Package 'SeuratObject'

Description

Defines S4 classes for single-cell genomic data and associated information, such as dimensionality reduction embeddings, nearest-neighbor graphs, and spatially-resolved coordinates. Provides data access methods and R-native hooks to ensure the Seurat object is familiar to other R users. 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, and Stuart T, Butler A, et al (2019) doi:10.1016/j.cell.2019.05.031 for more details.

Author(s)

Maintainer: Paul Hoffman [email protected] (ORCID)

Authors:

• Rahul Satija [email protected] (ORCID)

• Andrew Butler [email protected] (ORCID)

• Tim Stuart [email protected] (ORCID)

Other contributors:

• Jeff Farrell [email protected] [contributor]

• Shiwei Zheng [email protected] (ORCID) [contributor]

• Christoph Hafemeister [email protected] (ORCID) [contributor]

• Patrick Roelli [email protected] [contributor]

• Yuhan Hao [email protected] (ORCID) [contributor]

See Also

Useful links:

• https://mojaveazure.github.io/seurat-object/

• https://github.com/mojaveazure/seurat-object

• Report bugs at https://github.com/mojaveazure/seurat-object/issues

Description

Adds additional data to the object. Can be any piece of information associated with a cell (examples include read depth, alignment rate, experimental batch, or subpopulation identity) or feature (ENSG name, variance). To add cell level information, add to the Seurat object. If adding feature-level metadata, add to the Assay object (e.g. object[["RNA"]])

Usage

AddMetaData(object, metadata, col.name = NULL)

## S3 method for class 'Assay'
AddMetaData(object, metadata, col.name = NULL)

## S3 method for class 'Seurat'
AddMetaData(object, metadata, col.name = NULL)

Arguments

Value

object with metadata added

Examples

cluster_letters <- LETTERS[Idents(object = pbmc_small)]
names(cluster_letters) <- colnames(x = pbmc_small)
pbmc_small <- AddMetaData(
  object = pbmc_small,
  metadata = cluster_letters,
  col.name = 'letter.idents'
)
head(x = pbmc_small[[]])

Description

Aggregate Molecules into an Expression Matrix

Usage

## S3 method for class 'FOV'
aggregate(x, by = NULL, set = NULL, drop = TRUE, ...)

## S3 method for class 'Molecules'
aggregate(x, by, drop = TRUE, ...)

Arguments

Value

An expression matrix

Progress Updates with progressr

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")

Parallelization with future

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")

Description

Convert Segmentation Layers

Usage

as.Centroids(x, nsides = NULL, radius = NULL, theta = NULL, ...)

as.Segmentation(x, ...)

## S3 method for class 'Segmentation'
as.Centroids(x, nsides = NULL, radius = NULL, theta = NULL, ...)

## S3 method for class 'Centroids'
as.Segmentation(x, ...)

Arguments

Value

as.Centroids: A Centroids object

as.Segmentation: A Segmentation object

Description

Convert a matrix (or Matrix) to a Graph object

Usage

as.Graph(x, ...)

## S3 method for class 'Matrix'
as.Graph(x, ...)

## S3 method for class 'matrix'
as.Graph(x, ...)

## S3 method for class 'Neighbor'
as.Graph(x, weighted = TRUE, ...)

Arguments

Value

A Graph object

Examples

# converting sparse matrix
mat <- Matrix::rsparsematrix(nrow = 10, ncol = 10, density = 0.1)
rownames(x = mat) <- paste0("feature_", 1:10)
colnames(x = mat) <- paste0("cell_", 1:10)
g <- as.Graph(x = mat)

# converting dense matrix
mat <- matrix(data = 1:16, nrow = 4)
rownames(x = mat) <- paste0("feature_", 1:4)
colnames(x = mat) <- paste0("cell_", 1:4)
g <- as.Graph(x = mat)

Description

Convert objects to Neighbor objects

Usage

as.Neighbor(x, ...)

## S3 method for class 'Graph'
as.Neighbor(x, ...)

Arguments

Value

A Neighbor object

Description

Convert objects to Seurat objects

Usage

as.Seurat(x, ...)

Arguments

Value

A Seurat object generated from x

Description

Convert dense objects to sparse representations

Usage

as.sparse(x, ...)

## S3 method for class 'data.frame'
as.sparse(x, row.names = NULL, ...)

## S3 method for class 'Matrix'
as.sparse(x, ...)

## S3 method for class 'matrix'
as.sparse(x, ...)

## S3 method for class 'ngCMatrix'
as.sparse(x, ...)

Arguments

Value

A sparse representation of the input data

Description

The Assay object is the basic unit of Seurat; each Assay stores raw, normalized, and scaled data as well as cluster information, variable features, and any other assay-specific metadata. Assays should contain single cell expression data such as RNA-seq, protein, or imputed expression data.

Slots

counts

Unnormalized data such as raw counts or TPMs

data

Normalized expression data

scale.data

Scaled expression data

key

Key for the Assay

assay.orig

Original assay that this assay is based off of. Used to track assay provenance

var.features

Vector of features exhibiting high variance across single cells

meta.features

Feature-level metadata

misc

Utility slot for storing additional data associated with the assay

See Also

Assay-methods

Description

Methods for Assay objects for generics defined in other packages

Usage

## S3 method for class 'Assay'
x[i, j, ...]

## S3 method for class 'Assay'
x[[i, ..., drop = FALSE]]

## S3 method for class 'Assay'
dim(x)

## S3 method for class 'Assay'
dimnames(x)

## S3 method for class 'Assay'
head(x, n = 10L, ...)

## S3 method for class 'Assay'
merge(x = NULL, y = NULL, add.cell.ids = NULL, merge.data = TRUE, ...)

## S3 method for class 'Assay'
subset(x, cells = NULL, features = NULL, ...)

## S3 method for class 'Assay'
tail(x, n = 10L, ...)

## S4 replacement method for signature 'Assay,ANY,ANY,ANY'
x[[i, j, ...]] <- value

## S4 method for signature 'Assay'
colMeans(x, na.rm = FALSE, dims = 1, ..., slot = "data")

## S4 method for signature 'Assay'
colSums(x, na.rm = FALSE, dims = 1, ..., slot = "data")

## S4 method for signature 'Assay'
rowMeans(x, na.rm = FALSE, dims = 1, ..., slot = "data")

## S4 method for signature 'Assay'
rowSums(x, na.rm = FALSE, dims = 1, ..., slot = "data")

## S4 method for signature 'Assay'
show(object)

Arguments

Value

[: The data slot for features i and cells j

[[: The feature-level metadata for i

dim: The number of features (nrow) and cells (ncol)

dimnames: Feature (row) and cell (column) names

head: The first n rows of feature-level metadata

merge: Merged object

subset: A subsetted Assay

tail: The last n rows of feature-level metadata

[[<-: x with metadata value added as i

colMeans: The column (cell-wise) means of slot

colSums: The column (cell-wise) sums of slot

rowMeans: The row (feature-wise) means of slot

rowSums: The row (feature-wise) sums of slot

show: Prints summary to stdout and invisibly returns NULL

Functions

• [: Get expression data from an Assay

• [[: Get feature-level metadata

• dim(Assay): Number of cells and features for an Assay

• dimnames(Assay): Cell- and feature-names for an Assay

• head(Assay): Get the first rows of feature-level metadata

• merge(Assay): Merge Assay objects

• subset(Assay): Subset an Assay

• tail(Assay): Get the last rows of feature-level metadata

• `[[`(x = Assay, i = ANY, j = ANY) <- value: Add feature-level metadata

• colMeans(Assay): Calculate colMeans on an Assay

• colSums(Assay): Calculate colSums on an Assay

• rowMeans(Assay): Calculate rowMeans on an Assay

• rowSums(Assay): Calculate rowSums on an Assay

• show(Assay): Overview of an Assay object

Description

General accessor and setter functions for Assay objects. GetAssayData can be used to pull information from any of the expression matrices (eg. “counts”, “data”, or “scale.data”). SetAssayData can be used to replace one of these expression matrices

Usage

GetAssayData(object, slot, ...)

SetAssayData(object, slot, new.data, ...)

## S3 method for class 'Seurat'
GetAssayData(object, slot = "data", assay = NULL, ...)

## S3 method for class 'Seurat'
SetAssayData(object, slot = "data", new.data, assay = NULL, ...)

## S3 method for class 'Assay'
GetAssayData(object, slot = c("data", "scale.data", "counts"), ...)

## S3 method for class 'Assay'
SetAssayData(object, slot = c("data", "scale.data", "counts"), new.data, ...)

Arguments

Value

GetAssayData: returns the specified assay data

SetAssayData: object with the assay data set

Examples

# Get assay data from the default assay in a Seurat object
GetAssayData(object = pbmc_small, slot = "data")[1:5,1:5]

# Set an Assay slot through the Seurat object
count.data <- GetAssayData(object = pbmc_small[["RNA"]], slot = "counts")
count.data <- as.matrix(x = count.data + 1)
new.seurat.object <- SetAssayData(
    object = pbmc_small,
    slot = "counts",
    new.data = count.data,
    assay = "RNA"
)

# Get the data directly from an Assay object
GetAssayData(pbmc_small[["RNA"]], slot = "data")[1:5,1:5]

# Set an Assay slot directly
count.data <- GetAssayData(pbmc_small[["RNA"]], slot = "counts")
count.data <- as.matrix(x = count.data + 1)
new.assay <- SetAssayData(pbmc_small[["RNA"]], slot = "counts", new.data = count.data)

Description

List the names of Assay, DimReduc, Graph, Neighbor objects

Usage

Assays(object, slot = NULL)

Graphs(object, slot = NULL)

Neighbors(object, slot = NULL)

Reductions(object, slot = NULL)

Arguments

Value

If slot is NULL, the names of all component objects in this Seurat object. Otherwise, the specific object specified

Examples

Assays(object = pbmc_small)

Graphs(pbmc_small)

Reductions(object = pbmc_small)

Description

Helper function to attach required packages. Detects if a package is already attached and if so, skips it. Should be called in .onAttach

Usage

AttachDeps(deps)

Arguments

Value

Invisibly returns NULL

Examples

# Use in your .onAttach hook
if (FALSE) {
  .onAttach <- function(libname, pkgname) {
    AttachDeps(c("SeuratObject", "rlang"))
  }
}

Description

Get, Set, and Query Segmentation Boundaries

Usage

Boundaries(object, ...)

DefaultBoundary(object)

DefaultBoundary(object, ...) <- value

Molecules(object, ...)

## S3 method for class 'FOV'
Boundaries(object, ...)

## S3 method for class 'FOV'
DefaultBoundary(object)

## S3 replacement method for class 'FOV'
DefaultBoundary(object, ...) <- value

## S3 method for class 'FOV'
Molecules(object, ...)

Arguments

Value

Boundaries: The names of all segmentation boundaries present within object

DefaultBoundary: The name of the default segmentation boundary

DefaultBoundary<-: object with the default segmentation boundary set to value

Molecules: The names of all molecule sets present within object

Description

Get the cell and feature names of an object

Usage

Cells(x, ...)

Features(x, ...)

## Default S3 method:
Cells(x, ...)

## S3 method for class 'DimReduc'
Cells(x, ...)

## S3 method for class 'Neighbor'
Cells(x, ...)

Arguments

Value

Cell: A vector of cell names

Features: A vector of feature names

Examples

Cells(x = pbmc_small)

Description

Get cell names grouped by identity class

Usage

CellsByIdentities(object, idents = NULL, cells = NULL, return.null = FALSE)

Arguments

Value

A named list where names are identity classes and values are vectors of cells belonging to that class

Examples

CellsByIdentities(object = pbmc_small)

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

The Centroids Class

Slots

cells

(character [n]) A vector of cell names; there should be as many cell names as there are points and no duplicate names

nsides

(integer [1L]) The number of sides to draw when plotting centroids; must be either 0L for circles or greater than 3

radius

(numeric [1L]) The radius of the shape when plotting the centroids

theta

(numeric [1L]) The angle in degrees to adjust the shape when plotting the centroids

See Also

Centroids methods: Centroids-methods

Segmentation layer classes: Molecules-class, Segmentation-class

Description

Methods for Centroids objects

Usage

## S3 method for class 'Centroids'
Cells(x, ...)

## S3 method for class 'Centroids'
GetTissueCoordinates(object, full = TRUE, ...)

## S3 method for class 'Centroids'
Radius(object)

## S3 method for class 'Centroids'
RenameCells(object, new.names = NULL, ...)

## S3 method for class 'Centroids'
Theta(object)

## S3 method for class 'Centroids'
is.finite(x)

## S3 method for class 'Centroids'
is.infinite(...)

## S3 method for class 'Centroids'
length(x)

## S3 method for class 'Centroids'
lengths(x, use.names = TRUE)

## S3 method for class 'Centroids'
subset(x, cells = NULL, ...)

## S4 method for signature 'Centroids,character,ANY,ANY'
x[i, j, ..., drop = TRUE]

## S4 method for signature 'Centroids,numeric,ANY,ANY'
x[i, j, ..., drop = TRUE]

## S4 method for signature 'Centroids'
show(object)

Arguments

Details

GetTissueCoordinates: Get cell spatial coordinates

Radius: Get the centroid radius

RenameCells: Update cell names

Theta: Get the offset angle

is.finite, is.infinite: Test to see if the centroids are circular or polygonal

length: Get the number of sides for the polygonal centroid

lengths: Generate a run-length encoding of the cells present

subset, [: Subset a Centroids object to certain cells

show: Display an object summary to stdout

Value

GetTissueCoordinates: A data frame with three columns:

• “x”: the x-coordinate

• “y”: the y-coordinate

• “cell”: the cell name

If full is TRUE, then each coordinate will indicate a vertex for the cell polygon; otherwise, each coordinate will indicate a centroid for the cell

Radius The radius of the centroids

RenameCells: object with the cells renamed to new.names

Theta: The offset angle in degrees

is.finite: TRUE if the centroids are polygonal, FALSE if circular

is.infinite: The opposite of is.finite

length: 0 if the centroids are circular, otherwise the number of sides of the polygonal centroid

lengths: An rle object for the cells

subset, [: x subsetted to the cells specified by cells/i

show: Invisibly returns NULL

See Also

Centroids-class

Description

Call gc only when desired

Usage

CheckGC(option = "SeuratObject.memsafe")

Arguments

Value

Invisibly returns NULL

Description

Pull information on previously run commands in the Seurat object.

Usage

Command(object, ...)

## S3 method for class 'Seurat'
Command(object, command = NULL, value = NULL, ...)

Arguments

Value

Either a SeuratCommand object or the requested parameter value

Description

Create an Assay object from a feature (e.g. gene) expression matrix. The expected format of the input matrix is features x cells.

Usage

CreateAssayObject(
  counts,
  data,
  min.cells = 0,
  min.features = 0,
  check.matrix = FALSE,
  ...
)

Arguments

Details

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

Value

A Assay object

Examples

## Not run: 
pbmc_raw <- read.table(
  file = system.file('extdata', 'pbmc_raw.txt', package = 'Seurat'),
  as.is = TRUE
)
pbmc_rna <- CreateAssayObject(counts = pbmc_raw)
pbmc_rna

## End(Not run)

Create a Centroids Objects

Description

Create a Centroids Objects

Usage

CreateCentroids(coords, nsides, radius, theta)

Arguments

Value

A Centroids object

Description

Create a DimReduc object

Usage

CreateDimReducObject(
  embeddings = new(Class = "matrix"),
  loadings = new(Class = "matrix"),
  projected = new(Class = "matrix"),
  assay = NULL,
  stdev = numeric(),
  key = NULL,
  global = FALSE,
  jackstraw = NULL,
  misc = list()
)

Arguments

Value

A DimReduc object

Examples

data <- GetAssayData(pbmc_small[["RNA"]], slot = "scale.data")
pcs <- prcomp(x = data)
pca.dr <- CreateDimReducObject(
  embeddings = pcs$rotation,
  loadings = pcs$x,
  stdev = pcs$sdev,
  key = "PC",
  assay = "RNA"
)

Description

Create Spatial Coordinates

Usage

CreateFOV(coords, ...)

## S3 method for class 'Centroids'
CreateFOV(
  coords,
  molecules = NULL,
  assay = "Spatial",
  key = NULL,
  name = NULL,
  ...
)

## S3 method for class 'data.frame'
CreateFOV(
  coords,
  type = c("segmentation", "centroids"),
  nsides = Inf,
  radius = NULL,
  theta = 0L,
  molecules = NULL,
  assay = "Spatial",
  key = NULL,
  name = NULL,
  ...
)

## S3 method for class 'list'
CreateFOV(coords, molecules = NULL, assay = "Spatial", key = NULL, ...)

## S3 method for class 'Segmentation'
CreateFOV(
  coords,
  molecules = NULL,
  assay = "Spatial",
  key = NULL,
  name = NULL,
  ...
)

Arguments

Value

A FOV object

See Also

FOV-class

Create a Molecules Object

Description

Create a Molecules Object

Usage

CreateMolecules(coords, ...)

## S3 method for class 'data.frame'
CreateMolecules(coords, key = "", ...)

## S3 method for class 'Molecules'
CreateMolecules(coords, ...)

## S3 method for class ''NULL''
CreateMolecules(coords, ...)

Arguments

Value

A Molecules object

Create a Segmentation Objects

Description

Create a Segmentation Objects

Usage

CreateSegmentation(coords)

## S3 method for class 'data.frame'
CreateSegmentation(coords)

## S3 method for class 'Segmentation'
CreateSegmentation(coords)

Arguments

Value

A Segmentation object

Description

Create a Seurat object from raw data

Usage

CreateSeuratObject(
  counts,
  project = "CreateSeuratObject",
  assay = "RNA",
  names.field = 1,
  names.delim = "_",
  meta.data = NULL,
  ...
)

## Default S3 method:
CreateSeuratObject(
  counts,
  project = "SeuratProject",
  assay = "RNA",
  names.field = 1,
  names.delim = "_",
  meta.data = NULL,
  min.cells = 0,
  min.features = 0,
  row.names = NULL,
  ...
)

## S3 method for class 'Assay'
CreateSeuratObject(
  counts,
  project = "SeuratProject",
  assay = "RNA",
  names.field = 1,
  names.delim = "_",
  meta.data = NULL,
  ...
)

Arguments

Value

A Seurat object

Note

In previous versions (<3.0), this function also accepted a parameter to set the expression threshold for a ‘detected’ feature (gene). This functionality has been removed to simplify the initialization process/assumptions. If you would still like to impose this threshold for your particular dataset, simply filter the input expression matrix before calling this function.

Examples

## Not run: 
pbmc_raw <- read.table(
  file = system.file('extdata', 'pbmc_raw.txt', package = 'Seurat'),
  as.is = TRUE
)
pbmc_small <- CreateSeuratObject(counts = pbmc_raw)
pbmc_small

## End(Not run)

Description

Crop Coordinates

Usage

Crop(object, x = NULL, y = NULL, coords = c("plot", "tissue"), ...)

## S3 method for class 'FOV'
Crop(object, x = NULL, y = NULL, coords = c("plot", "tissue"), ...)

Arguments

Value

object cropped to the region specified by x and y

Description

Get and set the default assay

Usage

DefaultAssay(object, ...)

DefaultAssay(object, ...) <- value

## S3 method for class 'Graph'
DefaultAssay(object, ...)

## S3 replacement method for class 'Graph'
DefaultAssay(object, ...) <- value

## S3 method for class 'Assay'
DefaultAssay(object, ...)

## S3 replacement method for class 'Assay'
DefaultAssay(object, ...) <- value

## S3 method for class 'SeuratCommand'
DefaultAssay(object, ...)

## S3 method for class 'DimReduc'
DefaultAssay(object, ...)

## S3 replacement method for class 'DimReduc'
DefaultAssay(object, ...) <- value

## S3 method for class 'Seurat'
DefaultAssay(object, ...)

## S3 replacement method for class 'Seurat'
DefaultAssay(object, ...) <- value

Arguments

Value

DefaultAssay: The name of the default assay

DefaultAssay<-: An object with the default assay updated

Examples

# Get current default assay
DefaultAssay(object = pbmc_small)

# Create dummy new assay to demo switching default assays
new.assay <- pbmc_small[["RNA"]]
Key(object = new.assay) <- "RNA2_"
pbmc_small[["RNA2"]] <- new.assay
# switch default assay to RNA2
DefaultAssay(object = pbmc_small) <- "RNA2"
DefaultAssay(object = pbmc_small)

Find the default DimReduc

Description

Searches for DimReducs matching “umap”, “tsne”, or “pca”, case-insensitive, and in that order. Priority given to DimReducs matching the DefaultAssay or assay specified (eg. “pca” for the default assay weights higher than “umap” for a non-default assay)

Usage

DefaultDimReduc(object, assay = NULL)

Arguments

Value

The default DimReduc, if possible

Examples

DefaultDimReduc(pbmc_small)

Description

Get and Set the Default FOV

Usage

DefaultFOV(object, ...)

DefaultFOV(object, ...) <- value

## S3 method for class 'Seurat'
DefaultFOV(object, assay = NULL, ...)

## S3 replacement method for class 'Seurat'
DefaultFOV(object, assay = NA, ...) <- value

Arguments

Value

DefaultFOV: The name of the default FOV

DefaultFOV<-: object with the default FOV set to value

Description

The DimReduc object stores a dimensionality reduction taken out in Seurat; each DimReduc consists of a cell embeddings matrix, a feature loadings matrix, and a projected feature loadings matrix.

Slots

cell.embeddings

Cell embeddings matrix (required)

feature.loadings

Feature loadings matrix (optional)

feature.loadings.projected

Projected feature loadings matrix (optional)

assay.used

Name of assay used to generate DimReduc object

global

Is this DimReduc global/persistent? If so, it will not be removed when removing its associated assay

stdev

A vector of standard deviations

key

Key for the DimReduc, must be alphanumeric characters followed by an underscore

jackstraw

A JackStrawData-class object associated with this DimReduc

misc

Utility slot for storing additional data associated with the DimReduc (e.g. the total variance of the PCA)

Description

Methods for DimReduc objects for generics defined in other packages

Usage

## S3 method for class 'DimReduc'
x[i, j, drop = FALSE, ...]

## S3 method for class 'DimReduc'
x[[i, j, drop = FALSE, ...]]

## S3 method for class 'DimReduc'
dim(x)

## S3 method for class 'DimReduc'
dimnames(x)

## S3 method for class 'DimReduc'
length(x)

## S3 method for class 'DimReduc'
merge(x = NULL, y = NULL, add.cell.ids = NULL, ...)

## S3 method for class 'DimReduc'
names(x)

## S3 method for class 'DimReduc'
print(x, dims = 1:5, nfeatures = 20, projected = FALSE, ...)

## S3 method for class 'DimReduc'
subset(x, cells = NULL, features = NULL, ...)

## S4 method for signature 'DimReduc'
show(object)

Arguments

Value

[: Feature loadings for features i and dimensions j

[[: Cell embeddings for cells i and dimensions j

dim: The number of cells (nrow) and dimensions (ncol)

dimnames: The cell (row) and dimension (column) names

length: The number of dimensions

names: The names for the dimensions (eg. “PC_1”)

print: Displays set of features defining the components and invisibly returns x

subset: x for cells cells and features features

show: Prints summary to stdout and invisibly returns NULL

Functions

• [: Pull feature loadings

• [[: Pull cell embeddings

• dim(DimReduc): The number of cells and dimensions for a DimReduc

• dimnames(DimReduc): The cell and dimension names for a DimReduc object

• length(DimReduc): The number of dimensions for a DimReduc object

• merge(DimReduc): Merge two or more DimReduc objects together

• names(DimReduc): The dimension names for a DimReduc object

• print(DimReduc): Prints a set of features that most strongly define a set of components; note: requires feature loadings to be present in order to work

• subset(DimReduc): Subset a DimReduc object

• show(DimReduc): Show basic summary of a DimReduc object

See Also

cat

Description

Get the Neighbor nearest neighbors distance matrix

Usage

Distances(object, ...)

## S3 method for class 'Neighbor'
Distances(object, ...)

Arguments

Value

The distance matrix

Description

Get Cell Embeddings

Usage

Embeddings(object, ...)

## S3 method for class 'DimReduc'
Embeddings(object, ...)

## S3 method for class 'Seurat'
Embeddings(object, reduction = "pca", ...)

Arguments

Value

The embeddings matrix

Examples

# Get the embeddings directly from a DimReduc object
Embeddings(object = pbmc_small[["pca"]])[1:5, 1:5]

# Get the embeddings from a specific DimReduc in a Seurat object
Embeddings(object = pbmc_small, reduction = "pca")[1:5, 1:5]

Description

Retrieves data (feature expression, PCA scores, metrics, etc.) for a set of cells in a Seurat object

Usage

FetchData(object, ...)

## S3 method for class 'DimReduc'
FetchData(
  object,
  vars,
  cells = NULL,
  slot = c("embeddings", "loadings", "projected"),
  ...
)

## S3 method for class 'Seurat'
FetchData(object, vars, cells = NULL, slot = "data", ...)

Arguments

Value

A data frame with cells as rows and cellular data as columns

Examples

pc1 <- FetchData(object = pbmc_small, vars = 'PC_1')
head(x = pc1)
head(x = FetchData(object = pbmc_small, vars = c('groups', 'ident')))

Description

Get the names of objects within a Seurat object that are of a certain class

Usage

FilterObjects(object, classes.keep = c("Assay", "DimReduc"))

Arguments

Value

A vector with the names of objects within the Seurat object that are of class classes.keep

Examples

FilterObjects(pbmc_small)

Description

A modern container for storing coordinates of spatially-resolved single cells. Capable of storing multiple cell segmentation boundary masks. Supports coordinates for spatially-resolved molecule (FISH) data. Compatible with SpatialImage

Slots

molecules

(list) A named list of Molecules objects defining spatially-resolved molecular coordinates

boundaries

([named]list {Segmentation, Centroids}) A named list of Segmentation and Centroids objects defining spatially-resolved boundaries

assay

(character [1L]) A character naming the associated assay of the spatial coordinates

key

(character [1L]) The key for the spatial coordinates

See Also

FOV-methods

Description

Methods for FOV objects

Usage

## S3 method for class 'FOV'
Cells(x, boundary = NULL, ...)

## S3 method for class 'FOV'
Features(x, set = NULL, ...)

## S3 method for class 'FOV'
FetchData(object, vars, cells = NULL, simplify = TRUE, ...)

## S3 method for class 'FOV'
GetTissueCoordinates(object, which = NULL, ...)

## S3 method for class 'FOV'
Keys(object, ...)

## S3 method for class 'FOV'
RenameCells(object, new.names = NULL, ...)

## S3 method for class 'FOV'
x$i, ...

## S3 method for class 'FOV'
x[i, j, ...]

## S3 method for class 'FOV'
x[[i, ...]]

## S3 method for class 'FOV'
length(x)

## S3 method for class 'FOV'
names(x)

## S3 method for class 'FOV'
subset(x, cells = NULL, features = NULL, ...)

## S4 replacement method for signature 'FOV,character,missing,Centroids'
x[[i, j, ...]] <- value

## S4 replacement method for signature 'FOV,character,missing,Molecules'
x[[i, j, ...]] <- value

## S4 replacement method for signature 'FOV,character,missing,'NULL''
x[[i, j, ...]] <- value

## S4 replacement method for signature 'FOV,character,missing,Segmentation'
x[[i, j, ...]] <- value

## S4 method for signature 'FOV'
show(object)

Arguments

Details

The following methods are defined for interacting with a FOV object:

Cells: Get cell names

Features: Get spatially-resolved molecule names

FetchData: Fetch boundary and/or molecule coordinates from a FOV object

GetTissueCoordinates: Get boundary or molecule coordinates from a FOV object

Keys: Get the keys of molecule sets contained within a FOV object

RenameCells: Update cell names

$, [[: Extract a segmentation boundary

length: Get the number of segmentation layers in a FOV object

names: Get the names of segmentation layers and molecule sets

subset, [: Subset a FOV object

[[<-: Add or remove segmentation layers and molecule information to/from a FOV object

show: Display an object summary to stdout

Value

Cells: A vector of cell names

Features: A vector of spatially-resolved molecule names; if no molecular information present, returns NULL

FetchData: If both molecule and boundary coordinates are requested, then a two-length list:

• “molecules”: A data frame with the molecule coordinates requested. If molecules requested are keyed, the keys are preserved in the data frame

• “coordinates”: A data frame with coordinates from the segmentation boundaries requested

If simplify is TRUE and only one data frame is generated, then only the data frame is returned. Otherwise, a one-length list is returned with the single data frame generated

GetTissueCoordinates: ...

Keys: A named vector of molecule set keys; names are the names of the molecule sets and values are the keys for the respective molecule set

RenameCells: object with the cells renamed to new.names

$, [[: The segmentation boundary or spatially-resolved molecule information stored at i

length: The number of segmentation layers (Segmentation or Centroids objects)

names: A vector of segmentation boundary and molecule set names

subset: x with just the cells and features specified

[[<-: Varies depending on the class of value:

• If value is NULL, returns x with the boundary i removed; also allows removing molecules; does not allow removing the default segmentation

• If value is a Molecules, returns x with value stored in molecules; requires that i is “molecules”

• Otherwise, stores value as a segmentation boundary named i

show: Invisibly returns NULL

See Also

FOV-class

Description

Get image data

Usage

GetImage(object, mode = c("grob", "raster", "plotly", "raw"), ...)

## S3 method for class 'Seurat'
GetImage(
  object,
  mode = c("grob", "raster", "plotly", "raw"),
  image = NULL,
  ...
)

Arguments

Value

Image data, varying depending on the value of mode:

“grob”

An object representing image data inheriting from grob objects (eg. rastergrob)

“raster”

An object of class raster

“plotly”

A list with image data suitable for Plotly rendering, see plotly::layout for more details

“raw”

The raw image data as stored in the object

See Also

layout

Description

Get tissue coordinates

Usage

GetTissueCoordinates(object, ...)

## S3 method for class 'Seurat'
GetTissueCoordinates(object, image = NULL, ...)

Arguments

Value

A data frame with tissue coordinates

Description

The Graph class inherits from dgCMatrix. We do this to enable future expandability of graphs.

Slots

assay.used

Optional name of assay used to generate Graph object

See Also

dgCMatrix-class

Description

Get and set variable feature information for an Assay object. HVFInfo and VariableFeatures utilize generally variable features, while SVFInfo and SpatiallyVariableFeatures are restricted to spatially variable features

Usage

HVFInfo(object, selection.method, status = FALSE, ...)

VariableFeatures(object, selection.method = NULL, ...)

VariableFeatures(object, ...) <- value

SVFInfo(object, selection.method, status, ...)

SpatiallyVariableFeatures(object, selection.method, ...)

## S3 method for class 'Seurat'
HVFInfo(object, selection.method = NULL, status = FALSE, assay = NULL, ...)

## S3 method for class 'Seurat'
VariableFeatures(object, selection.method = NULL, assay = NULL, ...)

## S3 replacement method for class 'Seurat'
VariableFeatures(object, assay = NULL, ...) <- value

## S3 method for class 'Seurat'
SVFInfo(
  object,
  selection.method = c("markvariogram", "moransi"),
  status = FALSE,
  assay = NULL,
  ...
)

## S3 method for class 'Seurat'
SpatiallyVariableFeatures(
  object,
  selection.method = "markvariogram",
  assay = NULL,
  decreasing = TRUE,
  ...
)

## S3 method for class 'Assay'
HVFInfo(object, selection.method, status = FALSE, ...)

## S3 method for class 'Assay'
SpatiallyVariableFeatures(
  object,
  selection.method = "markvariogram",
  decreasing = TRUE,
  ...
)

## S3 method for class 'Assay'
SVFInfo(
  object,
  selection.method = c("markvariogram", "moransi"),
  status = FALSE,
  ...
)

## S3 method for class 'Assay'
VariableFeatures(object, selection.method = NULL, ...)

## S3 replacement method for class 'Assay'
VariableFeatures(object, ...) <- value

Arguments

Value

HVFInfo: A data frame with feature means, dispersion, and scaled dispersion

VariableFeatures: a vector of the variable features

SVFInfo: a data frame with the spatially variable features

SpatiallyVariableFeatures: a character vector of the spatially variable features

Examples

# Get the HVF info from a specific Assay in a Seurat object
HVFInfo(object = pbmc_small, assay = "RNA")[1:5, ]

# Get the HVF info directly from an Assay object
HVFInfo(pbmc_small[["RNA"]], selection.method = 'vst')[1:5, ]

Description

Get, set, and manipulate an object's identity classes

Usage

Idents(object, ...)

Idents(object, ...) <- value

RenameIdents(object, ...)

ReorderIdent(object, var, ...)

SetIdent(object, ...)

StashIdent(object, save.name, ...)

## S3 method for class 'Seurat'
Idents(object, ...)

## S3 replacement method for class 'Seurat'
Idents(object, cells = NULL, drop = FALSE, ...) <- value

## S3 method for class 'Seurat'
ReorderIdent(
  object,
  var,
  reverse = FALSE,
  afxn = mean,
  reorder.numeric = FALSE,
  ...
)

## S3 method for class 'Seurat'
RenameIdents(object, ...)

## S3 method for class 'Seurat'
SetIdent(object, cells = NULL, value, ...)

## S3 method for class 'Seurat'
StashIdent(object, save.name = "orig.ident", ...)

## S3 method for class 'Seurat'
droplevels(x, ...)

## S3 method for class 'Seurat'
levels(x)

## S3 replacement method for class 'Seurat'
levels(x) <- value

Arguments

Value

Idents: The cell identities

Idents<-: object with the cell identities changed

RenameIdents: An object with selected identity classes renamed

ReorderIdent: An object with

SetIdent: An object with new identity classes set

StashIdent: An object with the identities stashed

Examples

# Get cell identity classes
Idents(pbmc_small)

# Set cell identity classes
# Can be used to set identities for specific cells to a new level
Idents(pbmc_small, cells = 1:4) <- 'a'
head(Idents(pbmc_small))

# Can also set idents from a value in object metadata
colnames(pbmc_small[[]])
Idents(pbmc_small) <- 'RNA_snn_res.1'
levels(pbmc_small)

# Rename cell identity classes
# Can provide an arbitrary amount of idents to rename
levels(pbmc_small)
pbmc_small <- RenameIdents(pbmc_small, '0' = 'A', '2' = 'C')
levels(pbmc_small)

## Not run: 
head(Idents(pbmc_small))
pbmc_small <- ReorderIdent(pbmc_small, var = 'PC_1')
head(Idents(pbmc_small))

## End(Not run)

# Set cell identity classes using SetIdent
cells.use <- WhichCells(pbmc_small, idents = '1')
pbmc_small <- SetIdent(pbmc_small, cells = cells.use, value = 'B')

head(pbmc_small[[]])
pbmc_small <- StashIdent(pbmc_small, save.name = 'idents')
head(pbmc_small[[]])

# Get the levels of identity classes of a Seurat object
levels(x = pbmc_small)

# Reorder identity classes
levels(x = pbmc_small)
levels(x = pbmc_small) <- c('C', 'A', 'B')
levels(x = pbmc_small)

Description

List the names of SpatialImage objects present in a Seurat object. If assay is provided, limits search to images associated with that assay

Usage

Images(object, assay = NULL)

Arguments

Value

A list of image names

Examples

## Not run: 
Images(object)

## End(Not run)

Description

Get Neighbor algorithm index

Usage

Index(object, ...)

Index(object, ...) <- value

## S3 method for class 'Neighbor'
Index(object, ...)

## S3 replacement method for class 'Neighbor'
Index(object, ...) <- value

Arguments

Value

Returns the value in the alg.idx slot of the Neighbor object

Idents<-: A Neighbor object with the index stored

Description

Get Neighbor nearest neighbor index matrices

Usage

Indices(object, ...)

## S3 method for class 'Neighbor'
Indices(object, ...)

Arguments

Value

A matrix with the nearest neighbor indices

Description

Typically, when removing Assay objects from an Seurat object, all associated objects (eg. DimReduc, Graph, and SeuratCommand objects) are removed as well. If an associated object is marked as global/persistent, the associated object will remain even if its original assay was deleted

Usage

IsGlobal(object, ...)

## Default S3 method:
IsGlobal(object, ...)

## S3 method for class 'DimReduc'
IsGlobal(object, ...)

Arguments

Value

TRUE if the object is global/persistent otherwise FALSE

Examples

IsGlobal(pbmc_small[['pca']])

Description

Takes a matrix and asks if it's empty (either 0x0 or 1x1 with a value of NA)

Usage

IsMatrixEmpty(x)

Arguments

Value

Whether or not x is empty

Examples

IsMatrixEmpty(new("matrix"))
IsMatrixEmpty(matrix())
IsMatrixEmpty(matrix(1:3))

Description

Check to see if a list has names; also check to enforce that all names are present and unique

Usage

IsNamedList(x, all.unique = TRUE, allow.empty = FALSE, pass.zero = FALSE)

Arguments

Value

TRUE if ..., otherwise FALSE

Description

The JackStrawData is used to store the results of a JackStraw computation.

Slots

empirical.p.values

Empirical p-values

fake.reduction.scores

Fake reduction scores

empirical.p.values.full

Empirical p-values on full

overall.p.values

Overall p-values from ScoreJackStraw

Description

Methods for JackStrawData objects for generics defined in other packages

Usage

## S3 method for class 'JackStrawData'
.DollarNames(x, pattern = "")

## S3 method for class 'JackStrawData'
x$i, ...

## S3 method for class 'JackStrawData'
as.logical(x, ...)

## S4 method for signature 'JackStrawData'
show(object)

Arguments

Value

$: Slot i from x

as.logical: TRUE if empirical p-values have been calculated otherwise FALSE

show: Prints summary to stdout and invisibly returns NULL

Functions

• .DollarNames(JackStrawData): Autocompletion for $ access on a JackStrawData object

• $: Access data from a JackStrawData object

• as.logical(JackStrawData): Have empirical p-values for a JackStrawData object been calculated

• show(JackStrawData): Overview of a JackStrawData object

Description

Get and set JackStraw information

Usage

JS(object, ...)

JS(object, ...) <- value

## S3 method for class 'JackStrawData'
JS(object, slot, ...)

## S3 replacement method for class 'JackStrawData'
JS(object, slot, ...) <- value

## S3 method for class 'DimReduc'
JS(object, slot = NULL, ...)

## S3 replacement method for class 'DimReduc'
JS(object, slot = NULL, ...) <- value

Arguments

Value

JS: either a JackStrawData object or the specified jackstraw data

JS<-: object with the update jackstraw information

Description

Get and set object keys

Usage

Key(object, ...)

Key(object, ...) <- value

Keys(object, ...)

## S3 method for class 'Assay'
Key(object, ...)

## S3 replacement method for class 'Assay'
Key(object, ...) <- value

## S3 method for class 'DimReduc'
Key(object, ...)

## S3 replacement method for class 'DimReduc'
Key(object, ...) <- value

## S3 method for class 'Seurat'
Key(object, ...)

## S3 method for class 'Seurat'
Keys(object, ...)

Arguments

Value

Key: the object key

Key<-: object with an updated key

Keys: a named vector of keys of sub-objects

Examples

# Get an Assay key
Key(pbmc_small[["RNA"]])

# Set the key for an Assay
Key(pbmc_small[["RNA"]]) <- "newkey_"
Key(pbmc_small[["RNA"]])

# Get a DimReduc key
Key(object = pbmc_small[["pca"]])

# Set the key for DimReduc
Key(object = pbmc_small[["pca"]]) <- "newkey2_"
Key(object = pbmc_small[["pca"]])

# Show all keys associated with a Seurat object
Key(object = pbmc_small)
Keys(object = pbmc_small)

Description

Get and set feature loadings

Usage

Loadings(object, ...)

Loadings(object, ...) <- value

## S3 method for class 'DimReduc'
Loadings(object, projected = FALSE, ...)

## S3 replacement method for class 'DimReduc'
Loadings(object, projected = TRUE, ...) <- value

## S3 method for class 'Seurat'
Loadings(object, reduction = "pca", projected = FALSE, ...)

Arguments

Value

Loadings: the feature loadings for object

Loadings<-: object with the updated loadings

Examples

# Get the feature loadings for a given DimReduc
Loadings(object = pbmc_small[["pca"]])[1:5,1:5]

# Set the feature loadings for a given DimReduc
new.loadings <- Loadings(object = pbmc_small[["pca"]])
new.loadings <- new.loadings + 0.01
Loadings(object = pbmc_small[["pca"]]) <- new.loadings

# Get the feature loadings for a specified DimReduc in a Seurat object
Loadings(object = pbmc_small, reduction = "pca")[1:5,1:5]

Description

A simple container for storing mappings of values using logical matrices. Keeps track of which values (rows) are present in which observations (columns). LogMap objects can be created with LogMap(); queries can be performed with [[ and observations can be added or removed with [[<-

Usage

LogMap(y)

## S4 method for signature 'LogMap,character,missing'
x[[i, j, ...]]

## S4 method for signature 'LogMap,missing,missing'
x[[i, j, ...]]

## S4 method for signature 'LogMap,'NULL',missing'
x[[i, j, ...]]

## S4 replacement method for signature 'LogMap,character,missing,character'
x[[i, j, ...]] <- value

## S4 replacement method for signature 'LogMap,character,missing,integer'
x[[i, j, ...]] <- value

## S4 replacement method for signature 'LogMap,character,missing,'NULL''
x[[i, j, ...]] <- value

## S4 replacement method for signature 'LogMap,character,missing,numeric'
x[[i, j, ...]] <- value

## S4 method for signature 'LogMap'
show(object)

Arguments

Value

LogMap: A new LogMap object with zero columns and length(x = x) rows; rownames are set to x

[[: if i is a character vector, the rownames that are mapped to i; otherwise the rownames of x

[[<-: If value is NULL, then x without the observations for i; otherwise, x with a new column for i recording a TRUE for all values present in value

Slots

.Data

A logical matrix with at least one row

Examples

# Create a LogMap
map <- LogMap(letters[1:10])
map

# Get the names of values in the LogMap
map[[NULL]]
rownames(map)

# Add an observation to the LogMap
map[['obs']] <- c(1, 3, 7)
map

# Get the names of observations in the LogMap
colnames(map)

# Fetch an observation from the LogMap
map[['obs']]

# Get the full logical matrix
map[[]]

# Remove an observation from the LogMap
map[['obs']] <- NULL
map

Description

Logs command run, storing the name, timestamp, and argument list. Stores in the Seurat object

Usage

LogSeuratCommand(object, return.command = FALSE)

Arguments

Value

If return.command, returns a SeuratCommand object. Otherwise, returns the Seurat object with command stored

See Also

Command

Description

Match Cells

Usage

MatchCells(new, orig, ordered = FALSE)

## S3 method for class 'character'
MatchCells(new, orig, ordered = FALSE)

## S3 method for class ''NULL''
MatchCells(new, orig, ordered = FALSE)

## S3 method for class 'numeric'
MatchCells(new, orig, ordered = FALSE)

Arguments

Value

A numeric vector with new cells in order of the original cells; if no match can be found, returns NULL

Description

Get and set miscellaneous data

Usage

Misc(object, ...)

Misc(object, ...) <- value

## S3 method for class 'Assay'
Misc(object, slot = NULL, ...)

## S3 replacement method for class 'Assay'
Misc(object, slot, ...) <- value

## S3 method for class 'DimReduc'
Misc(object, slot = NULL, ...)

## S3 replacement method for class 'DimReduc'
Misc(object, slot, ...) <- value

## S3 method for class 'Seurat'
Misc(object, slot = NULL, ...)

## S3 replacement method for class 'Seurat'
Misc(object, slot, ...) <- value

Arguments

Value

Miscellaneous data

An object with miscellaneous data added

Examples

# Get the misc info
Misc(object = pbmc_small, slot = "example")

# Add misc info
Misc(object = pbmc_small, slot = "example") <- "testing_misc"

Description

The Spatial Molecules Class

Slots

.Data

A list of SpatialPoints objects

key

The key for the Molecules

See Also

Molecules methods: Molecules-methods

Segmentation layer classes: Centroids-class, Segmentation-class

Description

Methods for Molecules objects

Usage

## S3 method for class 'Molecules'
Features(x, ...)

## S3 method for class 'Molecules'
GetTissueCoordinates(object, features = NULL, ...)

## S3 method for class 'Molecules'
subset(x, features = NULL, ...)

## S4 method for signature 'Molecules'
show(object)

Arguments

Details

Features: Get spatially-resolved molecule names

GetTissueCoordinates: Get spatially-resolved molecule coordinates

subset: Subset a Molecules object to certain molecules

show: Display an object summary to stdout

Value

Features: A vector of spatially-resolved molecule names; if no molecular information present, returns NULL

GetTissueCoordinates: A data frame with three columns:

• “x”: the x-coordinate of a molecule

• “y”: the y-coordinate of a molecule

• “molecule”: the molecule name

subset: x subsetted to the features specified by features

show: Invisibly returns NULL

See Also

Molecules-class

Description

The Neighbor class is used to store the results of neighbor finding algorithms

Slots

nn.idx

Matrix containing the nearest neighbor indices

nn.dist

Matrix containing the nearest neighbor distances

alg.idx

The neighbor finding index (if applicable). E.g. the annoy index

alg.info

Any information associated with the algorithm that may be needed downstream (e.g. distance metric used with annoy is needed when reading in from stored file).

cell.names

Names of the cells for which the neighbors have been computed.

Description

Methods for Neighbor objects for generics defined in other packages

Usage

## S3 method for class 'Neighbor'
dim(x)

## S4 method for signature 'Neighbor'
show(object)

Arguments

Value

dim Dimensions of the indices matrix

show: Prints summary to stdout and invisibly returns NULL

Functions

• dim(Neighbor): Dimensions of the neighbor indices

• show(Neighbor): Overview of a Neighbor object

Description

Create an overlay of some query spatial object (x) against some target object (y). Basically, find all components of a query that fall within the bounds of a target spatial region

Usage

Overlay(x, y, invert = FALSE, ...)

## S4 method for signature 'Centroids,SpatialPolygons'
Overlay(x, y, invert = FALSE, ...)

## S4 method for signature 'Segmentation,SpatialPolygons'
Overlay(x, y, invert = FALSE, ...)

## S4 method for signature 'Molecules,SpatialPolygons'
Overlay(x, y, invert = FALSE, ...)

## S4 method for signature 'FOV,Spatial'
Overlay(x, y, invert = FALSE, ...)

## S4 method for signature 'FOV,SpatialPolygons'
Overlay(x, y, invert = FALSE, ...)

## S4 method for signature 'FOV,FOV'
Overlay(x, y, invert = FALSE, ...)

Arguments

Value

x with only the components that fall within the bounds of y

Description

Check the existence of a package

Usage

PackageCheck(..., error = TRUE)

Arguments

Value

Invisibly returns boolean denoting if the package is installed

Examples

PackageCheck("SeuratObject", error = FALSE)

Description

A subsetted version of 10X Genomics' 3k PBMC dataset

Usage

pbmc_small

Format

A Seurat object with the following slots filled

assays

Currently only contains one assay ("RNA" - scRNA-seq expression data)

• counts - Raw expression data

• data - Normalized expression data

• scale.data - Scaled expression data

• var.features - names of the current features selected as variable

• meta.features - Assay level metadata such as mean and variance

meta.data

Cell level metadata

active.assay

Current default assay

active.ident

Current default idents

graphs

Neighbor graphs computed, currently stores the SNN

reductions

Dimensional reductions: currently PCA and tSNE

version

Seurat version used to create the object

commands

Command history

Source

https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.1.0/pbmc3k

Description

Get and set project information

Usage

Project(object, ...)

Project(object, ...) <- value

## S3 method for class 'Seurat'
Project(object, ...)

## S3 replacement method for class 'Seurat'
Project(object, ...) <- value

Arguments

Value

Project information

An object with project information added

Description

Get the spot radius from an image

Usage

Radius(object)

Arguments

Value

The radius size

Description

Make a name from randomly sampled lowercase letters, pasted together with no spaces or other characters

Usage

RandomName(length = 5L, ...)

Arguments

Value

A character with nchar == length of randomly sampled letters

See Also

sample

Examples

set.seed(42L)
RandomName()
RandomName(7L, replace = TRUE)

Description

Rename assays in a Seurat object

Usage

RenameAssays(object, ...)

Arguments

Value

object with assays renamed

Examples

RenameAssays(object = pbmc_small, RNA = 'rna')

Description

Change the cell names in all the different parts of an object. Can be useful before combining multiple objects.

Usage

RenameCells(object, ...)

## S3 method for class 'Assay'
RenameCells(object, new.names = NULL, ...)

## S3 method for class 'DimReduc'
RenameCells(object, new.names = NULL, ...)

## S3 method for class 'Neighbor'
RenameCells(object, old.names = NULL, new.names = NULL, ...)

## S3 method for class 'Seurat'
RenameCells(
  object,
  add.cell.id = NULL,
  new.names = NULL,
  for.merge = FALSE,
  ...
)

Arguments

Details

If add.cell.id is set a prefix is added to existing cell names. If new.names is set these will be used to replace existing names.

Value

An object with new cell names

Examples

# Rename cells in an Assay
head(x = colnames(x = pbmc_small[["RNA"]]))
renamed.assay <- RenameCells(
    pbmc_small[["RNA"]],
    new.names = paste0("A_", colnames(x = pbmc_small[["RNA"]]))
)
head(x = colnames(x = renamed.assay))

# Rename cells in a DimReduc
head(x = Cells(x = pbmc_small[["pca"]]))
renamed.dimreduc <- RenameCells(
    object = pbmc_small[["pca"]],
    new.names = paste0("A_", Cells(x = pbmc_small[["pca"]]))
)
head(x = Cells(x = renamed.dimreduc))

# Rename cells in a Seurat object
head(x = colnames(x = pbmc_small))
pbmc_small <- RenameCells(object = pbmc_small, add.cell.id = "A")
head(x = colnames(x = pbmc_small))

Description

Merge two or more sparse matrices by rowname.

Usage

RowMergeSparseMatrices(mat1, mat2)

Arguments

Details

Shared matrix rows (with the same row name) will be merged, and unshared rows (with different names) will be filled with zeros in the matrix not containing the row.

Value

Returns a sparse matrix

Description

Convert S4 objects to lists and vice versa. Useful for declassing an S4 object while keeping track of it's class using attributes (see section S4 Class Definition Attributes below for more details). Both ListToS4 and S4ToList are recursive functions, affecting all lists/S4 objects contained as sub-lists/sub-objects.

Usage

S4ToList(object)

IsS4List(x)

ListToS4(x)

## Default S3 method:
S4ToList(object)

## S3 method for class 'list'
S4ToList(object)

Arguments

Value

S4ToList: A list with an S4 class definition attribute

IsS4List: TRUE if x is a list with an S4 class definition attribute

ListToS4: An S4 object as defined by the S4 class definition attribute

S4 Class Definition Attributes

S4 classes are scoped to the package and class name. In order to properly track which class a list is generated from in order to build a new one, these function use an attribute to denote the class name and package of origin. This attribute is stored as “classDef” and takes the form of “package:class”.

Description

The Segmentation Class

See Also

Segmentation methods: Segmentation-methods

Segmentation layer classes: Centroids-class, Molecules-class

Description

Methods for Segmentation objects

Usage

## S3 method for class 'Segmentation'
Cells(x, ...)

## S3 method for class 'Segmentation'
GetTissueCoordinates(object, full = TRUE, ...)

## S3 method for class 'Segmentation'
RenameCells(object, new.names = NULL, ...)

## S3 method for class 'Segmentation'
lengths(x, use.names = TRUE)

## S3 method for class 'Segmentation'
subset(x, cells = NULL, ...)

## S4 method for signature 'Segmentation,ANY,ANY,ANY'
x[i, j, ..., drop = TRUE]

## S4 method for signature 'Segmentation'
coordinates(obj, full = TRUE, ...)

## S4 method for signature 'Segmentation'
show(object)

Arguments

Details

Cells: Get cell names

GetTissueCoordinates, coordinates: Get tissue coordinates

RenameCells: Update cell names

lengths: Generate a run-length encoding of the cells present

subset, [: Subset a Segmentation object to certain cells

show: Display an object summary to stdout

Value

Cells: A vector of cell names

GetTissueCoordinates, coordinates: A data frame with three columns:

• “x”: the x-coordinate

• “y”: the y-coordinate

• “cell” or “ID”: the cell name

If full is TRUE, then each coordinate will indicate a vertex for the cell polygon; otherwise, each coordinate will indicate a centroid for the cell. Note: GetTissueCoordinates ....

RenameCells: object with the cells renamed to new.names

lengths: An rle object for the cells

subset, [: x subsetted to the cells specified by cells/i

show: Invisibly returns NULL

Progress Updates with progressr

The following methods use progressr to render status updates and progress bars:

• RenameCells

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")

Parallelization with future

The following methods use future to enable parallelization:

• RenameCells

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")

See Also

Segmentation-class

Description

Set a default value depending on if an object is NA

Usage

x %NA% y

x %na% y

x %!NA% y

x %!na% y

Arguments

Value

For %NA%: y if x is NA; otherwise x

For %!NA%: y if x is not NA; otherwise x

Examples

1 %NA% 2
NA %NA% 2

1 %!NA% 2
NA %!NA% 2

Description

The Seurat object is a representation of single-cell expression data for R; each Seurat object revolves around a set of cells and consists of one or more Assay objects, or individual representations of expression data (eg. RNA-seq, ATAC-seq, etc). These assays can be reduced from their high-dimensional state to a lower-dimension state and stored as DimReduc objects. Seurat objects also store additional metadata, both at the cell and feature level (contained within individual assays). The object was designed to be as self-contained as possible, and easily extendable to new methods.

Slots

assays

A list of assays for this project

meta.data

Contains meta-information about each cell, starting with number of features detected (nFeature) and the original identity class (orig.ident); more information is added using AddMetaData

active.assay

Name of the active, or default, assay; settable using DefaultAssay

active.ident

The active cluster identity for this Seurat object; settable using Idents

graphs

A list of Graph objects

neighbors

...

reductions

A list of dimensional reduction objects for this object

images

A list of spatial image objects

project.name

Name of the project

misc

A list of miscellaneous information

version

Version of Seurat this object was built under

commands

A list of logged commands run on this Seurat object

tools

A list of miscellaneous data generated by other tools, should be filled by developers only using Tool<-

Description

Methods for Seurat objects for generics defined in other packages

Usage

## S3 method for class 'Seurat'
.DollarNames(x, pattern = "")

## S3 method for class 'Seurat'
x$i, ...

## S3 replacement method for class 'Seurat'
x$i, ... <- value

## S3 method for class 'Seurat'
x[i, j, ...]

## S3 method for class 'Seurat'
x[[i, ..., drop = FALSE]]

## S3 method for class 'Seurat'
dim(x)

## S3 method for class 'Seurat'
dimnames(x)

## S3 method for class 'Seurat'
head(x, n = 10L, ...)

## S3 method for class 'Seurat'
merge(
  x = NULL,
  y = NULL,
  add.cell.ids = NULL,
  merge.data = TRUE,
  merge.dr = NULL,
  project = "SeuratProject",
  ...
)

## S3 method for class 'Seurat'
names(x)

## S3 method for class 'Seurat'
subset(
  x,
  subset,
  cells = NULL,
  features = NULL,
  idents = NULL,
  return.null = FALSE,
  ...
)

## S3 method for class 'Seurat'
tail(x, n = 10L, ...)

## S4 replacement method for signature 'Seurat,ANY,ANY,ANY'
x[[i, j, ...]] <- value

## S4 method for signature 'Seurat'
colMeans(x, na.rm = FALSE, dims = 1, ..., slot = "data")

## S4 method for signature 'Seurat'
colSums(x, na.rm = FALSE, dims = 1, ..., slot = "data")

## S4 method for signature 'Seurat'
rowMeans(x, na.rm = FALSE, dims = 1, ..., slot = "data")

## S4 method for signature 'Seurat'
rowSums(x, na.rm = FALSE, dims = 1, ..., slot = "data")

## S4 method for signature 'Seurat'
show(object)

Arguments

Value

$: metadata column i for object x; note: unlike [[, $ drops the shape of the metadata to return a vector instead of a data frame

$<-: object x with metadata value saved as i

[: object x with features i and cells j

[[: If i is missing, the metadata data frame; if i is a vector of metadata names, a data frame with the requested metadata, otherwise, the requested associated object

dim: The number of features (nrow) and cells (ncol) for the default assay; note: while the number of features changes depending on the active assay, the number of cells remains the same across all assays

dimnames: The feature (row) and cell (column) names; note: while the features change depending on the active assay, the cell names remain the same across all assays

head: The first n rows of cell-level metadata

merge: Merged object

names: The names of all Assay, DimReduc, Graph, and SpatialImage objects in the Seurat object

subset: A subsetted Seurat object

tail: The last n rows of cell-level metadata

[[<-: x with the metadata or associated objects added as i; if value is NULL, removes metadata or associated object i from object x

show: Prints summary to stdout and invisibly returns NULL

Functions

• .DollarNames(Seurat): Autocompletion for $ access on a Seurat object

• $: Metadata access for Seurat objects

• `$`(Seurat) <- value: Metadata setter for Seurat objects

• [: Simple subsetter for Seurat objects

• [[: Metadata and associated object accessor

• dim(Seurat): Number of cells and features for the active assay

• dimnames(Seurat): The cell and feature names for the active assay

• head(Seurat): Get the first rows of cell-level metadata

• merge(Seurat): Merge two or more Seurat objects together

• names(Seurat): Common associated objects

• subset(Seurat): Subset a Seurat object

• tail(Seurat): Get the last rows of cell-level metadata

• `[[`(x = Seurat, i = ANY, j = ANY) <- value: Add cell-level metadata or associated objects

• colMeans(Seurat): Calculate colMeans on a Seurat object

• colSums(Seurat): Calculate colSums on a Seurat object

• rowMeans(Seurat): Calculate rowMeans on a rowMeans object

• rowSums(Seurat): Calculate rowSums on a Seurat object

• show(Seurat): Overview of a Seurat object

Merge Details

When merging Seurat objects, the merge procedure will merge the Assay level counts and potentially the data slots (depending on the merge.data parameter). It will also merge the cell-level meta data that was stored with each object and preserve the cell identities that were active in the objects pre-merge. The merge will optionally merge reductions depending on the values passed to merge.dr if they have the same name across objects. Here the embeddings slots will be merged and if there are differing numbers of dimensions across objects, only the first N shared dimensions will be merged. The feature loadings slots will be filled by the values present in the first object.The merge will not preserve graphs, logged commands, or feature-level metadata that were present in the original objects. If add.cell.ids isn't specified and any cell names are duplicated, cell names will be appended with _X, where X is the numeric index of the object in c(x, y).

See Also

subset WhichCells

Examples

# Get metadata using `$'
head(pbmc_small$groups)

# Add metadata using the `$' operator
set.seed(42)
pbmc_small$value <- sample(1:3, size = ncol(pbmc_small), replace = TRUE)
head(pbmc_small[["value"]])

# `[' examples
pbmc_small[VariableFeatures(object = pbmc_small), ]
pbmc_small[, 1:10]

# Get the cell-level metadata data frame
head(pbmc_small[[]])

# Pull specific metadata information
head(pbmc_small[[c("letter.idents", "groups")]])
head(pbmc_small[["groups", drop = TRUE]])

# Get a sub-object (eg. an `Assay' or `DimReduc')
pbmc_small[["RNA"]]
pbmc_small[["pca"]]

# Get the number of features in an object
nrow(pbmc_small)

# Get the number of cells in an object
ncol(pbmc_small)

# Get the feature names of an object
rownames(pbmc_small)

# Get the cell names of an object
colnames(pbmc_small)

# Get the first 10 rows of cell-level metadata
head(pbmc_small)

# `merge' examples
# merge two objects
merge(pbmc_small, y = pbmc_small)
# to merge more than two objects, pass one to x and a list of objects to y
merge(pbmc_small, y = c(pbmc_small, pbmc_small))

names(pbmc_small)

# `subset' examples
subset(pbmc_small, subset = MS4A1 > 4)
subset(pbmc_small, subset = `DLGAP1-AS1` > 2)
subset(pbmc_small, idents = '0', invert = TRUE)
subset(pbmc_small, subset = MS4A1 > 3, slot = 'counts')
subset(pbmc_small, features = VariableFeatures(object = pbmc_small))

# Get the last 10 rows of cell-level metadata
tail(pbmc_small)

head(colMeans(pbmc_small))

head(colSums(pbmc_small))

head(rowMeans(pbmc_small))

head(rowSums(pbmc_small))

Description

The SeuratCommand is used for logging commands that are run on a Seurat object; it stores parameters and timestamps

Slots

name

Command name

time.stamp

Timestamp of when command was tun

assay.used

Optional name of assay used to generate SeuratCommand object

call.string

String of the command call

params

List of parameters used in the command call

Description

Methods for SeuratCommand objects for generics defined in other packages

Usage

## S3 method for class 'SeuratCommand'
.DollarNames(x, pattern = "")

## S3 method for class 'SeuratCommand'
x$i, ...

## S3 method for class 'SeuratCommand'
x[i, ...]

## S3 method for class 'SeuratCommand'
as.list(x, complete = FALSE, ...)

## S4 method for signature 'SeuratCommand'
show(object)

Arguments