or features.tsv to use for gene names; default is 2 |
cell.column |
Specify which column of barcodes.tsv to use for cell names; default is 1 |
unique.features |
Make feature names unique (default TRUE) |
strip.suffix |
Remove trailing "-1" if present in all cell barcodes. |
If features.csv indicates the data has multiple data types, a list containing a sparse matrix of the data from each type will be returned. Otherwise a sparse matrix containing the expression data will be returned.
## Not run: # For output from CellRanger < 3.0 data_dir <- 'path/to/data/directory' list.files(data_dir) # Should show barcodes.tsv, genes.tsv, and matrix.mtx expression_matrix <- Read10X(data.dir = data_dir) seurat_object = CreateSeuratObject(counts = expression_matrix) # For output from CellRanger >= 3.0 with multiple data types data_dir <- 'path/to/data/directory' list.files(data_dir) # Should show barcodes.tsv.gz, features.tsv.gz, and matrix.mtx.gz data <- Read10X(data.dir = data_dir) seurat_object = CreateSeuratObject(counts = data$`Gene Expression`) seurat_object[['Protein']] = CreateAssayObject(counts = data$`Antibody Capture`) ## End(Not run)
## Not run: # For output from CellRanger < 3.0 data_dir <- 'path/to/data/directory' list.files(data_dir) # Should show barcodes.tsv, genes.tsv, and matrix.mtx expression_matrix <- Read10X(data.dir = data_dir) seurat_object = CreateSeuratObject(counts = expression_matrix) # For output from CellRanger >= 3.0 with multiple data types data_dir <- 'path/to/data/directory' list.files(data_dir) # Should show barcodes.tsv.gz, features.tsv.gz, and matrix.mtx.gz data <- Read10X(data.dir = data_dir) seurat_object = CreateSeuratObject(counts = data$`Gene Expression`) seurat_object[['Protein']] = CreateAssayObject(counts = data$`Antibody Capture`) ## End(Not run)
Read count matrix from 10X CellRanger hdf5 file. This can be used to read both scATAC-seq and scRNA-seq matrices.
Read10X_h5(filename, use.names = TRUE, unique.features = TRUE)
Read10X_h5(filename, use.names = TRUE, unique.features = TRUE)
filename |
Path to h5 file |
use.names |
Label row names with feature names rather than ID numbers. |
unique.features |
Make feature names unique (default TRUE) |
Returns a sparse matrix with rows and columns labeled. If multiple genomes are present, returns a list of sparse matrices (one per genome).
Load a 10X Genomics Visium Image
Read10X_Image( image.dir, image.name = "tissue_lowres_image.png", filter.matrix = TRUE, ... )
Read10X_Image( image.dir, image.name = "tissue_lowres_image.png", filter.matrix = TRUE, ... )
image.dir |
Path to directory with 10X Genomics visium image data;
should include files |
image.name |
The file name of the image. Defaults to tissue_lowres_image.png.
|
filter.matrix |
Filter spot/feature matrix to only include spots that have been determined to be over tissue. |
... |
Ignored for now |
A VisiumV1
object
This function reads the probe metadata from a 10x Genomics probe barcode matrix file in HDF5 format.
Read10X_probe_metadata(data.dir, filename = "raw_probe_bc_matrix.h5")
Read10X_probe_metadata(data.dir, filename = "raw_probe_bc_matrix.h5")
data.dir |
The directory where the file is located. |
filename |
The name of the file containing the raw probe barcode matrix in HDF5 format. The default filename is 'raw_probe_bc_matrix.h5'. |
Returns a data.frame containing the probe metadata.
Read and Load Akoya CODEX data
ReadAkoya( filename, type = c("inform", "processor", "qupath"), filter = "DAPI|Blank|Empty", inform.quant = c("mean", "total", "min", "max", "std") ) LoadAkoya( filename, type = c("inform", "processor", "qupath"), fov, assay = "Akoya", ... )
ReadAkoya( filename, type = c("inform", "processor", "qupath"), filter = "DAPI|Blank|Empty", inform.quant = c("mean", "total", "min", "max", "std") ) LoadAkoya( filename, type = c("inform", "processor", "qupath"), fov, assay = "Akoya", ... )
filename |
Path to matrix generated by upstream processing. |
type |
Specify which type matrix is being provided.
|
filter |
A pattern to filter features by; pass |
inform.quant |
When |
fov |
Name to store FOV as |
assay |
Name to store expression matrix as |
... |
Arguments passed on to |
ReadAkoya
: A list with some combination of the following values
“matrix
”: a
sparse matrix with expression data; cells
are columns and features are rows
“centroids
”: a data frame with cell centroid
coordinates in three columns: “x”, “y”, and “cell”
“metadata
”: a data frame with cell-level meta data;
includes all columns in filename
that aren't in
“matrix
” or “centroids
”
When type
is “inform
”, additional expression matrices
are returned and named using their segmentation type (eg.
“nucleus”, “membrane”). The “Entire Cell” segmentation
type is returned in the “matrix
” entry of the list
LoadAkoya
: A Seurat
object
This function uses
progressr to
render status updates and progress bars. To enable progress updates, wrap
the function call in with_progress
or run
handlers(global = TRUE)
before running
this function. For more details about progressr, please read
vignette("progressr-intro")
This function requires the data.table package to be installed
Enables easy loading of sparse data matrices
ReadMtx( mtx, cells, features, cell.column = 1, feature.column = 2, cell.sep = "\t", feature.sep = "\t", skip.cell = 0, skip.feature = 0, mtx.transpose = FALSE, unique.features = TRUE, strip.suffix = FALSE )
ReadMtx( mtx, cells, features, cell.column = 1, feature.column = 2, cell.sep = "\t", feature.sep = "\t", skip.cell = 0, skip.feature = 0, mtx.transpose = FALSE, unique.features = TRUE, strip.suffix = FALSE )
mtx |
Name or remote URL of the mtx file |
cells |
Name or remote URL of the cells/barcodes file |
features |
Name or remote URL of the features/genes file |
cell.column |
Specify which column of cells file to use for cell names; default is 1 |
feature.column |
Specify which column of features files to use for feature/gene names; default is 2 |
cell.sep |
Specify the delimiter in the cell name file |
feature.sep |
Specify the delimiter in the feature name file |
skip.cell |
Number of lines to skip in the cells file before beginning to read cell names |
skip.feature |
Number of lines to skip in the features file before beginning to gene names |
mtx.transpose |
Transpose the matrix after reading in |
unique.features |
Make feature names unique (default TRUE) |
strip.suffix |
Remove trailing "-1" if present in all cell barcodes. |
A sparse matrix containing the expression data.
## Not run: # For local files: expression_matrix <- ReadMtx( mtx = "count_matrix.mtx.gz", features = "features.tsv.gz", cells = "barcodes.tsv.gz" ) seurat_object <- CreateSeuratObject(counts = expression_matrix) # For remote files: expression_matrix <- ReadMtx(mtx = "http://localhost/matrix.mtx", cells = "http://localhost/barcodes.tsv", features = "http://localhost/genes.tsv") seurat_object <- CreateSeuratObject(counts = data) ## End(Not run)
## Not run: # For local files: expression_matrix <- ReadMtx( mtx = "count_matrix.mtx.gz", features = "features.tsv.gz", cells = "barcodes.tsv.gz" ) seurat_object <- CreateSeuratObject(counts = expression_matrix) # For remote files: expression_matrix <- ReadMtx(mtx = "http://localhost/matrix.mtx", cells = "http://localhost/barcodes.tsv", features = "http://localhost/genes.tsv") seurat_object <- CreateSeuratObject(counts = data) ## End(Not run)
Read and Load Nanostring SMI data
ReadNanostring( data.dir, mtx.file = NULL, metadata.file = NULL, molecules.file = NULL, segmentations.file = NULL, type = "centroids", mol.type = "pixels", metadata = NULL, mols.filter = NA_character_, genes.filter = NA_character_, fov.filter = NULL, subset.counts.matrix = NULL, cell.mols.only = TRUE ) LoadNanostring(data.dir, fov, assay = "Nanostring")
ReadNanostring( data.dir, mtx.file = NULL, metadata.file = NULL, molecules.file = NULL, segmentations.file = NULL, type = "centroids", mol.type = "pixels", metadata = NULL, mols.filter = NA_character_, genes.filter = NA_character_, fov.filter = NULL, subset.counts.matrix = NULL, cell.mols.only = TRUE ) LoadNanostring(data.dir, fov, assay = "Nanostring")
data.dir |
Path to folder containing Nanostring SMI outputs |
mtx.file |
Path to Nanostring cell x gene matrix CSV |
metadata.file |
Contains metadata including cell center, area, and stain intensities |
molecules.file |
Path to molecules file |
segmentations.file |
Path to segmentations CSV |
type |
Type of cell spatial coordinate matrices to read; choose one or more of:
|
mol.type |
Type of molecule spatial coordinate matrices to read; choose one or more of:
|
metadata |
Type of available metadata to read; choose zero or more of:
|
mols.filter |
Filter molecules that match provided string |
genes.filter |
Filter genes from cell x gene matrix that match provided string |
fov.filter |
Only load in select FOVs. Nanostring SMI data contains 30 total FOVs. |
subset.counts.matrix |
If the counts matrix should be built from molecule coordinates for a specific segmentation; One of:
|
cell.mols.only |
If TRUE, only load molecules within a cell |
fov |
Name to store FOV as |
assay |
Name to store expression matrix as |
ReadNanostring
: A list with some combination of the
following values:
“matrix
”: a
sparse matrix with expression data; cells
are columns and features are rows
“centroids
”: a data frame with cell centroid
coordinates in three columns: “x”, “y”, and “cell”
“pixels
”: a data frame with molecule pixel coordinates
in three columns: “x”, “y”, and “gene”
LoadNanostring
: A Seurat
object
This function uses
progressr to
render status updates and progress bars. To enable progress updates, wrap
the function call in with_progress
or run
handlers(global = TRUE)
before running
this function. For more details about progressr, please read
vignette("progressr-intro")
This function uses
future to enable
parallelization. Parallelization strategies can be set using
plan
. Common plans include “sequential
”
for non-parallelized processing or “multisession
” for parallel
evaluation using multiple R sessions; for other plans, see the
“Implemented evaluation strategies” section of
?future::plan
. For a more thorough introduction
to future, see
vignette("future-1-overview")
This function requires the data.table package to be installed
Read output from Parse Biosciences
ReadParseBio(data.dir, ...)
ReadParseBio(data.dir, ...)
data.dir |
Directory containing the data files |
... |
Extra parameters passed to |
Load Slide-seq spatial data
ReadSlideSeq(coord.file, assay = "Spatial")
ReadSlideSeq(coord.file, assay = "Spatial")
coord.file |
Path to csv file containing bead coordinate positions |
assay |
Name of assay to associate image to |
A SlideSeq
object
Read output from STARsolo
ReadSTARsolo(data.dir, ...)
ReadSTARsolo(data.dir, ...)
data.dir |
Directory containing the data files |
... |
Extra parameters passed to |
Read in data from Vitessce-formatted JSON files
ReadVitessce( counts = NULL, coords = NULL, molecules = NULL, type = c("segmentations", "centroids"), filter = NA_character_ ) LoadHuBMAPCODEX(data.dir, fov, assay = "CODEX")
ReadVitessce( counts = NULL, coords = NULL, molecules = NULL, type = c("segmentations", "centroids"), filter = NA_character_ ) LoadHuBMAPCODEX(data.dir, fov, assay = "CODEX")
counts |
Path or URL to a Vitessce-formatted JSON file with
expression data; should end in “ |
coords |
Path or URL to a Vitessce-formatted JSON file with cell/spot
spatial coordinates; should end in “ |
molecules |
Path or URL to a Vitessce-formatted JSON file with molecule
spatial coordinates; should end in “ |
type |
Type of cell/spot spatial coordinates to return, choose one or more from:
|
filter |
A character to filter molecules by, pass |
data.dir |
Path to a directory containing Vitessce cells and clusters JSONs |
fov |
Name to store FOV as |
assay |
Name to store expression matrix as |
ReadVitessce
: A list with some combination of the
following values:
“counts
”: if counts
is not NULL
, an
expression matrix with cells as columns and features as rows
“centroids
”: if coords
is not NULL
and
type
is contains“centroids”, a data frame with cell centroids
in three columns: “x”, “y”, and “cell”
“segmentations
”: if coords
is not NULL
and
type
contains “centroids”, a data frame with cell
segmentations in three columns: “x”, “y” and “cell”
“molecules
”: if molecules
is not NULL
, a
data frame with molecule spatial coordinates in three columns: “x”,
“y”, and “gene”
LoadHuBMAPCODEX
: A Seurat
object
This function uses
progressr to
render status updates and progress bars. To enable progress updates, wrap
the function call in with_progress
or run
handlers(global = TRUE)
before running
this function. For more details about progressr, please read
vignette("progressr-intro")
This function requires the jsonlite package to be installed
## Not run: coords <- ReadVitessce( counts = "https://s3.amazonaws.com/vitessce-data/0.0.31/master_release/wang/wang.genes.json", coords = "https://s3.amazonaws.com/vitessce-data/0.0.31/master_release/wang/wang.cells.json", molecules = "https://s3.amazonaws.com/vitessce-data/0.0.31/master_release/wang/wang.molecules.json" ) names(coords) coords$counts[1:10, 1:10] head(coords$centroids) head(coords$segmentations) head(coords$molecules) ## End(Not run)
## Not run: coords <- ReadVitessce( counts = "https://s3.amazonaws.com/vitessce-data/0.0.31/master_release/wang/wang.genes.json", coords = "https://s3.amazonaws.com/vitessce-data/0.0.31/master_release/wang/wang.cells.json", molecules = "https://s3.amazonaws.com/vitessce-data/0.0.31/master_release/wang/wang.molecules.json" ) names(coords) coords$counts[1:10, 1:10] head(coords$centroids) head(coords$segmentations) head(coords$molecules) ## End(Not run)
Read and load in MERFISH data from Vizgen-formatted files
ReadVizgen( data.dir, transcripts = NULL, spatial = NULL, molecules = NULL, type = "segmentations", mol.type = "microns", metadata = NULL, filter = NA_character_, z = 3L ) LoadVizgen(data.dir, fov, assay = "Vizgen", z = 3L)
ReadVizgen( data.dir, transcripts = NULL, spatial = NULL, molecules = NULL, type = "segmentations", mol.type = "microns", metadata = NULL, filter = NA_character_, z = 3L ) LoadVizgen(data.dir, fov, assay = "Vizgen", z = 3L)
data.dir |
Path to the directory with Vizgen MERFISH files; requires at least one of the following files present:
|
transcripts |
Optional file path for counts matrix; pass |
spatial |
Optional file path for spatial metadata; pass |
molecules |
Optional file path for molecule coordinates file; pass
|
type |
Type of cell spatial coordinate matrices to read; choose one or more of:
|
mol.type |
Type of molecule spatial coordinate matrices to read; choose one or more of:
|
metadata |
Type of available metadata to read; choose zero or more of:
|
filter |
A character to filter molecules by, pass |
z |
Z-index to load; must be between 0 and 6, inclusive |
fov |
Name to store FOV as |
assay |
Name to store expression matrix as |
ReadVizgen
: A list with some combination of the
following values:
“transcripts
”: a
sparse matrix with expression data; cells
are columns and features are rows
“segmentations
”: a data frame with cell polygon outlines in
three columns: “x”, “y”, and “cell”
“centroids
”: a data frame with cell centroid
coordinates in three columns: “x”, “y”, and “cell”
“boxes
”: a data frame with cell box outlines in three
columns: “x”, “y”, and “cell”
“microns
”: a data frame with molecule micron
coordinates in three columns: “x”, “y”, and “gene”
“pixels
”: a data frame with molecule pixel coordinates
in three columns: “x”, “y”, and “gene”
“metadata
”: a data frame with the cell-level metadata
requested by metadata
LoadVizgen
: A Seurat
object
This function uses
progressr to
render status updates and progress bars. To enable progress updates, wrap
the function call in with_progress
or run
handlers(global = TRUE)
before running
this function. For more details about progressr, please read
vignette("progressr-intro")
This function uses
future to enable
parallelization. Parallelization strategies can be set using
plan
. Common plans include “sequential
”
for non-parallelized processing or “multisession
” for parallel
evaluation using multiple R sessions; for other plans, see the
“Implemented evaluation strategies” section of
?future::plan
. For a more thorough introduction
to future, see
vignette("future-1-overview")
This function requires the data.table package to be installed
For cells in each ident, set a new identity based on the most common value of a specified metadata column.
RegroupIdents(object, metadata)
RegroupIdents(object, metadata)
object |
Seurat object |
metadata |
Name of metadata column |
A Seurat object with the active idents regrouped
data("pbmc_small") pbmc_small <- RegroupIdents(pbmc_small, metadata = "groups")
data("pbmc_small") pbmc_small <- RegroupIdents(pbmc_small, metadata = "groups")
Normalize count data to relative counts per cell by dividing by the total
per cell. Optionally use a scale factor, e.g. for counts per million (CPM)
use scale.factor = 1e6
.
RelativeCounts(data, scale.factor = 1, verbose = TRUE)
RelativeCounts(data, scale.factor = 1, verbose = TRUE)
data |
Matrix with the raw count data |
scale.factor |
Scale the result. Default is 1 |
verbose |
Print progress |
Returns a matrix with the relative counts
mat <- matrix(data = rbinom(n = 25, size = 5, prob = 0.2), nrow = 5) mat mat_norm <- RelativeCounts(data = mat) mat_norm
mat <- matrix(data = rbinom(n = 25, size = 5, prob = 0.2), nrow = 5) mat mat_norm <- RelativeCounts(data = mat) mat_norm
Rename Cells in an Object
## S3 method for class 'SCTAssay' RenameCells(object, new.names = NULL, ...) ## S3 method for class 'SlideSeq' RenameCells(object, new.names = NULL, ...) ## S3 method for class 'STARmap' RenameCells(object, new.names = NULL, ...) ## S3 method for class 'VisiumV1' RenameCells(object, new.names = NULL, ...)
## S3 method for class 'SCTAssay' RenameCells(object, new.names = NULL, ...) ## S3 method for class 'SlideSeq' RenameCells(object, new.names = NULL, ...) ## S3 method for class 'STARmap' RenameCells(object, new.names = NULL, ...) ## S3 method for class 'VisiumV1' RenameCells(object, new.names = NULL, ...)
object |
An object |
new.names |
vector of new cell names |
... |
Arguments passed to other methods |
Draws a ridge plot of single cell data (gene expression, metrics, PC scores, etc.)
RidgePlot( object, features, cols = NULL, idents = NULL, sort = FALSE, assay = NULL, group.by = NULL, y.max = NULL, same.y.lims = FALSE, log = FALSE, ncol = NULL, slot = "data", stack = FALSE, combine = TRUE, fill.by = "feature" )
RidgePlot( object, features, cols = NULL, idents = NULL, sort = FALSE, assay = NULL, group.by = NULL, y.max = NULL, same.y.lims = FALSE, log = FALSE, ncol = NULL, slot = "data", stack = FALSE, combine = TRUE, fill.by = "feature" )
object |
Seurat object |
features |
Features to plot (gene expression, metrics, PC scores, anything that can be retreived by FetchData) |
cols |
Colors to use for plotting |
idents |
Which classes to include in the plot (default is all) |
sort |
Sort identity classes (on the x-axis) by the average expression of the attribute being potted, can also pass 'increasing' or 'decreasing' to change sort direction |
assay |
Name of assay to use, defaults to the active assay |
group.by |
Group (color) cells in different ways (for example, orig.ident) |
y.max |
Maximum y axis value |
same.y.lims |
Set all the y-axis limits to the same values |
log |
plot the feature axis on log scale |
ncol |
Number of columns if multiple plots are displayed |
slot |
Slot to pull expression data from (e.g. "counts" or "data") |
stack |
Horizontally stack plots for each feature |
combine |
Combine plots into a single |
fill.by |
Color violins/ridges based on either 'feature' or 'ident' |
A patchworked
ggplot object if
combine = TRUE
; otherwise, a list of ggplot objects
data("pbmc_small") RidgePlot(object = pbmc_small, features = 'PC_1')
data("pbmc_small") RidgePlot(object = pbmc_small, features = 'PC_1')
Runs a canonical correlation analysis using a diagonal implementation of CCA.
For details about stored CCA calculation parameters, see
PrintCCAParams
.
RunCCA(object1, object2, ...) ## Default S3 method: RunCCA( object1, object2, standardize = TRUE, num.cc = 20, seed.use = 42, verbose = FALSE, ... ) ## S3 method for class 'Seurat' RunCCA( object1, object2, assay1 = NULL, assay2 = NULL, num.cc = 20, features = NULL, renormalize = FALSE, rescale = FALSE, compute.gene.loadings = TRUE, add.cell.id1 = NULL, add.cell.id2 = NULL, verbose = TRUE, ... )
RunCCA(object1, object2, ...) ## Default S3 method: RunCCA( object1, object2, standardize = TRUE, num.cc = 20, seed.use = 42, verbose = FALSE, ... ) ## S3 method for class 'Seurat' RunCCA( object1, object2, assay1 = NULL, assay2 = NULL, num.cc = 20, features = NULL, renormalize = FALSE, rescale = FALSE, compute.gene.loadings = TRUE, add.cell.id1 = NULL, add.cell.id2 = NULL, verbose = TRUE, ... )
object1 |
First Seurat object |
object2 |
Second Seurat object. |
... |
Extra parameters (passed onto MergeSeurat in case with two objects passed, passed onto ScaleData in case with single object and rescale.groups set to TRUE) |
standardize |
Standardize matrices - scales columns to have unit variance and mean 0 |
num.cc |
Number of canonical vectors to calculate |
seed.use |
Random seed to set. If NULL, does not set a seed |
verbose |
Show progress messages |
assay1 , assay2
|
Assays to pull from in the first and second objects, respectively |
features |
Set of genes to use in CCA. Default is the union of both the variable features sets present in both objects. |
renormalize |
Renormalize raw data after merging the objects. If FALSE, merge the data matrices also. |
rescale |
Rescale the datasets prior to CCA. If FALSE, uses existing data in the scale data slots. |
compute.gene.loadings |
Also compute the gene loadings. NOTE - this will scale every gene in the dataset which may impose a high memory cost. |
add.cell.id1 , add.cell.id2
|
Add ... |
Returns a combined Seurat object with the CCA results stored.
data("pbmc_small") pbmc_small # As CCA requires two datasets, we will split our test object into two just for this example pbmc1 <- subset(pbmc_small, cells = colnames(pbmc_small)[1:40]) pbmc2 <- subset(pbmc_small, cells = colnames(x = pbmc_small)[41:80]) pbmc1[["group"]] <- "group1" pbmc2[["group"]] <- "group2" pbmc_cca <- RunCCA(object1 = pbmc1, object2 = pbmc2) # Print results print(x = pbmc_cca[["cca"]])
data("pbmc_small") pbmc_small # As CCA requires two datasets, we will split our test object into two just for this example pbmc1 <- subset(pbmc_small, cells = colnames(pbmc_small)[1:40]) pbmc2 <- subset(pbmc_small, cells = colnames(x = pbmc_small)[41:80]) pbmc1[["group"]] <- "group1" pbmc2[["group"]] <- "group2" pbmc_cca <- RunCCA(object1 = pbmc1, object2 = pbmc2) # Print results print(x = pbmc_cca[["cca"]])
Run fastica algorithm from the ica package for ICA dimensionality reduction.
For details about stored ICA calculation parameters, see
PrintICAParams
.
RunICA(object, ...) ## Default S3 method: RunICA( object, assay = NULL, nics = 50, rev.ica = FALSE, ica.function = "icafast", verbose = TRUE, ndims.print = 1:5, nfeatures.print = 30, reduction.name = "ica", reduction.key = "ica_", seed.use = 42, ... ) ## S3 method for class 'Assay' RunICA( object, assay = NULL, features = NULL, nics = 50, rev.ica = FALSE, ica.function = "icafast", verbose = TRUE, ndims.print = 1:5, nfeatures.print = 30, reduction.name = "ica", reduction.key = "ica_", seed.use = 42, ... ) ## S3 method for class 'Seurat' RunICA( object, assay = NULL, features = NULL, nics = 50, rev.ica = FALSE, ica.function = "icafast", verbose = TRUE, ndims.print = 1:5, nfeatures.print = 30, reduction.name = "ica", reduction.key = "IC_", seed.use = 42, ... )
RunICA(object, ...) ## Default S3 method: RunICA( object, assay = NULL, nics = 50, rev.ica = FALSE, ica.function = "icafast", verbose = TRUE, ndims.print = 1:5, nfeatures.print = 30, reduction.name = "ica", reduction.key = "ica_", seed.use = 42, ... ) ## S3 method for class 'Assay' RunICA( object, assay = NULL, features = NULL, nics = 50, rev.ica = FALSE, ica.function = "icafast", verbose = TRUE, ndims.print = 1:5, nfeatures.print = 30, reduction.name = "ica", reduction.key = "ica_", seed.use = 42, ... ) ## S3 method for class 'Seurat' RunICA( object, assay = NULL, features = NULL, nics = 50, rev.ica = FALSE, ica.function = "icafast", verbose = TRUE, ndims.print = 1:5, nfeatures.print = 30, reduction.name = "ica", reduction.key = "IC_", seed.use = 42, ... )
object |
Seurat object |
... |
Additional arguments to be passed to fastica |
assay |
Name of Assay ICA is being run on |
nics |
Number of ICs to compute |
rev.ica |
By default, computes the dimensional reduction on the cell x feature matrix. Setting to true will compute it on the transpose (feature x cell matrix). |
ica.function |
ICA function from ica package to run (options: icafast, icaimax, icajade) |
verbose |
Print the top genes associated with high/low loadings for the ICs |
ndims.print |
ICs to print genes for |
nfeatures.print |
Number of genes to print for each IC |
reduction.name |
dimensional reduction name |
reduction.key |
dimensional reduction key, specifies the string before the number for the dimension names. |
seed.use |
Set a random seed. Setting NULL will not set a seed. |
features |
Features to compute ICA on |
Run Linear Discriminant Analysis
Function to perform Linear Discriminant Analysis.
RunLDA(object, ...) ## Default S3 method: RunLDA( object, labels, assay = NULL, verbose = TRUE, ndims.print = 1:5, nfeatures.print = 30, reduction.key = "LDA_", seed = 42, ... ) ## S3 method for class 'Assay' RunLDA( object, assay = NULL, labels, features = NULL, verbose = TRUE, ndims.print = 1:5, nfeatures.print = 30, reduction.key = "LDA_", seed = 42, ... ) ## S3 method for class 'Seurat' RunLDA( object, assay = NULL, labels, features = NULL, reduction.name = "lda", reduction.key = "LDA_", seed = 42, verbose = TRUE, ndims.print = 1:5, nfeatures.print = 30, ... )
RunLDA(object, ...) ## Default S3 method: RunLDA( object, labels, assay = NULL, verbose = TRUE, ndims.print = 1:5, nfeatures.print = 30, reduction.key = "LDA_", seed = 42, ... ) ## S3 method for class 'Assay' RunLDA( object, assay = NULL, labels, features = NULL, verbose = TRUE, ndims.print = 1:5, nfeatures.print = 30, reduction.key = "LDA_", seed = 42, ... ) ## S3 method for class 'Seurat' RunLDA( object, assay = NULL, labels, features = NULL, reduction.name = "lda", reduction.key = "LDA_", seed = 42, verbose = TRUE, ndims.print = 1:5, nfeatures.print = 30, ... )
object |
An object of class Seurat. |
... |
Arguments passed to other methods |
labels |
Meta data column with target gene class labels. |
assay |
Assay to use for performing Linear Discriminant Analysis (LDA). |
verbose |
Print the top genes associated with high/low loadings for the PCs |
ndims.print |
Number of LDA dimensions to print. |
nfeatures.print |
Number of features to print for each LDA component. |
reduction.key |
Reduction key name. |
seed |
Value for random seed |
features |
Features to compute LDA on |
reduction.name |
dimensional reduction name, lda by default |
Wraps the functionality of markvario from the spatstat package.
RunMarkVario(spatial.location, data, ...)
RunMarkVario(spatial.location, data, ...)
spatial.location |
A 2 column matrix giving the spatial locations of each of the data points also in data |
data |
Matrix containing the data used as "marks" (e.g. gene expression) |
... |
Arguments passed to markvario |
Function to identify perturbed and non-perturbed gRNA expressing cells that accounts for multiple treatments/conditions/chemical perturbations.
RunMixscape( object, assay = "PRTB", slot = "scale.data", labels = "gene", nt.class.name = "NT", new.class.name = "mixscape_class", min.de.genes = 5, min.cells = 5, de.assay = "RNA", logfc.threshold = 0.25, iter.num = 10, verbose = FALSE, split.by = NULL, fine.mode = FALSE, fine.mode.labels = "guide_ID", prtb.type = "KO" )
RunMixscape( object, assay = "PRTB", slot = "scale.data", labels = "gene", nt.class.name = "NT", new.class.name = "mixscape_class", min.de.genes = 5, min.cells = 5, de.assay = "RNA", logfc.threshold = 0.25, iter.num = 10, verbose = FALSE, split.by = NULL, fine.mode = FALSE, fine.mode.labels = "guide_ID", prtb.type = "KO" )
object |
An object of class Seurat. |
assay |
Assay to use for mixscape classification. |
slot |
Assay data slot to use. |
labels |
metadata column with target gene labels. |
nt.class.name |
Classification name of non-targeting gRNA cells. |
new.class.name |
Name of mixscape classification to be stored in metadata. |
min.de.genes |
Required number of genes that are differentially expressed for method to separate perturbed and non-perturbed cells. |
min.cells |
Minimum number of cells in target gene class. If fewer than this many cells are assigned to a target gene class during classification, all are assigned NP. |
de.assay |
Assay to use when performing differential expression analysis. Usually RNA. |
logfc.threshold |
Limit testing to genes which show, on average, at least X-fold difference (log-scale) between the two groups of cells. Default is 0.25 Increasing logfc.threshold speeds up the function, but can miss weaker signals. |
iter.num |
Number of normalmixEM iterations to run if convergence does not occur. |
verbose |
Display messages |
split.by |
metadata column with experimental condition/cell type classification information. This is meant to be used to account for cases a perturbation is condition/cell type -specific. |
fine.mode |
When this is equal to TRUE, DE genes for each target gene class will be calculated for each gRNA separately and pooled into one DE list for calculating the perturbation score of every cell and their subsequent classification. |
fine.mode.labels |
metadata column with gRNA ID labels. |
prtb.type |
specify type of CRISPR perturbation expected for labeling mixscape classifications. Default is KO. |
Returns Seurat object with with the following information in the meta data and tools slots:
Classification result with cells being either classified as perturbed (KO, by default) or non-perturbed (NP) based on their target gene class.
Global classification result (perturbed, NP or NT)
Posterior probabilities used to determine if a cell is KO (default). Name of this item will change to match prtb.type parameter setting. (>0.5) or NP
Perturbation scores for every cell calculated in the first iteration of the function.
Wraps the functionality of the Moran.I function from the ape package. Weights are computed as 1/distance.
RunMoransI(data, pos, verbose = TRUE)
RunMoransI(data, pos, verbose = TRUE)
data |
Expression matrix |
pos |
Position matrix |
verbose |
Display messages/progress |
Run a PCA dimensionality reduction. For details about stored PCA calculation
parameters, see PrintPCAParams
.
RunPCA(object, ...) ## Default S3 method: RunPCA( object, assay = NULL, npcs = 50, rev.pca = FALSE, weight.by.var = TRUE, verbose = TRUE, ndims.print = 1:5, nfeatures.print = 30, reduction.key = "PC_", seed.use = 42, approx = TRUE, ... ) ## S3 method for class 'Assay' RunPCA( object, assay = NULL, features = NULL, npcs = 50, rev.pca = FALSE, weight.by.var = TRUE, verbose = TRUE, ndims.print = 1:5, nfeatures.print = 30, reduction.key = "PC_", seed.use = 42, ... ) ## S3 method for class 'Seurat' RunPCA( object, assay = NULL, features = NULL, npcs = 50, rev.pca = FALSE, weight.by.var = TRUE, verbose = TRUE, ndims.print = 1:5, nfeatures.print = 30, reduction.name = "pca", reduction.key = "PC_", seed.use = 42, ... )
RunPCA(object, ...) ## Default S3 method: RunPCA( object, assay = NULL, npcs = 50, rev.pca = FALSE, weight.by.var = TRUE, verbose = TRUE, ndims.print = 1:5, nfeatures.print = 30, reduction.key = "PC_", seed.use = 42, approx = TRUE, ... ) ## S3 method for class 'Assay' RunPCA( object, assay = NULL, features = NULL, npcs = 50, rev.pca = FALSE, weight.by.var = TRUE, verbose = TRUE, ndims.print = 1:5, nfeatures.print = 30, reduction.key = "PC_", seed.use = 42, ... ) ## S3 method for class 'Seurat' RunPCA( object, assay = NULL, features = NULL, npcs = 50, rev.pca = FALSE, weight.by.var = TRUE, verbose = TRUE, ndims.print = 1:5, nfeatures.print = 30, reduction.name = "pca", reduction.key = "PC_", seed.use = 42, ... )
object |
An object |
... |
Arguments passed to other methods and IRLBA |
assay |
Name of Assay PCA is being run on |
npcs |
Total Number of PCs to compute and store (50 by default) |
rev.pca |
By default computes the PCA on the cell x gene matrix. Setting to true will compute it on gene x cell matrix. |
weight.by.var |
Weight the cell embeddings by the variance of each PC (weights the gene loadings if rev.pca is TRUE) |
verbose |
Print the top genes associated with high/low loadings for the PCs |
ndims.print |
PCs to print genes for |
nfeatures.print |
Number of genes to print for each PC |
reduction.key |
dimensional reduction key, specifies the string before the number for the dimension names. PC by default |
seed.use |
Set a random seed. By default, sets the seed to 42. Setting NULL will not set a seed. |
approx |
Use truncated singular value decomposition to approximate PCA |
features |
Features to compute PCA on. If features=NULL, PCA will be run using the variable features for the Assay. Note that the features must be present in the scaled data. Any requested features that are not scaled or have 0 variance will be dropped, and the PCA will be run using the remaining features. |
reduction.name |
dimensional reduction name, pca by default |
Returns Seurat object with the PCA calculation stored in the reductions slot
Run a supervised LSI (SLSI) dimensionality reduction supervised by a cell-cell kernel. SLSI is used to capture a linear transformation of peaks that maximizes its dependency to the given cell-cell kernel.
RunSLSI(object, ...) ## Default S3 method: RunSLSI( object, assay = NULL, n = 50, reduction.key = "SLSI_", graph = NULL, verbose = TRUE, seed.use = 42, ... ) ## S3 method for class 'Assay' RunSLSI( object, assay = NULL, features = NULL, n = 50, reduction.key = "SLSI_", graph = NULL, verbose = TRUE, seed.use = 42, ... ) ## S3 method for class 'Seurat' RunSLSI( object, assay = NULL, features = NULL, n = 50, reduction.name = "slsi", reduction.key = "SLSI_", graph = NULL, verbose = TRUE, seed.use = 42, ... )
RunSLSI(object, ...) ## Default S3 method: RunSLSI( object, assay = NULL, n = 50, reduction.key = "SLSI_", graph = NULL, verbose = TRUE, seed.use = 42, ... ) ## S3 method for class 'Assay' RunSLSI( object, assay = NULL, features = NULL, n = 50, reduction.key = "SLSI_", graph = NULL, verbose = TRUE, seed.use = 42, ... ) ## S3 method for class 'Seurat' RunSLSI( object, assay = NULL, features = NULL, n = 50, reduction.name = "slsi", reduction.key = "SLSI_", graph = NULL, verbose = TRUE, seed.use = 42, ... )
object |
An object |
... |
Arguments passed to IRLBA irlba |
assay |
Name of Assay SLSI is being run on |
n |
Total Number of SLSI components to compute and store |
reduction.key |
dimensional reduction key, specifies the string before the number for the dimension names |
graph |
Graph used supervised by SLSI |
verbose |
Display messages |
seed.use |
Set a random seed. Setting NULL will not set a seed. |
features |
Features to compute SLSI on. If NULL, SLSI will be run using the variable features for the Assay. |
reduction.name |
dimensional reduction name |
Returns Seurat object with the SLSI calculation stored in the reductions slot
Run a supervised PCA (SPCA) dimensionality reduction supervised by a cell-cell kernel. SPCA is used to capture a linear transformation which maximizes its dependency to the given cell-cell kernel. We use SNN graph as the kernel to supervise the linear matrix factorization.
RunSPCA(object, ...) ## Default S3 method: RunSPCA( object, assay = NULL, npcs = 50, reduction.key = "SPC_", graph = NULL, verbose = FALSE, seed.use = 42, ... ) ## S3 method for class 'Assay' RunSPCA( object, assay = NULL, features = NULL, npcs = 50, reduction.key = "SPC_", graph = NULL, verbose = TRUE, seed.use = 42, ... ) ## S3 method for class 'Seurat' RunSPCA( object, assay = NULL, features = NULL, npcs = 50, reduction.name = "spca", reduction.key = "SPC_", graph = NULL, verbose = TRUE, seed.use = 42, ... )
RunSPCA(object, ...) ## Default S3 method: RunSPCA( object, assay = NULL, npcs = 50, reduction.key = "SPC_", graph = NULL, verbose = FALSE, seed.use = 42, ... ) ## S3 method for class 'Assay' RunSPCA( object, assay = NULL, features = NULL, npcs = 50, reduction.key = "SPC_", graph = NULL, verbose = TRUE, seed.use = 42, ... ) ## S3 method for class 'Seurat' RunSPCA( object, assay = NULL, features = NULL, npcs = 50, reduction.name = "spca", reduction.key = "SPC_", graph = NULL, verbose = TRUE, seed.use = 42, ... )
object |
An object |
... |
Arguments passed to other methods and IRLBA |
assay |
Name of Assay SPCA is being run on |
npcs |
Total Number of SPCs to compute and store (50 by default) |
reduction.key |
dimensional reduction key, specifies the string before the number for the dimension names. SPC by default |
graph |
Graph used supervised by SPCA |
verbose |
Print the top genes associated with high/low loadings for the SPCs |
seed.use |
Set a random seed. By default, sets the seed to 42. Setting NULL will not set a seed. |
features |
Features to compute SPCA on. If features=NULL, SPCA will be run using the variable features for the Assay. |
reduction.name |
dimensional reduction name, spca by default |
Returns Seurat object with the SPCA calculation stored in the reductions slot
Barshan E, Ghodsi A, Azimifar Z, Jahromi MZ. Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds. Pattern Recognition. 2011 Jul 1;44(7):1357-71. https://www.sciencedirect.com/science/article/pii/S0031320310005819?casa_token=AZMFg5OtPnAAAAAA:_Udu7GJ7G2ed1-XSmr-3IGSISUwcHfMpNtCj-qacXH5SBC4nwzVid36GXI3r8XG8dK5WOQui;
Run t-SNE dimensionality reduction on selected features. Has the option of
running in a reduced dimensional space (i.e. spectral tSNE, recommended),
or running based on a set of genes. For details about stored TSNE calculation
parameters, see PrintTSNEParams
.
RunTSNE(object, ...) ## S3 method for class 'matrix' RunTSNE( object, assay = NULL, seed.use = 1, tsne.method = "Rtsne", dim.embed = 2, reduction.key = "tSNE_", ... ) ## S3 method for class 'DimReduc' RunTSNE( object, cells = NULL, dims = 1:5, seed.use = 1, tsne.method = "Rtsne", dim.embed = 2, reduction.key = "tSNE_", ... ) ## S3 method for class 'dist' RunTSNE( object, assay = NULL, seed.use = 1, tsne.method = "Rtsne", dim.embed = 2, reduction.key = "tSNE_", ... ) ## S3 method for class 'Seurat' RunTSNE( object, reduction = "pca", cells = NULL, dims = 1:5, features = NULL, seed.use = 1, tsne.method = "Rtsne", dim.embed = 2, distance.matrix = NULL, reduction.name = "tsne", reduction.key = "tSNE_", ... )
RunTSNE(object, ...) ## S3 method for class 'matrix' RunTSNE( object, assay = NULL, seed.use = 1, tsne.method = "Rtsne", dim.embed = 2, reduction.key = "tSNE_", ... ) ## S3 method for class 'DimReduc' RunTSNE( object, cells = NULL, dims = 1:5, seed.use = 1, tsne.method = "Rtsne", dim.embed = 2, reduction.key = "tSNE_", ... ) ## S3 method for class 'dist' RunTSNE( object, assay = NULL, seed.use = 1, tsne.method = "Rtsne", dim.embed = 2, reduction.key = "tSNE_", ... ) ## S3 method for class 'Seurat' RunTSNE( object, reduction = "pca", cells = NULL, dims = 1:5, features = NULL, seed.use = 1, tsne.method = "Rtsne", dim.embed = 2, distance.matrix = NULL, reduction.name = "tsne", reduction.key = "tSNE_", ... )
object |
Seurat object |
... |
Arguments passed to other methods and to t-SNE call (most commonly used is perplexity) |
assay |
Name of assay that that t-SNE is being run on |
seed.use |
Random seed for the t-SNE. If NULL, does not set the seed |
tsne.method |
Select the method to use to compute the tSNE. Available methods are:
|
dim.embed |
The dimensional space of the resulting tSNE embedding (default is 2). For example, set to 3 for a 3d tSNE |
reduction.key |
dimensional reduction key, specifies the string before the number for the dimension names. tSNE_ by default |
cells |
Which cells to analyze (default, all cells) |
dims |
Which dimensions to use as input features |
reduction |
Which dimensional reduction (e.g. PCA, ICA) to use for the tSNE. Default is PCA |
features |
If set, run the tSNE on this subset of features
(instead of running on a set of reduced dimensions). Not set (NULL) by default;
|
distance.matrix |
If set, runs tSNE on the given distance matrix instead of data matrix (experimental) |
reduction.name |
dimensional reduction name, specifies the position in the object$dr list. tsne by default |
Runs the Uniform Manifold Approximation and Projection (UMAP) dimensional
reduction technique. To run using umap.method="umap-learn"
, you must
first install the umap-learn python package (e.g. via
pip install umap-learn
). Details on this package can be
found here: https://github.com/lmcinnes/umap. For a more in depth
discussion of the mathematics underlying UMAP, see the ArXiv paper here:
https://arxiv.org/abs/1802.03426.
RunUMAP(object, ...) ## Default S3 method: RunUMAP( object, reduction.key = "UMAP_", assay = NULL, reduction.model = NULL, return.model = FALSE, umap.method = "uwot", n.neighbors = 30L, n.components = 2L, metric = "cosine", n.epochs = NULL, learning.rate = 1, min.dist = 0.3, spread = 1, set.op.mix.ratio = 1, local.connectivity = 1L, repulsion.strength = 1, negative.sample.rate = 5, a = NULL, b = NULL, uwot.sgd = FALSE, seed.use = 42, metric.kwds = NULL, angular.rp.forest = FALSE, densmap = FALSE, dens.lambda = 2, dens.frac = 0.3, dens.var.shift = 0.1, verbose = TRUE, ... ) ## S3 method for class 'Graph' RunUMAP( object, assay = NULL, umap.method = "umap-learn", n.components = 2L, metric = "correlation", n.epochs = 0L, learning.rate = 1, min.dist = 0.3, spread = 1, repulsion.strength = 1, negative.sample.rate = 5L, a = NULL, b = NULL, uwot.sgd = FALSE, seed.use = 42L, metric.kwds = NULL, densmap = FALSE, densmap.kwds = NULL, verbose = TRUE, reduction.key = "UMAP_", ... ) ## S3 method for class 'Neighbor' RunUMAP(object, reduction.model, ...) ## S3 method for class 'Seurat' RunUMAP( object, dims = NULL, reduction = "pca", features = NULL, graph = NULL, assay = DefaultAssay(object = object), nn.name = NULL, slot = "data", umap.method = "uwot", reduction.model = NULL, return.model = FALSE, n.neighbors = 30L, n.components = 2L, metric = "cosine", n.epochs = NULL, learning.rate = 1, min.dist = 0.3, spread = 1, set.op.mix.ratio = 1, local.connectivity = 1L, repulsion.strength = 1, negative.sample.rate = 5L, a = NULL, b = NULL, uwot.sgd = FALSE, seed.use = 42L, metric.kwds = NULL, angular.rp.forest = FALSE, densmap = FALSE, dens.lambda = 2, dens.frac = 0.3, dens.var.shift = 0.1, verbose = TRUE, reduction.name = "umap", reduction.key = "UMAP_", ... )
RunUMAP(object, ...) ## Default S3 method: RunUMAP( object, reduction.key = "UMAP_", assay = NULL, reduction.model = NULL, return.model = FALSE, umap.method = "uwot", n.neighbors = 30L, n.components = 2L, metric = "cosine", n.epochs = NULL, learning.rate = 1, min.dist = 0.3, spread = 1, set.op.mix.ratio = 1, local.connectivity = 1L, repulsion.strength = 1, negative.sample.rate = 5, a = NULL, b = NULL, uwot.sgd = FALSE, seed.use = 42, metric.kwds = NULL, angular.rp.forest = FALSE, densmap = FALSE, dens.lambda = 2, dens.frac = 0.3, dens.var.shift = 0.1, verbose = TRUE, ... ) ## S3 method for class 'Graph' RunUMAP( object, assay = NULL, umap.method = "umap-learn", n.components = 2L, metric = "correlation", n.epochs = 0L, learning.rate = 1, min.dist = 0.3, spread = 1, repulsion.strength = 1, negative.sample.rate = 5L, a = NULL, b = NULL, uwot.sgd = FALSE, seed.use = 42L, metric.kwds = NULL, densmap = FALSE, densmap.kwds = NULL, verbose = TRUE, reduction.key = "UMAP_", ... ) ## S3 method for class 'Neighbor' RunUMAP(object, reduction.model, ...) ## S3 method for class 'Seurat' RunUMAP( object, dims = NULL, reduction = "pca", features = NULL, graph = NULL, assay = DefaultAssay(object = object), nn.name = NULL, slot = "data", umap.method = "uwot", reduction.model = NULL, return.model = FALSE, n.neighbors = 30L, n.components = 2L, metric = "cosine", n.epochs = NULL, learning.rate = 1, min.dist = 0.3, spread = 1, set.op.mix.ratio = 1, local.connectivity = 1L, repulsion.strength = 1, negative.sample.rate = 5L, a = NULL, b = NULL, uwot.sgd = FALSE, seed.use = 42L, metric.kwds = NULL, angular.rp.forest = FALSE, densmap = FALSE, dens.lambda = 2, dens.frac = 0.3, dens.var.shift = 0.1, verbose = TRUE, reduction.name = "umap", reduction.key = "UMAP_", ... )
object |
An object |
... |
Arguments passed to other methods and UMAP |
reduction.key |
dimensional reduction key, specifies the string before the number for the dimension names. UMAP by default |
assay |
Assay to pull data for when using |
reduction.model |
|
return.model |
whether UMAP will return the uwot model |
umap.method |
UMAP implementation to run. Can be
|
n.neighbors |
This determines the number of neighboring points used in local approximations of manifold structure. Larger values will result in more global structure being preserved at the loss of detailed local structure. In general this parameter should often be in the range 5 to 50. |
n.components |
The dimension of the space to embed into. |
metric |
metric: This determines the choice of metric used to measure distance in the input space. A wide variety of metrics are already coded, and a user defined function can be passed as long as it has been JITd by numba. |
n.epochs |
he number of training epochs to be used in optimizing the low dimensional embedding. Larger values result in more accurate embeddings. If NULL is specified, a value will be selected based on the size of the input dataset (200 for large datasets, 500 for small). |
learning.rate |
The initial learning rate for the embedding optimization. |
min.dist |
This controls how tightly the embedding is allowed compress points together. Larger values ensure embedded points are moreevenly distributed, while smaller values allow the algorithm to optimise more accurately with regard to local structure. Sensible values are in the range 0.001 to 0.5. |
spread |
The effective scale of embedded points. In combination with min.dist this determines how clustered/clumped the embedded points are. |
set.op.mix.ratio |
Interpolate between (fuzzy) union and intersection as the set operation used to combine local fuzzy simplicial sets to obtain a global fuzzy simplicial sets. Both fuzzy set operations use the product t-norm. The value of this parameter should be between 0.0 and 1.0; a value of 1.0 will use a pure fuzzy union, while 0.0 will use a pure fuzzy intersection. |
local.connectivity |
The local connectivity required - i.e. the number of nearest neighbors that should be assumed to be connected at a local level. The higher this value the more connected the manifold becomes locally. In practice this should be not more than the local intrinsic dimension of the manifold. |
repulsion.strength |
Weighting applied to negative samples in low dimensional embedding optimization. Values higher than one will result in greater weight being given to negative samples. |
negative.sample.rate |
The number of negative samples to select per positive sample in the optimization process. Increasing this value will result in greater repulsive force being applied, greater optimization cost, but slightly more accuracy. |
a |
More specific parameters controlling the embedding. If NULL, these values are set automatically as determined by min. dist and spread. Parameter of differentiable approximation of right adjoint functor. |
b |
More specific parameters controlling the embedding. If NULL, these values are set automatically as determined by min. dist and spread. Parameter of differentiable approximation of right adjoint functor. |
uwot.sgd |
Set |
seed.use |
Set a random seed. By default, sets the seed to 42. Setting NULL will not set a seed |
metric.kwds |
A dictionary of arguments to pass on to the metric, such as the p value for Minkowski distance. If NULL then no arguments are passed on. |
angular.rp.forest |
Whether to use an angular random projection forest to initialise the approximate nearest neighbor search. This can be faster, but is mostly on useful for metric that use an angular style distance such as cosine, correlation etc. In the case of those metrics angular forests will be chosen automatically. |
densmap |
Whether to use the density-augmented objective of densMAP. Turning on this option generates an embedding where the local densities are encouraged to be correlated with those in the original space. Parameters below with the prefix ‘dens’ further control the behavior of this extension. Default is FALSE. Only compatible with 'umap-learn' method and version of umap-learn >= 0.5.0 |
dens.lambda |
Specific parameter which controls the regularization weight of the density correlation term in densMAP. Higher values prioritize density preservation over the UMAP objective, and vice versa for values closer to zero. Setting this parameter to zero is equivalent to running the original UMAP algorithm. Default value is 2. |
dens.frac |
Specific parameter which controls the fraction of epochs (between 0 and 1) where the density-augmented objective is used in densMAP. The first (1 - dens_frac) fraction of epochs optimize the original UMAP objective before introducing the density correlation term. Default is 0.3. |
dens.var.shift |
Specific parameter which specifies a small constant added to the variance of local radii in the embedding when calculating the density correlation objective to prevent numerical instability from dividing by a small number. Default is 0.1. |
verbose |
Controls verbosity |
densmap.kwds |
A dictionary of arguments to pass on to the densMAP optimization. |
dims |
Which dimensions to use as input features, used only if
|
reduction |
Which dimensional reduction (PCA or ICA) to use for the UMAP input. Default is PCA |
features |
If set, run UMAP on this subset of features (instead of running on a
set of reduced dimensions). Not set (NULL) by default; |
graph |
Name of graph on which to run UMAP |
nn.name |
Name of knn output on which to run UMAP |
slot |
The slot used to pull data for when using |
reduction.name |
Name to store dimensional reduction under in the Seurat object |
Returns a Seurat object containing a UMAP representation
McInnes, L, Healy, J, UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, ArXiv e-prints 1802.03426, 2018
## Not run: data("pbmc_small") pbmc_small # Run UMAP map on first 5 PCs pbmc_small <- RunUMAP(object = pbmc_small, dims = 1:5) # Plot results DimPlot(object = pbmc_small, reduction = 'umap') ## End(Not run)
## Not run: data("pbmc_small") pbmc_small # Run UMAP map on first 5 PCs pbmc_small <- RunUMAP(object = pbmc_small, dims = 1:5) # Plot results DimPlot(object = pbmc_small, reduction = 'umap') ## End(Not run)
Downsample each cell to a specified number of UMIs. Includes an option to upsample cells below specified UMI as well.
SampleUMI(data, max.umi = 1000, upsample = FALSE, verbose = FALSE)
SampleUMI(data, max.umi = 1000, upsample = FALSE, verbose = FALSE)
data |
Matrix with the raw count data |
max.umi |
Number of UMIs to sample to |
upsample |
Upsamples all cells with fewer than max.umi |
verbose |
Display the progress bar |
Matrix with downsampled data
data("pbmc_small") counts = as.matrix(x = GetAssayData(object = pbmc_small, assay = "RNA", slot = "counts")) downsampled = SampleUMI(data = counts) head(x = downsampled)
data("pbmc_small") counts = as.matrix(x = GetAssayData(object = pbmc_small, assay = "RNA", slot = "counts")) downsampled = SampleUMI(data = counts) head(x = downsampled)
Save the Annoy index
SaveAnnoyIndex(object, file)
SaveAnnoyIndex(object, file)
object |
A Neighbor object with the annoy index stored |
file |
Path to file to write index to |
Scales and centers features in the dataset. If variables are provided in vars.to.regress, they are individually regressed against each feature, and the resulting residuals are then scaled and centered.
ScaleData(object, ...) ## Default S3 method: ScaleData( object, features = NULL, vars.to.regress = NULL, latent.data = NULL, split.by = NULL, model.use = "linear", use.umi = FALSE, do.scale = TRUE, do.center = TRUE, scale.max = 10, block.size = 1000, min.cells.to.block = 3000, verbose = TRUE, ... ) ## S3 method for class 'Assay' ScaleData( object, features = NULL, vars.to.regress = NULL, latent.data = NULL, split.by = NULL, model.use = "linear", use.umi = FALSE, do.scale = TRUE, do.center = TRUE, scale.max = 10, block.size = 1000, min.cells.to.block = 3000, verbose = TRUE, ... ) ## S3 method for class 'Seurat' ScaleData( object, features = NULL, assay = NULL, vars.to.regress = NULL, split.by = NULL, model.use = "linear", use.umi = FALSE, do.scale = TRUE, do.center = TRUE, scale.max = 10, block.size = 1000, min.cells.to.block = 3000, verbose = TRUE, ... )
ScaleData(object, ...) ## Default S3 method: ScaleData( object, features = NULL, vars.to.regress = NULL, latent.data = NULL, split.by = NULL, model.use = "linear", use.umi = FALSE, do.scale = TRUE, do.center = TRUE, scale.max = 10, block.size = 1000, min.cells.to.block = 3000, verbose = TRUE, ... ) ## S3 method for class 'Assay' ScaleData( object, features = NULL, vars.to.regress = NULL, latent.data = NULL, split.by = NULL, model.use = "linear", use.umi = FALSE, do.scale = TRUE, do.center = TRUE, scale.max = 10, block.size = 1000, min.cells.to.block = 3000, verbose = TRUE, ... ) ## S3 method for class 'Seurat' ScaleData( object, features = NULL, assay = NULL, vars.to.regress = NULL, split.by = NULL, model.use = "linear", use.umi = FALSE, do.scale = TRUE, do.center = TRUE, scale.max = 10, block.size = 1000, min.cells.to.block = 3000, verbose = TRUE, ... )
object |
An object |
... |
Arguments passed to other methods |
features |
Vector of features names to scale/center. Default is variable features. |
vars.to.regress |
Variables to regress out (previously latent.vars in RegressOut). For example, nUMI, or percent.mito. |
latent.data |
Extra data to regress out, should be cells x latent data |
split.by |
Name of variable in object metadata or a vector or factor defining
grouping of cells. See argument |
model.use |
Use a linear model or generalized linear model (poisson, negative binomial) for the regression. Options are 'linear' (default), 'poisson', and 'negbinom' |
use.umi |
Regress on UMI count data. Default is FALSE for linear modeling, but automatically set to TRUE if model.use is 'negbinom' or 'poisson' |
do.scale |
Whether to scale the data. |
do.center |
Whether to center the data. |
scale.max |
Max value to return for scaled data. The default is 10. Setting this can help reduce the effects of features that are only expressed in a very small number of cells. If regressing out latent variables and using a non-linear model, the default is 50. |
block.size |
Default size for number of features to scale at in a single computation. Increasing block.size may speed up calculations but at an additional memory cost. |
min.cells.to.block |
If object contains fewer than this number of cells, don't block for scaling calculations. |
verbose |
Displays a progress bar for scaling procedure |
assay |
Name of Assay to scale |
ScaleData now incorporates the functionality of the function formerly known as RegressOut (which regressed out given the effects of provided variables and then scaled the residuals). To make use of the regression functionality, simply pass the variables you want to remove to the vars.to.regress parameter.
Setting center to TRUE will center the expression for each feature by subtracting the average expression for that feature. Setting scale to TRUE will scale the expression level for each feature by dividing the centered feature expression levels by their standard deviations if center is TRUE and by their root mean square otherwise.
Get image scale factors
ScaleFactors(object, ...) scalefactors(spot, fiducial, hires, lowres) ## S3 method for class 'VisiumV1' ScaleFactors(object, ...) ## S3 method for class 'VisiumV1' ScaleFactors(object, ...)
ScaleFactors(object, ...) scalefactors(spot, fiducial, hires, lowres) ## S3 method for class 'VisiumV1' ScaleFactors(object, ...) ## S3 method for class 'VisiumV1' ScaleFactors(object, ...)
object |
An object to get scale factors from |
... |
Arguments passed to other methods |
spot |
Spot full resolution scale factor |
fiducial |
Fiducial full resolution scale factor |
hires |
High resolutoin scale factor |
lowres |
Low resolution scale factor |
An object of class scalefactors
scalefactors
objects can be created with scalefactors()
Significant PCs should show a p-value distribution that is strongly skewed to the left compared to the null distribution. The p-value for each PC is based on a proportion test comparing the number of features with a p-value below a particular threshold (score.thresh), compared with the proportion of features expected under a uniform distribution of p-values.
ScoreJackStraw(object, ...) ## S3 method for class 'JackStrawData' ScoreJackStraw(object, dims = 1:5, score.thresh = 1e-05, ...) ## S3 method for class 'DimReduc' ScoreJackStraw(object, dims = 1:5, score.thresh = 1e-05, ...) ## S3 method for class 'Seurat' ScoreJackStraw( object, reduction = "pca", dims = 1:5, score.thresh = 1e-05, do.plot = FALSE, ... )
ScoreJackStraw(object, ...) ## S3 method for class 'JackStrawData' ScoreJackStraw(object, dims = 1:5, score.thresh = 1e-05, ...) ## S3 method for class 'DimReduc' ScoreJackStraw(object, dims = 1:5, score.thresh = 1e-05, ...) ## S3 method for class 'Seurat' ScoreJackStraw( object, reduction = "pca", dims = 1:5, score.thresh = 1e-05, do.plot = FALSE, ... )
object |
An object |
... |
Arguments passed to other methods |
dims |
Which dimensions to examine |
score.thresh |
Threshold to use for the proportion test of PC significance (see Details) |
reduction |
Reduction associated with JackStraw to score |
do.plot |
Show plot. To return ggplot object, use |
Returns a Seurat object
Omri Wurtzel
The SCTModel object is a model and parameters storage from SCTransform. It can be used to calculate Pearson residuals for new genes.
The SCTAssay object contains all the information found in an Assay
object, with extra information from the results of SCTransform
## S3 method for class 'SCTAssay' levels(x) ## S3 replacement method for class 'SCTAssay' levels(x) <- value
## S3 method for class 'SCTAssay' levels(x) ## S3 replacement method for class 'SCTAssay' levels(x) <- value
x |
An |
value |
New levels, must be in the same order as the levels present |
levels
: SCT model names
levels<-
: x
with updated SCT model names
feature.attributes
A data.frame with feature attributes in SCTransform
cell.attributes
A data.frame with cell attributes in SCTransform
clips
A list of two numeric of length two specifying the min and max values the Pearson residual will be clipped to. One for vst and one for SCTransform
umi.assay
Name of the assay of the seurat object containing UMI matrix and the default is RNA
model
A formula used in SCTransform
arguments
other information used in SCTransform
median_umi
Median UMI (or scale factor) used to calculate corrected counts
SCTModel.list
A list containing SCT models
SCT results are named by initial run of SCTransform
in order
to keep SCT parameters straight between runs. When working with merged
SCTAssay
objects, these model names are important. levels
allows querying the models present. levels<-
allows the changing of
the names of the models present, useful when merging SCTAssay
objects.
Note: unlike normal levels<-
, levels<-.SCTAssay
allows complete changing of model names, not reordering.
SCTAssay
from an Assay
Conversion from an Assay
object to an SCTAssay
object by
is done by adding the additional slots to the object. If from
has
results generated by SCTransform
from Seurat v3.0.0 to v3.1.1,
the conversion will automagically fill the new slots with the data
## Not run: # SCTAssay objects are generated from SCTransform pbmc_small <- SCTransform(pbmc_small) ## End(Not run) # SCTAssay objects are generated from SCTransform pbmc_small <- SCTransform(pbmc_small) pbmc_small[["SCT"]] ## Not run: # Query and change SCT model names levels(pbmc_small[['SCT']]) levels(pbmc_small[['SCT']]) <- '3' levels(pbmc_small[['SCT']]) ## End(Not run)
## Not run: # SCTAssay objects are generated from SCTransform pbmc_small <- SCTransform(pbmc_small) ## End(Not run) # SCTAssay objects are generated from SCTransform pbmc_small <- SCTransform(pbmc_small) pbmc_small[["SCT"]] ## Not run: # Query and change SCT model names levels(pbmc_small[['SCT']]) levels(pbmc_small[['SCT']]) <- '3' levels(pbmc_small[['SCT']]) ## End(Not run)
This function calls sctransform::vst. The sctransform package is available at https://github.com/satijalab/sctransform. Use this function as an alternative to the NormalizeData, FindVariableFeatures, ScaleData workflow. Results are saved in a new assay (named SCT by default) with counts being (corrected) counts, data being log1p(counts), scale.data being pearson residuals; sctransform::vst intermediate results are saved in misc slot of new assay.
SCTransform( object, assay = "RNA", new.assay.name = "SCT", reference.SCT.model = NULL, do.correct.umi = TRUE, ncells = 5000, residual.features = NULL, variable.features.n = 3000, variable.features.rv.th = 1.3, vars.to.regress = NULL, do.scale = FALSE, do.center = TRUE, clip.range = c(-sqrt(x = ncol(x = object[[assay]])/30), sqrt(x = ncol(x = object[[assay]])/30)), conserve.memory = FALSE, return.only.var.genes = TRUE, seed.use = 1448145, verbose = TRUE, ... )
SCTransform( object, assay = "RNA", new.assay.name = "SCT", reference.SCT.model = NULL, do.correct.umi = TRUE, ncells = 5000, residual.features = NULL, variable.features.n = 3000, variable.features.rv.th = 1.3, vars.to.regress = NULL, do.scale = FALSE, do.center = TRUE, clip.range = c(-sqrt(x = ncol(x = object[[assay]])/30), sqrt(x = ncol(x = object[[assay]])/30)), conserve.memory = FALSE, return.only.var.genes = TRUE, seed.use = 1448145, verbose = TRUE, ... )
object |
A seurat object |
assay |
Name of assay to pull the count data from; default is 'RNA' |
new.assay.name |
Name for the new assay containing the normalized data |
reference.SCT.model |
If not NULL, compute residuals for the object using the provided SCT model; supports only log_umi as the latent variable. If residual.features are not specified, compute for the top variable.features.n specified in the model which are also present in the object. If residual.features are specified, the variable features of the resulting SCT assay are set to the top variable.features.n in the model. |
do.correct.umi |
Place corrected UMI matrix in assay counts slot; default is TRUE |
ncells |
Number of subsampling cells used to build NB regression; default is 5000 |
residual.features |
Genes to calculate residual features for; default is NULL (all genes). If specified, will be set to VariableFeatures of the returned object. |
variable.features.n |
Use this many features as variable features after ranking by residual variance; default is 3000. Only applied if residual.features is not set. |
variable.features.rv.th |
Instead of setting a fixed number of variable features,
use this residual variance cutoff; this is only used when |
vars.to.regress |
Variables to regress out in a second non-regularized linear regression. For example, percent.mito. Default is NULL |
do.scale |
Whether to scale residuals to have unit variance; default is FALSE |
do.center |
Whether to center residuals to have mean zero; default is TRUE |
clip.range |
Range to clip the residuals to; default is |
conserve.memory |
If set to TRUE the residual matrix for all genes is never created in full; useful for large data sets, but will take longer to run; this will also set return.only.var.genes to TRUE; default is FALSE |
return.only.var.genes |
If set to TRUE the scale.data matrices in output assay are subset to contain only the variable genes; default is TRUE |
seed.use |
Set a random seed. By default, sets the seed to 1448145. Setting NULL will not set a seed. |
verbose |
Whether to print messages and progress bars |
... |
Additional parameters passed to |
Returns a Seurat object with a new assay (named SCT by default) with counts being (corrected) counts, data being log1p(counts), scale.data being pearson residuals; sctransform::vst intermediate results are saved in misc slot of the new assay.
data("pbmc_small") SCTransform(object = pbmc_small)
data("pbmc_small") SCTransform(object = pbmc_small)
Pull the SCTResults
information from an SCTAssay
object.
SCTResults(object, ...) SCTResults(object, ...) <- value ## S3 method for class 'SCTModel' SCTResults(object, slot, ...) ## S3 replacement method for class 'SCTModel' SCTResults(object, slot, ...) <- value ## S3 method for class 'SCTAssay' SCTResults(object, slot, model = NULL, ...) ## S3 replacement method for class 'SCTAssay' SCTResults(object, slot, model = NULL, ...) <- value ## S3 method for class 'Seurat' SCTResults(object, assay = "SCT", slot, model = NULL, ...)
SCTResults(object, ...) SCTResults(object, ...) <- value ## S3 method for class 'SCTModel' SCTResults(object, slot, ...) ## S3 replacement method for class 'SCTModel' SCTResults(object, slot, ...) <- value ## S3 method for class 'SCTAssay' SCTResults(object, slot, model = NULL, ...) ## S3 replacement method for class 'SCTAssay' SCTResults(object, slot, model = NULL, ...) <- value ## S3 method for class 'Seurat' SCTResults(object, assay = "SCT", slot, model = NULL, ...)
object |
An object |
... |
Arguments passed to other methods (not used) |
value |
new data to set |
slot |
Which slot to pull the SCT results from |
model |
Name of SCModel to pull result from. Available names can be
retrieved with |
assay |
Assay in the Seurat object to pull from |
Returns the value present in the requested slot for the requested group. If group is not specified, returns a list of slot results for each group unless there is only one group present (in which case it just returns the slot directly).
Choose the features to use when integrating multiple datasets. This function ranks features by the number of datasets they are deemed variable in, breaking ties by the median variable feature rank across datasets. It returns the top scoring features by this ranking.
SelectIntegrationFeatures( object.list, nfeatures = 2000, assay = NULL, verbose = TRUE, fvf.nfeatures = 2000, ... )
SelectIntegrationFeatures( object.list, nfeatures = 2000, assay = NULL, verbose = TRUE, fvf.nfeatures = 2000, ... )
object.list |
List of seurat objects |
nfeatures |
Number of features to return |
assay |
Name or vector of assay names (one for each object) from which to pull the variable features. |
verbose |
Print messages |
fvf.nfeatures |
nfeatures for |
... |
Additional parameters to |
If for any assay in the list, FindVariableFeatures
hasn't been
run, this method will try to run it using the fvf.nfeatures
parameter
and any additional ones specified through the ....
A vector of selected features
## Not run: # to install the SeuratData package see https://github.com/satijalab/seurat-data library(SeuratData) data("panc8") # panc8 is a merged Seurat object containing 8 separate pancreas datasets # split the object by dataset and take the first 2 pancreas.list <- SplitObject(panc8, split.by = "tech")[1:2] # perform SCTransform normalization pancreas.list <- lapply(X = pancreas.list, FUN = SCTransform) # select integration features features <- SelectIntegrationFeatures(pancreas.list) ## End(Not run)
## Not run: # to install the SeuratData package see https://github.com/satijalab/seurat-data library(SeuratData) data("panc8") # panc8 is a merged Seurat object containing 8 separate pancreas datasets # split the object by dataset and take the first 2 pancreas.list <- SplitObject(panc8, split.by = "tech")[1:2] # perform SCTransform normalization pancreas.list <- lapply(X = pancreas.list, FUN = SCTransform) # select integration features features <- SelectIntegrationFeatures(pancreas.list) ## End(Not run)
Set integration data
SetIntegrationData(object, integration.name, slot, new.data)
SetIntegrationData(object, integration.name, slot, new.data)
object |
Seurat object |
integration.name |
Name of integration object |
slot |
Which slot in integration object to set |
new.data |
New data to insert |
Returns a Seurat
object
Converts a quantile in character form to a number regarding some data. String form for a quantile is represented as a number prefixed with “q”; for example, 10th quantile is “q10” while 2nd quantile is “q2”. Will only take a quantile of non-zero data values
SetQuantile(cutoff, data)
SetQuantile(cutoff, data)
cutoff |
The cutoff to turn into a quantile |
data |
The data to turn find the quantile of |
The numerical representation of the quantile
set.seed(42) SetQuantile('q10', sample(1:100, 10))
set.seed(42) SetQuantile('q10', sample(1:100, 10))
The Seurat object is a representation of single-cell expression data for R;
for more details, please see the documentation in
SeuratObject
For more details, please see the documentation in
SeuratObject
SeuratObject::SeuratCommand-class
Various themes to be applied to ggplot2-based plots
SeuratTheme
The curated Seurat theme, consists of ...
DarkTheme
A dark theme, axes and text turn to white, the background becomes black
NoAxes
Removes axis lines, text, and ticks
NoLegend
Removes the legend
FontSize
Sets axis and title font sizes
NoGrid
Removes grid lines
SeuratAxes
Set Seurat-style axes
SpatialTheme
A theme designed for spatial visualizations (eg PolyFeaturePlot
, PolyDimPlot
)
RestoreLegend
Restore a legend after removal
RotatedAxis
Rotate X axis text 45 degrees
BoldTitle
Enlarges and emphasizes the title
SeuratTheme() CenterTitle(...) DarkTheme(...) FontSize( x.text = NULL, y.text = NULL, x.title = NULL, y.title = NULL, main = NULL, ... ) NoAxes(..., keep.text = FALSE, keep.ticks = FALSE) NoLegend(...) NoGrid(...) SeuratAxes(...) SpatialTheme(...) RestoreLegend(..., position = "right") RotatedAxis(...) BoldTitle(...) WhiteBackground(...)
SeuratTheme() CenterTitle(...) DarkTheme(...) FontSize( x.text = NULL, y.text = NULL, x.title = NULL, y.title = NULL, main = NULL, ... ) NoAxes(..., keep.text = FALSE, keep.ticks = FALSE) NoLegend(...) NoGrid(...) SeuratAxes(...) SpatialTheme(...) RestoreLegend(..., position = "right") RotatedAxis(...) BoldTitle(...) WhiteBackground(...)
... |
Extra parameters to be passed to |
x.text , y.text
|
X and Y axis text sizes |
x.title , y.title
|
X and Y axis title sizes |
main |
Plot title size |
keep.text |
Keep axis text |
keep.ticks |
Keep axis ticks |
position |
A position to restore the legend to |
A ggplot2 theme object
# Generate a plot with a dark theme library(ggplot2) df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2)) p <- ggplot(data = df, mapping = aes(x = x, y = y)) + geom_point(mapping = aes(color = 'red')) p + DarkTheme(legend.position = 'none') # Generate a plot with no axes library(ggplot2) df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2)) p <- ggplot(data = df, mapping = aes(x = x, y = y)) + geom_point(mapping = aes(color = 'red')) p + NoAxes() # Generate a plot with no legend library(ggplot2) df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2)) p <- ggplot(data = df, mapping = aes(x = x, y = y)) + geom_point(mapping = aes(color = 'red')) p + NoLegend() # Generate a plot with no grid lines library(ggplot2) df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2)) p <- ggplot(data = df, mapping = aes(x = x, y = y)) + geom_point(mapping = aes(color = 'red')) p + NoGrid()
# Generate a plot with a dark theme library(ggplot2) df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2)) p <- ggplot(data = df, mapping = aes(x = x, y = y)) + geom_point(mapping = aes(color = 'red')) p + DarkTheme(legend.position = 'none') # Generate a plot with no axes library(ggplot2) df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2)) p <- ggplot(data = df, mapping = aes(x = x, y = y)) + geom_point(mapping = aes(color = 'red')) p + NoAxes() # Generate a plot with no legend library(ggplot2) df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2)) p <- ggplot(data = df, mapping = aes(x = x, y = y)) + geom_point(mapping = aes(color = 'red')) p + NoLegend() # Generate a plot with no grid lines library(ggplot2) df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2)) p <- ggplot(data = df, mapping = aes(x = x, y = y)) + geom_point(mapping = aes(color = 'red')) p + NoGrid()
The SlideSeq class represents spatial information from the Slide-seq platform
coordinates
...
assay
Name of assay to associate image data with; will give this image
priority for visualization when the assay is set as the active/default assay
in a Seurat
object
key
Key for the image
For more details, please see the documentation in
SeuratObject
SeuratObject::SpatialImage-class
SpatialPlot plots a feature or discrete grouping (e.g. cluster assignments) as spots over the image that was collected. We also provide SpatialFeaturePlot and SpatialDimPlot as wrapper functions around SpatialPlot for a consistent naming framework.
SpatialPlot( object, group.by = NULL, features = NULL, images = NULL, cols = NULL, image.alpha = 1, crop = TRUE, slot = "data", keep.scale = "feature", min.cutoff = NA, max.cutoff = NA, cells.highlight = NULL, cols.highlight = c("#DE2D26", "grey50"), facet.highlight = FALSE, label = FALSE, label.size = 5, label.color = "white", label.box = TRUE, repel = FALSE, ncol = NULL, combine = TRUE, pt.size.factor = 1.6, alpha = c(1, 1), stroke = 0.25, interactive = FALSE, do.identify = FALSE, identify.ident = NULL, do.hover = FALSE, information = NULL ) SpatialDimPlot( object, group.by = NULL, images = NULL, cols = NULL, crop = TRUE, cells.highlight = NULL, cols.highlight = c("#DE2D26", "grey50"), facet.highlight = FALSE, label = FALSE, label.size = 7, label.color = "white", repel = FALSE, ncol = NULL, combine = TRUE, pt.size.factor = 1.6, alpha = c(1, 1), image.alpha = 1, stroke = 0.25, label.box = TRUE, interactive = FALSE, information = NULL ) SpatialFeaturePlot( object, features, images = NULL, crop = TRUE, slot = "data", keep.scale = "feature", min.cutoff = NA, max.cutoff = NA, ncol = NULL, combine = TRUE, pt.size.factor = 1.6, alpha = c(1, 1), image.alpha = 1, stroke = 0.25, interactive = FALSE, information = NULL )
SpatialPlot( object, group.by = NULL, features = NULL, images = NULL, cols = NULL, image.alpha = 1, crop = TRUE, slot = "data", keep.scale = "feature", min.cutoff = NA, max.cutoff = NA, cells.highlight = NULL, cols.highlight = c("#DE2D26", "grey50"), facet.highlight = FALSE, label = FALSE, label.size = 5, label.color = "white", label.box = TRUE, repel = FALSE, ncol = NULL, combine = TRUE, pt.size.factor = 1.6, alpha = c(1, 1), stroke = 0.25, interactive = FALSE, do.identify = FALSE, identify.ident = NULL, do.hover = FALSE, information = NULL ) SpatialDimPlot( object, group.by = NULL, images = NULL, cols = NULL, crop = TRUE, cells.highlight = NULL, cols.highlight = c("#DE2D26", "grey50"), facet.highlight = FALSE, label = FALSE, label.size = 7, label.color = "white", repel = FALSE, ncol = NULL, combine = TRUE, pt.size.factor = 1.6, alpha = c(1, 1), image.alpha = 1, stroke = 0.25, label.box = TRUE, interactive = FALSE, information = NULL ) SpatialFeaturePlot( object, features, images = NULL, crop = TRUE, slot = "data", keep.scale = "feature", min.cutoff = NA, max.cutoff = NA, ncol = NULL, combine = TRUE, pt.size.factor = 1.6, alpha = c(1, 1), image.alpha = 1, stroke = 0.25, interactive = FALSE, information = NULL )
object |
A Seurat object |
group.by |
Name of meta.data column to group the data by |
features |
Name of the feature to visualize. Provide either group.by OR features, not both. |
images |
Name of the images to use in the plot(s) |
cols |
Vector of colors, each color corresponds to an identity class.
This may also be a single character or numeric value corresponding to a
palette as specified by |
image.alpha |
Adjust the opacity of the background images. Set to 0 to remove. |
crop |
Crop the plot in to focus on points plotted. Set to |
slot |
If plotting a feature, which data slot to pull from (counts, data, or scale.data) |
keep.scale |
How to handle the color scale across multiple plots. Options are:
|
min.cutoff , max.cutoff
|
Vector of minimum and maximum cutoff values for each feature, may specify quantile in the form of 'q##' where '##' is the quantile (eg, 'q1', 'q10') |
cells.highlight |
A list of character or numeric vectors of cells to highlight. If only one group of cells desired, can simply pass a vector instead of a list. If set, colors selected cells to the color(s) in cols.highlight |
cols.highlight |
A vector of colors to highlight the cells as; ordered the same as the groups in cells.highlight; last color corresponds to unselected cells. |
facet.highlight |
When highlighting certain groups of cells, split each group into its own plot |
label |
Whether to label the clusters |
label.size |
Sets the size of the labels |
label.color |
Sets the color of the label text |
label.box |
Whether to put a box around the label text (geom_text vs geom_label) |
repel |
Repels the labels to prevent overlap |
ncol |
Number of columns if plotting multiple plots |
combine |
Combine plots into a single gg object; note that if TRUE; themeing will not work when plotting multiple features/groupings |
pt.size.factor |
Scale the size of the spots. |
alpha |
Controls opacity of spots. Provide as a vector specifying the min and max for SpatialFeaturePlot. For SpatialDimPlot, provide a single alpha value for each plot. |
stroke |
Control the width of the border around the spots |
interactive |
Launch an interactive SpatialDimPlot or SpatialFeaturePlot
session, see |
do.identify , do.hover
|
DEPRECATED in favor of |
identify.ident |
DEPRECATED |
information |
An optional dataframe or matrix of extra information to be displayed on hover |
If do.identify
, either a vector of cells selected or the object
with selected cells set to the value of identify.ident
(if set). Else,
if do.hover
, a plotly object with interactive graphics. Else, a ggplot
object
## Not run: # For functionality analagous to FeaturePlot SpatialPlot(seurat.object, features = "MS4A1") SpatialFeaturePlot(seurat.object, features = "MS4A1") # For functionality analagous to DimPlot SpatialPlot(seurat.object, group.by = "clusters") SpatialDimPlot(seurat.object, group.by = "clusters") ## End(Not run)
## Not run: # For functionality analagous to FeaturePlot SpatialPlot(seurat.object, features = "MS4A1") SpatialFeaturePlot(seurat.object, features = "MS4A1") # For functionality analagous to DimPlot SpatialPlot(seurat.object, group.by = "clusters") SpatialDimPlot(seurat.object, group.by = "clusters") ## End(Not run)
Splits object based on a single attribute into a list of subsetted objects, one for each level of the attribute. For example, useful for taking an object that contains cells from many patients, and subdividing it into patient-specific objects.
SplitObject(object, split.by = "ident")
SplitObject(object, split.by = "ident")
object |
Seurat object |
split.by |
Attribute for splitting. Default is "ident". Currently only supported for class-level (i.e. non-quantitative) attributes. |
A named list of Seurat objects, each containing a subset of cells from the original object.
data("pbmc_small") # Assign the test object a three level attribute groups <- sample(c("group1", "group2", "group3"), size = 80, replace = TRUE) names(groups) <- colnames(pbmc_small) pbmc_small <- AddMetaData(object = pbmc_small, metadata = groups, col.name = "group") obj.list <- SplitObject(pbmc_small, split.by = "group")
data("pbmc_small") # Assign the test object a three level attribute groups <- sample(c("group1", "group2", "group3"), size = 80, replace = TRUE) names(groups) <- colnames(pbmc_small) pbmc_small <- AddMetaData(object = pbmc_small, metadata = groups, col.name = "group") obj.list <- SplitObject(pbmc_small, split.by = "group")
The STARmap class
assay
Name of assay to associate image data with; will give this image
priority for visualization when the assay is set as the active/default assay
in a Seurat
object
key
Key for the image
Subset an AnchorSet object
## S3 method for class 'AnchorSet' subset( x, score.threshold = NULL, disallowed.dataset.pairs = NULL, dataset.matrix = NULL, group.by = NULL, disallowed.ident.pairs = NULL, ident.matrix = NULL, ... )
## S3 method for class 'AnchorSet' subset( x, score.threshold = NULL, disallowed.dataset.pairs = NULL, dataset.matrix = NULL, group.by = NULL, disallowed.ident.pairs = NULL, ident.matrix = NULL, ... )
x |
object to be subsetted. |
score.threshold |
Only anchor pairs with scores greater than this value are retained. |
disallowed.dataset.pairs |
Remove any anchors formed between the
provided pairs. E.g. |
dataset.matrix |
Provide a binary matrix specifying whether a dataset pair is allowable (1) or not (0). Should be a dataset x dataset matrix. |
group.by |
Grouping variable to determine allowable ident pairs |
disallowed.ident.pairs |
Remove any anchors formed between provided
ident pairs. E.g. |
ident.matrix |
Provide a binary matrix specifying whether an ident pair is allowable (1) or not (0). Should be an ident x ident symmetric matrix |
... |
further arguments to be passed to or from other methods. |
Returns an AnchorSet
object with specified anchors
filtered out
This convenience function subsets a Seurat object based on calculated inflection points.
SubsetByBarcodeInflections(object)
SubsetByBarcodeInflections(object)
object |
Seurat object |
See [CalculateBarcodeInflections()] to calculate inflection points and [BarcodeInflectionsPlot()] to visualize and test inflection point calculations.
Returns a subsetted Seurat object.
Robert A. Amezquita, [email protected]
CalculateBarcodeInflections
BarcodeInflectionsPlot
data("pbmc_small") pbmc_small <- CalculateBarcodeInflections( object = pbmc_small, group.column = 'groups', threshold.low = 20, threshold.high = 30 ) SubsetByBarcodeInflections(object = pbmc_small)
data("pbmc_small") pbmc_small <- CalculateBarcodeInflections( object = pbmc_small, group.column = 'groups', threshold.low = 20, threshold.high = 30 ) SubsetByBarcodeInflections(object = pbmc_small)
Return a list of genes with the strongest contribution to a set of components
TopCells(object, dim = 1, ncells = 20, balanced = FALSE, ...)
TopCells(object, dim = 1, ncells = 20, balanced = FALSE, ...)
object |
DimReduc object |
dim |
Dimension to use |
ncells |
Number of cells to return |
balanced |
Return an equal number of cells with both + and - scores. |
... |
Extra parameters passed to |
Returns a vector of cells
data("pbmc_small") pbmc_small head(TopCells(object = pbmc_small[["pca"]])) # Can specify which dimension and how many cells to return TopCells(object = pbmc_small[["pca"]], dim = 2, ncells = 5)
data("pbmc_small") pbmc_small head(TopCells(object = pbmc_small[["pca"]])) # Can specify which dimension and how many cells to return TopCells(object = pbmc_small[["pca"]], dim = 2, ncells = 5)
Return a list of features with the strongest contribution to a set of components
TopFeatures( object, dim = 1, nfeatures = 20, projected = FALSE, balanced = FALSE, ... )
TopFeatures( object, dim = 1, nfeatures = 20, projected = FALSE, balanced = FALSE, ... )
object |
DimReduc object |
dim |
Dimension to use |
nfeatures |
Number of features to return |
projected |
Use the projected feature loadings |
balanced |
Return an equal number of features with both + and - scores. |
... |
Extra parameters passed to |
Returns a vector of features
data("pbmc_small") pbmc_small TopFeatures(object = pbmc_small[["pca"]], dim = 1) # After projection: TopFeatures(object = pbmc_small[["pca"]], dim = 1, projected = TRUE)
data("pbmc_small") pbmc_small TopFeatures(object = pbmc_small[["pca"]], dim = 1) # After projection: TopFeatures(object = pbmc_small[["pca"]], dim = 1, projected = TRUE)
Return a vector of cell names of the nearest n cells.
TopNeighbors(object, cell, n = 5)
TopNeighbors(object, cell, n = 5)
object |
|
cell |
Cell of interest |
n |
Number of neighbors to return |
Returns a vector of cell names
Inherits from the Anchorset class. Implemented mainly for method dispatch
purposes. See AnchorSet
for slot details.
Transfer categorical or continuous data across single-cell datasets. For
transferring categorical information, pass a vector from the reference
dataset (e.g. refdata = reference$celltype
). For transferring
continuous information, pass a matrix from the reference dataset (e.g.
refdata = GetAssayData(reference[['RNA']])
).
TransferData( anchorset, refdata, reference = NULL, query = NULL, weight.reduction = "pcaproject", l2.norm = FALSE, dims = NULL, k.weight = 50, sd.weight = 1, eps = 0, n.trees = 50, verbose = TRUE, slot = "data", prediction.assay = FALSE, store.weights = TRUE )
TransferData( anchorset, refdata, reference = NULL, query = NULL, weight.reduction = "pcaproject", l2.norm = FALSE, dims = NULL, k.weight = 50, sd.weight = 1, eps = 0, n.trees = 50, verbose = TRUE, slot = "data", prediction.assay = FALSE, store.weights = TRUE )
anchorset |
An |
refdata |
Data to transfer. This can be specified in one of two ways:
|
reference |
Reference object from which to pull data to transfer |
query |
Query object into which the data will be transferred. |
weight.reduction |
Dimensional reduction to use for the weighting anchors. Options are:
|
l2.norm |
Perform L2 normalization on the cell embeddings after dimensional reduction |
dims |
Set of dimensions to use in the anchor weighting procedure. If NULL, the same dimensions that were used to find anchors will be used for weighting. |
k.weight |
Number of neighbors to consider when weighting anchors |
sd.weight |
Controls the bandwidth of the Gaussian kernel for weighting |
eps |
Error bound on the neighbor finding algorithm (from
|
n.trees |
More trees gives higher precision when using annoy approximate nearest neighbor search |
verbose |
Print progress bars and output |
slot |
Slot to store the imputed data. Must be either "data" (default) or "counts" |
prediction.assay |
Return an |
store.weights |
Optionally store the weights matrix used for predictions in the returned query object. |
The main steps of this procedure are outlined below. For a more detailed description of the methodology, please see Stuart, Butler, et al Cell 2019. doi:10.1016/j.cell.2019.05.031; doi:10.1101/460147
For both transferring discrete labels and also feature imputation, we first compute the weights matrix.
Construct a weights matrix that defines the association between each
query cell and each anchor. These weights are computed as 1 - the distance
between the query cell and the anchor divided by the distance of the query
cell to the k.weight
th anchor multiplied by the anchor score
computed in FindIntegrationAnchors
. We then apply a Gaussian
kernel width a bandwidth defined by sd.weight
and normalize across
all k.weight
anchors.
The main difference between label transfer (classification) and feature imputation is what gets multiplied by the weights matrix. For label transfer, we perform the following steps:
Create a binary classification matrix, the rows corresponding to each possible class and the columns corresponding to the anchors. If the reference cell in the anchor pair is a member of a certain class, that matrix entry is filled with a 1, otherwise 0.
Multiply this classification matrix by the transpose of weights matrix to compute a prediction score for each class for each cell in the query dataset.
For feature imputation, we perform the following step:
Multiply the expression matrix for the reference anchor cells by the weights matrix. This returns a predicted expression matrix for the specified features for each cell in the query dataset.
If query
is not provided, for the categorical data in refdata
,
returns a data.frame with label predictions. If refdata
is a matrix,
returns an Assay object where the imputed data has been stored in the
provided slot.
If query
is provided, a modified query object is returned. For
the categorical data in refdata, prediction scores are stored as Assays
(prediction.score.NAME) and two additional metadata fields: predicted.NAME
and predicted.NAME.score which contain the class prediction and the score for
that predicted class. For continuous data, an Assay called NAME is returned.
NAME here corresponds to the name of the element in the refdata list.
Stuart T, Butler A, et al. Comprehensive Integration of Single-Cell Data. Cell. 2019;177:1888-1902 doi:10.1016/j.cell.2019.05.031
## Not run: # to install the SeuratData package see https://github.com/satijalab/seurat-data library(SeuratData) data("pbmc3k") # for demonstration, split the object into reference and query pbmc.reference <- pbmc3k[, 1:1350] pbmc.query <- pbmc3k[, 1351:2700] # perform standard preprocessing on each object pbmc.reference <- NormalizeData(pbmc.reference) pbmc.reference <- FindVariableFeatures(pbmc.reference) pbmc.reference <- ScaleData(pbmc.reference) pbmc.query <- NormalizeData(pbmc.query) pbmc.query <- FindVariableFeatures(pbmc.query) pbmc.query <- ScaleData(pbmc.query) # find anchors anchors <- FindTransferAnchors(reference = pbmc.reference, query = pbmc.query) # transfer labels predictions <- TransferData(anchorset = anchors, refdata = pbmc.reference$seurat_annotations) pbmc.query <- AddMetaData(object = pbmc.query, metadata = predictions) ## End(Not run)
## Not run: # to install the SeuratData package see https://github.com/satijalab/seurat-data library(SeuratData) data("pbmc3k") # for demonstration, split the object into reference and query pbmc.reference <- pbmc3k[, 1:1350] pbmc.query <- pbmc3k[, 1351:2700] # perform standard preprocessing on each object pbmc.reference <- NormalizeData(pbmc.reference) pbmc.reference <- FindVariableFeatures(pbmc.reference) pbmc.reference <- ScaleData(pbmc.reference) pbmc.query <- NormalizeData(pbmc.query) pbmc.query <- FindVariableFeatures(pbmc.query) pbmc.query <- ScaleData(pbmc.query) # find anchors anchors <- FindTransferAnchors(reference = pbmc.reference, query = pbmc.query) # transfer labels predictions <- TransferData(anchorset = anchors, refdata = pbmc.reference$seurat_annotations) pbmc.query <- AddMetaData(object = pbmc.query, metadata = predictions) ## End(Not run)
Update pre-V4 Assays generated with SCTransform in the Seurat to the new SCTAssay class
UpdateSCTAssays(object)
UpdateSCTAssays(object)
object |
A Seurat object |
A Seurat object with updated SCTAssays
Find current gene symbols based on old or alias symbols using the gene names database from the HUGO Gene Nomenclature Committee (HGNC)
GeneSymbolThesarus( symbols, timeout = 10, several.ok = FALSE, search.types = c("alias_symbol", "prev_symbol"), verbose = TRUE, ... ) UpdateSymbolList( symbols, timeout = 10, several.ok = FALSE, verbose = TRUE, ... )
GeneSymbolThesarus( symbols, timeout = 10, several.ok = FALSE, search.types = c("alias_symbol", "prev_symbol"), verbose = TRUE, ... ) UpdateSymbolList( symbols, timeout = 10, several.ok = FALSE, verbose = TRUE, ... )
symbols |
A vector of gene symbols |
timeout |
Time to wait before canceling query in seconds |
several.ok |
Allow several current gene symbols for each provided symbol |
search.types |
Type of query to perform:
This parameter accepts multiple options and short-hand options
(eg. “ |
verbose |
Show a progress bar depicting search progress |
... |
Extra parameters passed to |
For each symbol passed, we query the HGNC gene names database for
current symbols that have the provided symbol as either an alias
(alias_symbol
) or old (prev_symbol
) symbol. All other queries
are not supported.
GeneSymbolThesarus
:, if several.ok
, a named list
where each entry is the current symbol found for each symbol provided and
the names are the provided symbols. Otherwise, a named vector with the
same information.
UpdateSymbolList
: symbols
with updated symbols from
HGNC's gene names database
This function requires internet access
https://www.genenames.org/ https://www.genenames.org/help/rest/
## Not run: GeneSybmolThesarus(symbols = c("FAM64A")) ## End(Not run) ## Not run: UpdateSymbolList(symbols = cc.genes$s.genes) ## End(Not run)
## Not run: GeneSybmolThesarus(symbols = c("FAM64A")) ## End(Not run) ## Not run: UpdateSymbolList(symbols = cc.genes$s.genes) ## End(Not run)
View variable features
VariableFeaturePlot( object, cols = c("black", "red"), pt.size = 1, log = NULL, selection.method = NULL, assay = NULL, raster = NULL, raster.dpi = c(512, 512) )
VariableFeaturePlot( object, cols = c("black", "red"), pt.size = 1, log = NULL, selection.method = NULL, assay = NULL, raster = NULL, raster.dpi = c(512, 512) )
object |
Seurat object |
cols |
Colors to specify non-variable/variable status |
pt.size |
Size of the points on the plot |
log |
Plot the x-axis in log scale |
selection.method |
Which method to pull. For
For
|
assay |
Assay to pull variable features from |
raster |
Convert points to raster format, default is |
raster.dpi |
Pixel resolution for rasterized plots, passed to geom_scattermore(). Default is c(512, 512). |
A ggplot object
data("pbmc_small") VariableFeaturePlot(object = pbmc_small)
data("pbmc_small") VariableFeaturePlot(object = pbmc_small)
The VisiumV1 class represents spatial information from the 10X Genomics Visium platform
image
A three-dimensional array with PNG image data, see
readPNG
for more details
scale.factors
An object of class scalefactors
; see
scalefactors
for more information
coordinates
A data frame with tissue coordinate information
spot.radius
Single numeric value giving the radius of the spots
Visualize top genes associated with reduction components
VizDimLoadings( object, dims = 1:5, nfeatures = 30, col = "blue", reduction = "pca", projected = FALSE, balanced = FALSE, ncol = NULL, combine = TRUE )
VizDimLoadings( object, dims = 1:5, nfeatures = 30, col = "blue", reduction = "pca", projected = FALSE, balanced = FALSE, ncol = NULL, combine = TRUE )
object |
Seurat object |
dims |
Number of dimensions to display |
nfeatures |
Number of genes to display |
col |
Color of points to use |
reduction |
Reduction technique to visualize results for |
projected |
Use reduction values for full dataset (i.e. projected dimensional reduction values) |
balanced |
Return an equal number of genes with + and - scores. If FALSE (default), returns the top genes ranked by the scores absolute values |
ncol |
Number of columns to display |
combine |
Combine plots into a single |
A patchwork
ggplot object if
combine = TRUE
; otherwise, a list of ggplot objects
data("pbmc_small") VizDimLoadings(object = pbmc_small)
data("pbmc_small") VizDimLoadings(object = pbmc_small)
Draws a violin plot of single cell data (gene expression, metrics, PC scores, etc.)
VlnPlot( object, features, cols = NULL, pt.size = NULL, idents = NULL, sort = FALSE, assay = NULL, group.by = NULL, split.by = NULL, adjust = 1, y.max = NULL, same.y.lims = FALSE, log = FALSE, ncol = NULL, slot = "data", split.plot = FALSE, stack = FALSE, combine = TRUE, fill.by = "feature", flip = FALSE, add.noise = TRUE, raster = NULL )
VlnPlot( object, features, cols = NULL, pt.size = NULL, idents = NULL, sort = FALSE, assay = NULL, group.by = NULL, split.by = NULL, adjust = 1, y.max = NULL, same.y.lims = FALSE, log = FALSE, ncol = NULL, slot = "data", split.plot = FALSE, stack = FALSE, combine = TRUE, fill.by = "feature", flip = FALSE, add.noise = TRUE, raster = NULL )
object |
Seurat object |
features |
Features to plot (gene expression, metrics, PC scores, anything that can be retreived by FetchData) |
cols |
Colors to use for plotting |
pt.size |
Point size for geom_violin |
idents |
Which classes to include in the plot (default is all) |
sort |
Sort identity classes (on the x-axis) by the average expression of the attribute being potted, can also pass 'increasing' or 'decreasing' to change sort direction |
assay |
Name of assay to use, defaults to the active assay |
group.by |
Group (color) cells in different ways (for example, orig.ident) |
split.by |
A variable to split the violin plots by, |
adjust |
Adjust parameter for geom_violin |
y.max |
Maximum y axis value |
same.y.lims |
Set all the y-axis limits to the same values |
log |
plot the feature axis on log scale |
ncol |
Number of columns if multiple plots are displayed |
slot |
Slot to pull expression data from (e.g. "counts" or "data") |
split.plot |
plot each group of the split violin plots by multiple or single violin shapes. |
stack |
Horizontally stack plots for each feature |
combine |
Combine plots into a single |
fill.by |
Color violins/ridges based on either 'feature' or 'ident' |
flip |
flip plot orientation (identities on x-axis) |
add.noise |
determine if adding a small noise for plotting |
raster |
Convert points to raster format. Requires 'ggrastr' to be installed. |
A patchworked
ggplot object if
combine = TRUE
; otherwise, a list of ggplot objects
data("pbmc_small") VlnPlot(object = pbmc_small, features = 'PC_1') VlnPlot(object = pbmc_small, features = 'LYZ', split.by = 'groups')
data("pbmc_small") VlnPlot(object = pbmc_small, features = 'PC_1') VlnPlot(object = pbmc_small, features = 'LYZ', split.by = 'groups')