Package 'Seurat'

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.

Package options

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

Author(s)

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]

See Also

Useful links:

• https://satijalab.org/seurat

• https://github.com/satijalab/seurat

• Report bugs at https://github.com/satijalab/seurat/issues

Description

Add mapping and prediction scores, UMAP embeddings, and imputed assay (if available) from Azimuth to an existing or new Seurat object

Usage

AddAzimuthResults(object = NULL, filename)

Arguments

Value

object with Azimuth results added

Examples

## Not run: 
object <- AddAzimuthResults(object, filename = "azimuth_results.Rds")

## End(Not run)

Description

Add mapping and prediction scores from Azimuth to a Seurat object

Usage

AddAzimuthScores(object, filename)

Arguments

Value

object with the mapping scores added

Examples

## Not run: 
object <- AddAzimuthScores(object, filename = "azimuth_pred.tsv")

## End(Not run)

Description

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.

Usage

AddModuleScore(
  object,
  features,
  pool = NULL,
  nbin = 24,
  ctrl = 100,
  k = FALSE,
  assay = NULL,
  name = "Cluster",
  seed = 1,
  search = FALSE,
  ...
)

Arguments

Value

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

References

Tirosh et al, Science (2016)

Examples

## 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)

Description

Returns aggregated (summed) expression values for each identity class

Usage

AggregateExpression(
  object,
  assays = NULL,
  features = NULL,
  return.seurat = FALSE,
  group.by = "ident",
  add.ident = NULL,
  slot = "data",
  verbose = TRUE,
  ...
)

Arguments

Details

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.

Value

Returns a matrix with genes as rows, identity classes as columns. If return.seurat is TRUE, returns an object of class Seurat.

Examples

data("pbmc_small")
head(AggregateExpression(object = pbmc_small))

Description

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).

Slots

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

Description

Add info to anchor matrix

Usage

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,
  ...
)

Arguments

Value

Returns the anchor dataframe with additional columns for annotation metadata

Description

Convert objects to CellDataSet objects

Usage

as.CellDataSet(x, ...)

## S3 method for class 'Seurat'
as.CellDataSet(x, assay = NULL, reduction = NULL, ...)

Arguments

Description

Convert objects to Seurat objects

Usage

## 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",
  ...
)

Arguments

Value

A Seurat object generated from x

See Also

SeuratObject::as.Seurat

Description

Convert objects to SingleCellExperiment objects

Usage

as.SingleCellExperiment(x, ...)

## S3 method for class 'Seurat'
as.SingleCellExperiment(x, assay = NULL, ...)

Arguments

Description

Cast to Sparse

Usage

## 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)
)

Arguments

Value

as.data.frame.Matrix: A data frame representation of the S4 Matrix

See Also

SeuratObject::as.sparse

Description

The Assay object is the basic unit of Seurat; for more details, please see the documentation in SeuratObject

See Also

SeuratObject::Assay-class

Description

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.

Usage

AugmentPlot(plot, width = 10, height = 10, dpi = 100)

Arguments

Value

A ggplot object

Examples

## Not run: 
data("pbmc_small")
plot <- DimPlot(object = pbmc_small)
AugmentPlot(plot = plot)

## End(Not run)

Description

It happens to look good

Usage

AutoPointSize(data, raster = NULL)

Arguments

Value

The "optimal" point size for visualizing these data

Examples

df <- data.frame(x = rnorm(n = 10000), y = runif(n = 10000))
AutoPointSize(data = df)

Description

Returns averaged expression values for each identity class

Usage

AverageExpression(
  object,
  assays = NULL,
  features = NULL,
  return.seurat = FALSE,
  group.by = "ident",
  add.ident = NULL,
  slot = "data",
  verbose = TRUE,
  ...
)

Arguments

Details

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.

Value

Returns a matrix with genes as rows, identity classes as columns. If return.seurat is TRUE, returns an object of class Seurat.

Examples

data("pbmc_small")
head(AverageExpression(object = pbmc_small))

Description

This function plots the calculated inflection points derived from the barcode-rank distribution.

Usage

BarcodeInflectionsPlot(object)

Arguments

Details

See [CalculateBarcodeInflections()] to calculate inflection points and [SubsetByBarcodeInflections()] to subsequently subset the Seurat object.

Value

Returns a 'ggplot2' object showing the by-group inflection points and provided (or default) rank threshold values in grey.

Author(s)

Robert A. Amezquita, [email protected]

See Also

CalculateBarcodeInflections SubsetByBarcodeInflections

Examples

data("pbmc_small")
pbmc_small <- CalculateBarcodeInflections(pbmc_small, group.column = 'groups')
BarcodeInflectionsPlot(pbmc_small)

Description

Determine text color based on background color

Usage

BGTextColor(
  background,
  threshold = 186,
  w3c = FALSE,
  dark = "black",
  light = "white"
)

Arguments

Value

A named vector of either dark or light, depending on background; names of vector are background

Source

https://stackoverflow.com/questions/3942878/how-to-decide-font-color-in-white-or-black-depending-on-background-color

Examples

BGTextColor(background = c('black', 'white', '#E76BF3'))

Description

Creates a custom color palette based on low, middle, and high color values

Usage

BlackAndWhite(mid = NULL, k = 50)

BlueAndRed(k = 50)

CustomPalette(low = "white", high = "red", mid = NULL, k = 50)

PurpleAndYellow(k = 50)

Arguments

Value

A color palette for plotting

Examples

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())

Description

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.

Usage

BuildClusterTree(
  object,
  assay = NULL,
  features = NULL,
  dims = NULL,
  reduction = "pca",
  graph = NULL,
  slot = "data",
  reorder = FALSE,
  reorder.numeric = FALSE,
  verbose = TRUE
)

Arguments

Details

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.

Value

A Seurat object where the cluster tree can be accessed with Tool

Examples

if (requireNamespace("ape", quietly = TRUE)) {
  data("pbmc_small")
  pbmc_small
  pbmc_small <- BuildClusterTree(object = pbmc_small)
  Tool(object = pbmc_small, slot = 'BuildClusterTree')
}

Description

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.

Usage

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
)

Arguments

Value

Returns a Seurat object with a new assay added containing the perturbation signature for all cells in the data slot.

Description

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.

Usage

CalculateBarcodeInflections(
  object,
  barcode.column = "nCount_RNA",
  group.column = "orig.ident",
  threshold.low = NULL,
  threshold.high = NULL
)

Arguments

Details

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.

Value

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

Author(s)

Robert A. Amezquita, [email protected]

See Also

BarcodeInflectionsPlot SubsetByBarcodeInflections

Examples

data("pbmc_small")
CalculateBarcodeInflections(pbmc_small, group.column = 'groups')

Description

Match the case of character vectors

Usage

CaseMatch(search, match)

Arguments

Value

Values from search present in match with the case of match

Examples

data("pbmc_small")
cd_genes <- c('Cd79b', 'Cd19', 'Cd200')
CaseMatch(search = cd_genes, match = rownames(x = pbmc_small))

Description

A list of genes used in cell-cycle regression

Usage

cc.genes

Format

A list of two vectors

s.genes

Genes associated with S-phase

g2m.genes

Genes associated with G2M-phase

Source

https://www.science.org/doi/abs/10.1126/science.aad0501

Description

A list of genes used in cell-cycle regression, updated with 2019 symbols

Usage

cc.genes.updated.2019

Format

A list of two vectors

s.genes

Genes associated with S-phase

g2m.genes

Genes associated with G2M-phase

Updated symbols

The following symbols were updated from cc.genes

s.genes

• MCM2: MCM7

• MLF1IP: CENPU

• RPA2: POLR1B

• BRIP1: MRPL36

g2m.genes

• FAM64A: PIMREG

• HN1: JPT1

Source

https://www.science.org/doi/abs/10.1126/science.aad0501

See Also

cc.genes

Examples

## 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)

Description

Score cell cycle phases

Usage

CellCycleScoring(
  object,
  s.features,
  g2m.features,
  ctrl = NULL,
  set.ident = FALSE,
  ...
)

Arguments

Value

A Seurat object with the following columns added to object meta data: S.Score, G2M.Score, and Phase

See Also

AddModuleScore

Examples

## 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)

Description

Get Cell Names

Usage

## 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, ...)

Arguments

See Also

SeuratObject::Cells

Description

Get a vector of cell names associated with an image (or set of images)

Usage

CellsByImage(object, images = NULL, unlist = FALSE)

Arguments

Value

A vector of cell names

Examples

## Not run: 
CellsByImage(object = object, images = "slice1")

## End(Not run)

Description

Creates a plot of scatter plot of features across two single cells. Pearson correlation between the two cells is displayed above the plot.

Usage

CellScatter(
  object,
  cell1,
  cell2,
  features = NULL,
  highlight = NULL,
  cols = NULL,
  pt.size = 1,
  smooth = FALSE,
  raster = NULL,
  raster.dpi = c(512, 512)
)

Arguments

Value

A ggplot object

Examples

data("pbmc_small")
CellScatter(object = pbmc_small, cell1 = 'ATAGGAGAAACAGA', cell2 = 'CATCAGGATGCACA')

Description

Select points on a scatterplot and get information about them

Usage

CellSelector(plot, object = NULL, ident = "SelectedCells", ...)

FeatureLocator(plot, ...)

Arguments

Value

If object is NULL, the names of the points selected; otherwise, a Seurat object with the selected cells identity classes set to ident

See Also

DimPlot FeaturePlot

Examples

## 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)

Description

Move outliers towards center on dimension reduction plot

Usage

CollapseEmbeddingOutliers(
  object,
  reduction = "umap",
  dims = 1:2,
  group.by = "ident",
  outlier.sd = 2,
  reduction.key = "UMAP_"
)

Arguments

Value

Returns a DimReduc object with the modified embeddings

Examples

## 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)

Description

Valuable for CITE-seq analyses, where we typically spike in rare populations of 'negative control' cells from a different species.

Usage

CollapseSpeciesExpressionMatrix(
  object,
  prefix = "HUMAN_",
  controls = "MOUSE_",
  ncontrols = 100
)

Arguments

Value

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.

Examples

## Not run: 
cbmc.rna.collapsed <- CollapseSpeciesExpressionMatrix(cbmc.rna)

## End(Not run)

Description

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.

Usage

ColorDimSplit(
  object,
  node,
  left.color = "red",
  right.color = "blue",
  other.color = "grey50",
  ...
)

Arguments

Value

Returns a DimPlot

See Also

DimPlot

Examples

if (requireNamespace("ape", quietly = TRUE)) {
  data("pbmc_small")
  pbmc_small <- BuildClusterTree(object = pbmc_small, verbose = FALSE)
  PlotClusterTree(pbmc_small)
  ColorDimSplit(pbmc_small, node = 5)
}

Description

Combine ggplot2-based plots into a single plot

Usage

CombinePlots(plots, ncol = NULL, legend = NULL, ...)

Arguments

Value

A combined plot

Examples

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]]))
)

Description

Get the intensity and/or luminance of a color

Usage

Intensity(color)

Luminance(color)

Arguments

Value

A vector of intensities/luminances for each color

Source

https://stackoverflow.com/questions/3942878/how-to-decide-font-color-in-white-or-black-depending-on-background-color

Examples

Intensity(color = c('black', 'white', '#E76BF3'))

Luminance(color = c('black', 'white', '#E76BF3'))

Description

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.

Usage

CreateSCTAssayObject(
  counts,
  data,
  scale.data = NULL,
  umi.assay = "RNA",
  min.cells = 0,
  min.features = 0,
  SCTModel.list = NULL
)

Arguments

Details

Non-unique cell or feature names are not allowed. Please make unique before calling this function.

Description

Run a custom distance function on an input data matrix

Usage

CustomDistance(my.mat, my.function, ...)

Arguments

Value

A distance matrix

Author(s)

Jean Fan

Examples

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)

Description

DE and EnrichR pathway visualization barplot

Usage

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,
  ...
)

Arguments

Value

Returns one (only enriched) or two (both enriched and depleted) barplots with the top enriched/depleted GO terms from EnrichR.

Description

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.

Usage

DietSeurat(
  object,
  counts = TRUE,
  data = TRUE,
  scale.data = FALSE,
  features = NULL,
  assays = NULL,
  dimreducs = NULL,
  graphs = NULL,
  misc = TRUE
)

Arguments

Description

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.

Usage

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, ...)

Arguments

Value

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

See Also

image geom_raster

Examples

data("pbmc_small")
DimHeatmap(object = pbmc_small)

Description

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).

Usage

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, ...)

Arguments

Value

A patchworked ggplot object if combine = TRUE; otherwise, a list of ggplot objects

Note

For the old do.hover and do.identify functionality, please see HoverLocator and CellSelector, respectively.

See Also

FeaturePlot HoverLocator CellSelector FetchData

Examples

data("pbmc_small")
DimPlot(object = pbmc_small)
DimPlot(object = pbmc_small, split.by = 'ident')

Description

The DimReduc object stores a dimensionality reduction taken out in Seurat; for more details, please see the documentation in SeuratObject

See Also

SeuratObject::DimReduc-class

Description

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.

Usage

DiscretePalette(n, palette = NULL, shuffle = FALSE)

Arguments

Details

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

Value

A vector of colors

Description

Draws a heatmap of single cell feature expression.

Usage

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
)

Arguments

Value

A patchworked ggplot object if combine = TRUE; otherwise, a list of ggplot objects

Examples

data("pbmc_small")
DoHeatmap(object = pbmc_small)

Description

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).

Usage

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
)

Arguments

Value

A ggplot object

See Also

RColorBrewer::brewer.pal.info

Examples

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')

Description

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

Usage

ElbowPlot(object, ndims = 20, reduction = "pca")

Arguments

Value

A ggplot object

Examples

data("pbmc_small")
ElbowPlot(object = pbmc_small)

Description

Calculate mean of logged values in non-log space (return answer in log-space)

Usage

ExpMean(x, ...)

Arguments

Value

Returns the mean in log-space

Examples

ExpMean(x = c(1, 2, 3))

Description

Calculate SD of logged values in non-log space (return answer in log-space)

Usage

ExpSD(x)

Arguments

Value

Returns the standard deviation in log-space

Examples

ExpSD(x = c(1, 2, 3))

Description

Calculate variance of logged values in non-log space (return answer in log-space)

Usage

ExpVar(x)

Arguments

Value

Returns the variance in log-space

Examples

ExpVar(x = c(1, 2, 3))

Description

Performs row scaling and/or centering. Equivalent to using t(scale(t(mat))) in R except in the case of NA values.

Usage

FastRowScale(mat, center = TRUE, scale = TRUE, scale_max = 10)

Arguments

Value

Returns the center/scaled matrix

Description

Colors single cells on a dimensional reduction plot according to a 'feature' (i.e. gene expression, PC scores, number of genes detected, etc.)

Usage

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)
)

Arguments

Value

A patchworked ggplot object if combine = TRUE; otherwise, a list of ggplot objects

Note

For the old do.hover and do.identify functionality, please see HoverLocator and CellSelector, respectively.

See Also

DimPlot HoverLocator CellSelector

Examples

data("pbmc_small")
FeaturePlot(object = pbmc_small, features = 'PC_1')

Description

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.

Usage

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
)

Arguments

Value

A ggplot object

Examples

data("pbmc_small")
FeatureScatter(object = pbmc_small, feature1 = 'CD9', feature2 = 'CD3E')

Description

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.

Usage

FilterSlideSeq(
  object,
  image = "image",
  center = NULL,
  radius = NULL,
  do.plot = TRUE
)

Arguments

Value

Returns a Seurat object with only the subset of cells that pass the circular filter

Examples

## 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)

Description

Finds markers (differentially expressed genes) for each of the identity classes in a dataset

Usage

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,
  ...
)

Arguments

Value

Matrix containing a ranked list of putative markers, and associated statistics (p-values, ROC score, etc.)

Examples

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)

Description

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!

Usage

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,
  ...
)

Arguments

Details

To run Leiden algorithm, you must first install the leidenalg python package (e.g. via pip install leidenalg), see Traag et al (2018).

Value

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

Description

Finds markers that are conserved between the groups

Usage

FindConservedMarkers(
  object,
  ident.1,
  ident.2 = NULL,
  grouping.var,
  assay = "RNA",
  slot = "data",
  min.cells.group = 3,
  meta.method = metap::minimump,
  verbose = TRUE,
  ...
)

Arguments

Value

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.

Examples

## 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)

Description

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.

Usage

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
)

Arguments

Details

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.

Value

Returns an AnchorSet object that can be used as input to IntegrateData.

References

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

Examples

## 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)

Description

Finds markers (differentially expressed genes) for identity classes

Usage

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,
  ...
)

Arguments

Details

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.

Value

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

References

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

See Also

FoldChange

Examples

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)
}

Description

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.

Usage

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
)

Arguments

Value

Seurat object containing a nearest-neighbor object, KNN graph, and SNN graph - each based on a weighted combination of modalities.

Description

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.

Usage

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,
  ...
)

Arguments

Value

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.

Examples

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)

Description

Identify features whose variability in expression can be explained to some degree by spatial location.

Usage

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,
  ...
)

Arguments

Description

Find subclusters under one cluster

Usage

FindSubCluster(
  object,
  cluster,
  graph.name,
  subcluster.name = "sub.cluster",
  resolution = 0.5,
  algorithm = 1
)

Arguments

Value

return a object with sub cluster labels in the sub-cluster.name variable

Description

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.

Usage

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
)

Arguments