Title:	Data Structures for Single Cell Data
Version:	5.1.0
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>, Hao Y, Hao S, et al (2021) <doi:10.1016/j.cell.2021.04.048> and Hao Y, et al (2023) <doi:10.1101/2022.02.24.481684> for more details.
License:	MIT + file LICENSE
URL:	https://satijalab.github.io/seurat-object/, https://github.com/satijalab/seurat-object
BugReports:	https://github.com/satijalab/seurat-object/issues
Additional_repositories:	https://bnprks.r-universe.dev
Depends:	R (≥ 4.1.0), sp (≥ 1.5.0)
Imports:	future, future.apply, generics, grDevices, grid, lifecycle, Matrix (≥ 1.6.4), methods, progressr, Rcpp (≥ 1.0.5), rlang (≥ 0.4.7), spam, stats, tools, utils
Suggests:	BPCells, DelayedArray, fs (≥ 1.5.2), ggplot2, HDF5Array, rmarkdown, sf (≥ 1.0.0), testthat
LinkingTo:	Rcpp, RcppEigen
Config/Needs/website:	pkgdown
BuildManual:	true
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
Collate:	'RcppExports.R' 'zzz.R' 'generics.R' 'keymixin.R' 'graph.R' 'default.R' 'assay.R' 'logmap.R' 'layers.R' 'assay5.R' 'centroids.R' 'command.R' 'compliance.R' 'data.R' 'jackstraw.R' 'dimreduc.R' 'segmentation.R' 'molecules.R' 'spatial.R' 'fov.R' 'neighbor.R' 'seurat.R' 'sparse.R' 'utils.R'
NeedsCompilation:	yes
Packaged:	2025-04-22 18:55:17 UTC; root
Author:	Paul Hoffman [aut], Rahul Satija [aut, cre], David Collins [aut], Yuhan Hao [aut], Austin Hartman [aut], Gesmira Molla [aut], Andrew Butler [aut], Tim Stuart [aut], Madeline Kowalski [ctb], Saket Choudhary [ctb], Skylar Li [ctb], Longda Jiang [ctb], Jeff Farrell [ctb], Shiwei Zheng [ctb], Christoph Hafemeister [ctb], Patrick Roelli [ctb]
Maintainer:	Rahul Satija <seurat@nygenome.org>
Repository:	CRAN
Date/Publication:	2025-04-22 19:40:01 UTC

SeuratObject: Data Structures for Single Cell Data

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, Hao Y, Hao S, et al (2021) doi:10.1016/j.cell.2021.04.048 and Hao Y, et al (2023) doi:10.1101/2022.02.24.481684 for more details.

Author(s)

Maintainer: Rahul Satija seurat@nygenome.org (ORCID)

Authors:

• Paul Hoffman hoff0792@alumni.umn.edu (ORCID)

• David Collins dcollins@nygenome.org (ORCID)

• Yuhan Hao yhao@nygenome.org (ORCID)

• Austin Hartman ahartman@nygenome.org (ORCID)

• Gesmira Molla gmolla@nygenome.org (ORCID)

• Andrew Butler abutler@nygenome.org (ORCID)

• Tim Stuart tstuart@nygenome.org (ORCID)

Other contributors:

• Madeline Kowalski mkowalski@nygenome.org (ORCID) [contributor]

• Saket Choudhary schoudhary@nygenome.org (ORCID) [contributor]

• Skylar Li sli@nygenome.org [contributor]

• Longda Jiang ljiang@nygenome.org (ORCID) [contributor]

• Jeff Farrell jfarrell@g.harvard.edu [contributor]

• Shiwei Zheng szheng@nygenome.org (ORCID) [contributor]

• Christoph Hafemeister chafemeister@nygenome.org (ORCID) [contributor]

• Patrick Roelli proelli@nygenome.org [contributor]

See Also

Useful links:

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

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

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

Layer Data

Description

Get and set layer data

Usage

## S3 method for class 'Assay'
x$i

## S3 replacement method for class 'Assay'
x$i <- value

Arguments

Value

$: Layer data for layer i

$<-: x with layer data value saved as i

See Also

v3 Assay object, validity, and interaction methods: Assay-class, Assay-validity, CreateAssayObject(), [.Assay(), [[.Assay(), dim.Assay(), dimnames.Assay(), merge.Assay(), split.Assay(), subset.Assay()

Examples

rna <- pbmc_small[["RNA"]]

# Fetch a layer with `$`
rna$data[1:10, 1:4]

# Add a layer with `$`
rna$data <- rna$counts
rna$data[1:10, 1:4]

Layer Data

Description

Get and set layer data

Usage

## S3 method for class 'Assay5'
x$i

## S3 replacement method for class 'Assay5'
x$i <- value

Arguments

Value

$: Layer data for layer i

$<-: x with layer data value saved as i

See Also

v5 Assay object, validity, and interaction methods: Assay5-class, Assay5-validity, [.Assay5(), [[.Assay5(), dim.Assay5(), dimnames.Assay5(), merge.Assay5(), split.Assay5(), subset.Assay5()

Cell-Level Meta Data

Description

Get and set cell-level meta data

Usage

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

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

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

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

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

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

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

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

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

$<-: x with metadata value saved as i

See Also

Seurat object, validity, and interaction methods Seurat-class, Seurat-validity, [[.Seurat(), [[<-,Seurat, [[<-,Seurat,NULL, dim.Seurat(), dimnames.Seurat(), merge.Seurat(), names.Seurat(), subset.Seurat()

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"]])

Command Log Parameter Access

Description

Pull parameter values from a SeuratCommand object

Usage

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

Arguments

Value

The value for parameter i

See Also

Command log object and interaction methods .DollarNames.SeuratCommand(), LogSeuratCommand(), SeuratCommand-class, [.SeuratCommand(), as.list.SeuratCommand()

Examples

cmd <- pbmc_small[["NormalizeData.RNA"]]
cmd$normalization.method

Layer Data

Description

Get and set layer data

Usage

## S3 method for class 'StdAssay'
x$i

## S3 replacement method for class 'StdAssay'
x$i <- value

Arguments

Value

$: Layer data for layer i

$<-: x with layer data value saved as i

See Also

v5 Standard Assay object, validity, and interaction methods .DollarNames.StdAssay(), StdAssay-class, StdAssay-validity, [.StdAssay(), [[.StdAssay(), dim.StdAssay(), dimnames.StdAssay(), show,StdAssay-method, split.StdAssay(), subset.StdAssay()

Assay Class Label

Description

Assay Class Label

Usage

.AssayClass(object)

## S3 method for class 'StdAssay'
.AssayClass(object)

## Default S3 method:
.AssayClass(object)

Arguments

Value

The assay class label for object

BPCells Matrix Mode

Description

Get the mode (on-disk, in-memory) of an IterableMatrix object from BPCells

Usage

.BPMatrixMode(object, simplify = FALSE)

Arguments

Value

One of the following, depending on the mode of object:

• “memory”

• “file”

• “directory”

If simplify is TRUE, returns “disk” instead of “file” or “directory”

Calculate nCount and nFeature

Description

Calculate nCount and nFeature

Usage

.CalcN(object, ...)

Arguments

Value

A named list with ...

Examples

calcn <- .CalcN(pbmc_small[["RNA"]])
head(as.data.frame(calcn))

Check Feature Margin

Description

Check Feature Margin

Usage

.CheckFmargin(fmargin)

Arguments

Value

fmargin

Examples

.CheckFmargin(1L)
.CheckFmargin(2.3)

# Error if `fmargin` is outside of [1:2]
if (FALSE) {
  .CheckFmargin(3L)
}

Get the Package that Defines a Class

Description

Get the Package that Defines a Class

Usage

.ClassPkg(object)

## Default S3 method:
.ClassPkg(object)

## S3 method for class 'DelayedArray'
.ClassPkg(object)

## S3 method for class 'R6'
.ClassPkg(object)

## S3 method for class 'R6ClassGenerator'
.ClassPkg(object)

Arguments

Value

The package that defines the class of object

Examples

.ClassPkg(pbmc_small)

Identify Object Collections

Description

Find all collection (named lists) slots in an S4 object

Usage

.Collections(object, exclude = character(length = 0L), ...)

Arguments

Value

A character vector of names of collection slots

See Also

.FilterObjects(), .FindObject(), .Subobjects()

Examples

.Collections(pbmc_small)

Get Parent S4 Classes

Description

Get Parent S4 Classes

Usage

.Contains(object)

Arguments

Value

A vector of class names that object inherits from

Examples

.Contains(pbmc_small)

Generic Assay Creation

Description

Create an assay object; runs a standardized filtering scheme that works regardless of the direction of the data (eg. cells as columns and features as rows or vice versa) and creates an assay object based on the initialization scheme defined for StdAssay-derived class type

Usage

.CreateStdAssay(
  counts,
  min.cells = 0,
  min.features = 0,
  cells = NULL,
  features = NULL,
  transpose = FALSE,
  type = "Assay5",
  ...
)

## Default S3 method:
.CreateStdAssay(
  counts,
  min.cells = 0,
  min.features = 0,
  cells = NULL,
  features = NULL,
  transpose = FALSE,
  type = "Assay5",
  layer = "counts",
  ...
)

## S3 method for class 'list'
.CreateStdAssay(
  counts,
  min.cells = 0,
  min.features = 0,
  cells = NULL,
  features = NULL,
  transpose = FALSE,
  type = "Assay5",
  csum = Matrix::colSums,
  fsum = Matrix::rowSums,
  ...
)

## S3 method for class 'Matrix'
.CreateStdAssay(
  counts,
  min.cells = 0,
  min.features = 0,
  cells = NULL,
  features = NULL,
  transpose = FALSE,
  type = "Assay5",
  layer = "counts",
  ...
)

## S3 method for class 'matrix'
.CreateStdAssay(
  counts,
  min.cells = 0,
  min.features = 0,
  cells = NULL,
  features = NULL,
  transpose = FALSE,
  type = "Assay5",
  layer = "counts",
  ...
)

Arguments

Value

An object of class type with a layer named layer containing the data found in counts

Find the Default FOV

Description

Attempts to find the “default” FOV using the revamped spatial framework

Usage

.DefaultFOV(object, assay = NULL)

Arguments

Value

...

Deprecate Functions and Arguments

Description

Provides automatic deprecation and defunctation of functions and arguments;

Usage

.Deprecate(
  when,
  what,
  with = NULL,
  ...,
  pkg = NULL,
  env = missing_arg(),
  user_env = missing_arg()
)

Arguments

Value

Run for its side effect and invisibly returns NULL

See Also

lifecycle::deprecate_soft() lifecycle::deprecate_warn() lifecycle::deprecate_stop()

Disk Loading Function

Description

Generate a function to load a matrix from an on-disk file

Usage

.DiskLoad(x)

## Default S3 method:
.DiskLoad(x)

## S3 method for class ''10xMatrixH5''
.DiskLoad(x)

## S3 method for class 'AnnDataMatrixH5'
.DiskLoad(x)

## S3 method for class 'DelayedMatrix'
.DiskLoad(x)

## S3 method for class 'H5ADMatrix'
.DiskLoad(x)

## S3 method for class 'HDF5Matrix'
.DiskLoad(x)

## S3 method for class 'IterableMatrix'
.DiskLoad(x)

## S3 method for class 'MatrixDir'
.DiskLoad(x)

## S3 method for class 'MatrixH5'
.DiskLoad(x)

## S3 method for class 'TileDBMatrix'
.DiskLoad(x)

Arguments

Value

A one-length character that defines a function to load a matrix from a file

Dollar-sign Autocompletion

Description

Autocompletion for $ access on an Assay object

Usage

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

Arguments

Value

The layer name matches for pattern

See Also

utils::.DollarNames

Dollar-sign Autocompletion

Description

Autocompletion for $ access on an Assay5 object

Usage

## S3 method for class 'Assay5'
.DollarNames(x, pattern = "")

Arguments

Value

The layer name matches for pattern

See Also

utils::.DollarNames

Dollar-sign Autocompletion

Description

Autocompletion for $ access on a Seurat object

Usage

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

Arguments

Value

The meta data matches for pattern

See Also

utils::.DollarNames

Dollar-sign Autocompletion

Description

Autocompletion for $ access on a SeuratCommand object

Usage

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

Arguments

Value

The parameter name matches for pattern

See Also

Command log object and interaction methods $.SeuratCommand(), LogSeuratCommand(), SeuratCommand-class, [.SeuratCommand(), as.list.SeuratCommand()

Dollar-sign Autocompletion

Description

Autocompletion for $ access on an Assay5 object

Usage

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

Arguments

Value

The layer name matches for pattern

See Also

v5 Standard Assay object, validity, and interaction methods $.StdAssay(), StdAssay-class, StdAssay-validity, [.StdAssay(), [[.StdAssay(), dim.StdAssay(), dimnames.StdAssay(), show,StdAssay-method, split.StdAssay(), subset.StdAssay()

Move Files and Directories

Description

Move files and directories with fs; includes a handler for when path is a directory on a different filesystem than new_path by explicitly copying and deleting path

Usage

.FileMove(path, new_path, overwrite = FALSE, n = 1L)

Arguments

Value

The new path (invisibly).

Note

This function requires the fs package to be installed

See Also

fs::file_move()

Find a File Path

Description

Find a File Path

Usage

.FilePath(x)

## Default S3 method:
.FilePath(x)

## S3 method for class 'DelayedMatrix'
.FilePath(x)

## S3 method for class 'IterableMatrix'
.FilePath(x)

Arguments

Value

The path to the file that backs x; if x is not a file-backed object, returns NULL

Find Subobjects Of A Certain Class

Description

Find Subobjects Of A Certain Class

Usage

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

Arguments

Value

A vector of object names that are of class classes.keep

See Also

.Collections(), .FindObject(), .Subobjects()

Examples

.FilterObjects(pbmc_small)
.FilterObjects(pbmc_small, "Graph")

Find A Subobject

Description

Determine the slot that a subobject is contained in

Usage

.FindObject(object, name, exclude = c("misc", "tools"))

Arguments

Value

The name of the slot that contains name; returns NULL if a subobject named name cannot be found

See Also

.Collections(), .FilterObjects(), .Subobjects()

Examples

.FindObject(pbmc_small, "tsne")

Returns the most frequently observed features in 'features_by_layer'. If two features are observed at the same frequency their median index will be used to break the tie. If 'nfeatures' is not specified, all features in 'common_features' are returned.

Description

Returns the most frequently observed features in 'features_by_layer'. If two features are observed at the same frequency their median index will be used to break the tie. If 'nfeatures' is not specified, all features in 'common_features' are returned.

Usage

.GetConsensusFeatures(features_by_layer, common_features, nfeatures = NULL)

Arguments

Get a Method

Description

Get a Method

Usage

.GetMethod(fxn, cls)

Arguments

Value

The method of fxn for class cls; if no method found, returns the default method. If no default method found; returns NULL

Examples

.GetMethod('t', 'Matrix')
.GetMethod('t', 'data.frame')

Returns the top variable features from a data frame of highly variable feature annotations. Features are marked as variable if their value in 'label_column' is neither 'NA' nor 'FALSE'. When a ranking is provided via 'rank_column', features are ordered accordingly.

Description

Returns the top variable features from a data frame of highly variable feature annotations. Features are marked as variable if their value in 'label_column' is neither 'NA' nor 'FALSE'. When a ranking is provided via 'rank_column', features are ordered accordingly.

Usage

.GetVariableFeatures(hvf_info, label_column, rank_column, nfeatures)

Arguments

Test Future Compatibility with Seurat

Description

Check to see if SeuratObject and/or Seurat are at least a specific version or if they're configured to act as if they're a specific version (see details below). This allows testing compatibility with future requirements for both SeuratObject and Seurat

Usage

.IsFutureSeurat(version, lib.loc = NULL)

Arguments

Details

Blah blah blah

Value

TRUE if SeuratObject and/or Seurat

Regex Pattern for Keys

Description

Regex Pattern for Keys

Usage

.KeyPattern()

Value

Returns the regex pattern for keys (“^[a-zA-Z][a-zA-Z0-9]*_$”)

See Also

.RandomKey(), Key-validity, KeyMixin-class

Get the Margin of an Object

Description

Get the Margin of an Object

Usage

.MARGIN(x, ...)

Arguments

Value

The margin, eg. 1 for rows or 2 for columns

Propagate a List

Description

Propagate a List

Usage

.PropagateList(x, names, default = NA)

Arguments

Value

A named list where the names are present in both x and names and the values are either the values from x or default

Examples

.PropagateList("counts", c("RNA", "ADT", "SCT"))
.PropagateList(c("counts", "data"), c("RNA", "ADT", "SCT"))
.PropagateList("ADT", c("RNA", "ADT", "SCT"))
.PropagateList(c("RNA", "SCT"), c("RNA", "ADT", "SCT"))
.PropagateList(c("RNA", ADT = "counts"), c("RNA", "ADT", "SCT"))
.PropagateList(list(SCT = c("counts", "data"), ADT = "counts"), c("RNA", "ADT", "SCT"))
.PropagateList(list(SCT = c("counts", "data"), "ADT"), c("RNA", "ADT", "SCT"))

Generate a Random Key

Description

Generate a Random Key

Usage

.RandomKey(length = 7L, ...)

Arguments

Value

Returns a valid key

See Also

.KeyPattern(), Key-validity, KeyMixin-class

Examples

set.seed(42L)
.RandomKey()

Identify Sparse Slots

Description

Identify Sparse Slots

Usage

.SparseSlots(x, type = c("pointers", "indices", "entries"))

## S3 method for class 'CsparseMatrix'
.SparseSlots(x, type = c("pointers", "entries", "indices"))

## S3 method for class 'RsparseMatrix'
.SparseSlots(x, type = c("pointers", "indices", "entries"))

## S3 method for class 'spam'
.SparseSlots(x, type = c("pointers", "indices", "entries"))

Arguments

Value

...

See Also

IsSparse(), RegisterSparseMatrix()

Get the Subobject Names

Description

Get the Subobject Names

Usage

.Subobjects(object, exclude = c("misc", "tools"), collapse = TRUE, ...)

Arguments

Value

If collapse = TRUE, then a vector with the names of all subobjects; otherwise, a named list where the names are the names of the collections and the values are the names of subobjects within the collection

See Also

.Collections(), .FilterObjects(), .FindObject()

Examples

.Subobjects(pbmc_small)

Add in metadata associated with either cells or features.

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 'Assay5'
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[[]])

Add in metadata associated with either cells or features.

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

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

Arguments

Value

object with metadata added

Radian/Degree Conversions

Description

Convert degrees to radians and vice versa

Usage

Degrees(rad)

Radians(deg)

Arguments

Value

Degrees: rad in degrees

Radians: deg in radians

See Also

PolyVtx()

Examples

Degrees(pi)

Radians(180)

The Assay Class

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

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

A named list of unstructured miscellaneous data

key

A one-length character vector with the object's key; keys must be one or more alphanumeric characters followed by an underscore “_” (regex pattern “^[a-zA-Z][a-zA-Z0-9]*_$”)

See Also

v3 Assay object, validity, and interaction methods: $.Assay(), Assay-validity, CreateAssayObject(), [.Assay(), [[.Assay(), dim.Assay(), dimnames.Assay(), merge.Assay(), split.Assay(), subset.Assay()

V3 Assay Validity

Description

Validation of Assay objects is handled by validObject

data Validation

blah

counts Validation

blah

scale.data Validation

blah

Feature-Level Meta Data Validation

blah

Variable Feature Validation

blah

Key Validation

Keys must be a one-length character vector; a key must be composed of one of the following:

• An empty string (eg. “''”) where nchar() == 0

• An string composed of one or more alphanumeric values (both lower- and upper-case) that ends with an underscore (“_”); the first character must be a letter

Keys that are not empty strings are validated with the regex “^[a-zA-Z][a-zA-Z0-9]*_$”

See Also

validObject

v3 Assay object, validity, and interaction methods: $.Assay(), Assay-class, CreateAssayObject(), [.Assay(), [[.Assay(), dim.Assay(), dimnames.Assay(), merge.Assay(), split.Assay(), subset.Assay()

Examples

rna <- pbmc_small[["RNA"]]
validObject(rna)

The v5 Assay Object

Description

The v5 Assay is the typical Assay class used in Seurat v5; ...

Slots

layers

A named list containing expression matrices; each matrix should be a two-dimensional object containing some subset of cells and features defined in the cells and features slots. Cell and feature membership is recorded in the cells and features slots, respectively

cells

A logical mapping of cell names and layer membership; this map contains all the possible cells that this assay can contain. New layers must have some subset of cells present in this map

features

A logical mapping of feature names and layer membership; this map contains all the possible features that this assay can contain. New layers must have some subset of features present in this map

default

A one-length integer with the end index of the default layer; the default layer be all layers up to and including the layer at index default

assay.orig

Original assay that this assay is based off of; used to track assay provenance

meta.data

A data frame with feature-level meta data; should have the same number of rows as features

misc

A named list of unstructured miscellaneous data

key

A one-length character vector with the object's key; keys must be one or more alphanumeric characters followed by an underscore “_” (regex pattern “^[a-zA-Z][a-zA-Z0-9]*_$”)

See Also

v5 Assay object, validity, and interaction methods: $.Assay5(), Assay5-validity, [.Assay5(), [[.Assay5(), dim.Assay5(), dimnames.Assay5(), merge.Assay5(), split.Assay5(), subset.Assay5()

V5 Assay Validity

Description

Validation of Assay5 objects is handled by validObject

Layer Validation

blah

Key Validation

Keys must be a one-length character vector; a key must be composed of one of the following:

• An empty string (eg. “''”) where nchar() == 0

• An string composed of one or more alphanumeric values (both lower- and upper-case) that ends with an underscore (“_”); the first character must be a letter

Keys that are not empty strings are validated with the regex “^[a-zA-Z][a-zA-Z0-9]*_$”

See Also

validObject

v5 Assay object, validity, and interaction methods: $.Assay5(), Assay5-class, [.Assay5(), [[.Assay5(), dim.Assay5(), dimnames.Assay5(), merge.Assay5(), split.Assay5(), subset.Assay5()

The Transposed v5 Assay Object

Description

The Transposed v5 Assay Object

Slots

layers

A named list containing expression matrices; each matrix should be a two-dimensional object containing some subset of cells and features defined in the cells and features slots. Cell and feature membership is recorded in the cells and features slots, respectively

cells

A logical mapping of cell names and layer membership; this map contains all the possible cells that this assay can contain. New layers must have some subset of cells present in this map

features

A logical mapping of feature names and layer membership; this map contains all the possible features that this assay can contain. New layers must have some subset of features present in this map

default

A one-length integer with the end index of the default layer; the default layer be all layers up to and including the layer at index default

assay.orig

Original assay that this assay is based off of; used to track assay provenance

meta.data

A data frame with feature-level meta data; should have the same number of rows as features

misc

A named list of unstructured miscellaneous data

key

A one-length character vector with the object's key; keys must be one or more alphanumeric characters followed by an underscore “_” (regex pattern “^[a-zA-Z][a-zA-Z0-9]*_$”)

Lifecycle

Warning: functionality described here is experimental and prone to change without notice

Get and Set Assay Data

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, ...)

SetAssayData(object, layer, new.data, slot = deprecated(), ...)

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

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

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

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

Arguments

Value

GetAssayData: returns the specified assay data

SetAssayData: object with the assay data set

Lifecycle

GetAssayData and SetAssayData have been superseded. To fetch expression matrices, use LayerData; to set expression data, use LayerData<-

Examples

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

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

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

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

Get and Set Assay Data

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

## S3 method for class 'StdAssay'
GetAssayData(object, layer = NULL, slot = deprecated(), ...)

## S3 method for class 'StdAssay'
SetAssayData(object, layer, new.data, slot = deprecated(), ...)

Arguments

Value

GetAssayData: returns the specified assay data

SetAssayData: object with the assay data set

Lifecycle

GetAssayData and SetAssayData have been superseded. To fetch expression matrices, use LayerData; to set expression data, use LayerData<-

Query Specific Object Types

Description

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

Usage

Assays(object, ...)

Graphs(object, slot = NULL)

Neighbors(object, slot = NULL)

Reductions(object, slot = NULL)

## S3 method for class 'Seurat'
Assays(object, slot = deprecated(), ...)

Arguments

Value

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

Examples

Assays(pbmc_small)

Graphs(pbmc_small)

Reductions(object = pbmc_small)

Attach Required Packages

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

Lifecycle

AttachDeps has been superseded as of SeuratObject v5.0.0; as an alternative, list dependencies in the Depends section of DESCRIPTION

Examples

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

Get, Set, and Query Segmentation Boundaries

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

Cast Assay Layers

Description

Cast layers in v5 assays to other classes

Usage

CastAssay(object, to, ...)

## S3 method for class 'Assay5'
CastAssay(object, to, layers = NA, verbose = TRUE, ...)

Arguments

Value

object with the layers cast to class specified by to

Cast Assay Layers

Description

Cast layers in v5 assays to other classes

Usage

## S3 method for class 'StdAssay'
CastAssay(object, to, layers = NA, verbose = TRUE, ...)

Arguments

Value

object with the layers cast to class specified by to

Cell and Feature Names

Description

Get the cell and feature names of an object

Usage

Cells(x, ...)

Features(x, ...)

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

## S3 method for class 'Assay5'
Cells(x, layer = NULL, simplify = TRUE, ...)

## S3 method for class 'Assay5'
Features(x, layer = NULL, simplify = TRUE, ...)

## 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

See Also

dimnames.Assay(), dimnames.Assay5(), dimnames.Seurat()

Examples

Cells(x = pbmc_small)

Cell and Feature Names

Description

Get the cell and feature names of an object

Usage

## S3 method for class 'StdAssay'
Cells(x, layer = NULL, simplify = TRUE, ...)

## S3 method for class 'StdAssay'
Features(x, layer = NULL, simplify = TRUE, ...)

Arguments

Value

Cell: A vector of cell names

Features: A vector of feature names

Get cell names grouped by identity class

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)

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

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)

The Centroids Class

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: Centroids-methods, Molecules-class, Molecules-methods, Segmentation-class, Segmentation-methods

Centroids Methods

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

Segmentation layer classes: Centroids-class, Molecules-class, Molecules-methods, Segmentation-class, Segmentation-methods

Check the Use of Dots

Description

Function to check the use of unused arguments passed to ...; this function is designed to be called from another function to see if an argument passed to ... remains unused and alert the user if so. Also accepts a vector of function or function names to see if ... can be used in a downstream function

Usage

CheckDots(..., fxns = NULL)

Arguments

Details

Behavior of CheckDots can be controlled by the following option(s):

“Seurat.checkdots”

Control how to alert the presence of unused arguments in ...; choose from

• “warn”: emit a warning (default)

• “error”: throw an error

• “silent”: no not alert the presence of unused arguments in ...

Value

Emits either an error or warning if an argument passed is unused; invisibly returns NULL

Examples

## Not run: 
f <- function(x, ...) {
  CheckDots(...)
  return(x ^ 2)
}
f(x = 3, y = 9)

## End(Not run)

Check features names format

Description

Check features names format

Usage

CheckFeaturesNames(data)

Arguments

Value

data with update feature names

Conditional Garbage Collection

Description

Call gc only when desired

Usage

CheckGC(option = "SeuratObject.memsafe")

Arguments

Value

Invisibly returns NULL

Check layers names for the input list

Description

Check layers names for the input list

Usage

CheckLayersName(matrix.list, layers.type = c("counts", "data"))

Arguments

Check Matrix Validity

Description

Check Matrix Validity

Usage

CheckMatrix(object, checks, ...)

## Default S3 method:
CheckMatrix(object, checks, ...)

## S3 method for class 'dMatrix'
CheckMatrix(object, checks = c("infinite", "logical", "integer", "na"), ...)

## S3 method for class 'lMatrix'
CheckMatrix(object, checks = c("infinite", "logical", "integer", "na"), ...)

Arguments

Value

Emits warnings for each test and invisibly returns NULL

Generate a Class Key

Description

Generate class keys for S4 classes. A class key follows the following structure: “package:class”

Usage

ClassKey(class, package = NULL)

Arguments

Value

The class key (“package:class”)

See Also

s4list

Examples

ClassKey("Seurat")

Get SeuratCommands

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

Create a v5 Assay object

Description

Create an Assay5 object from a feature expression matrix; the expected format of the matrix is features x cells

Usage

CreateAssay5Object(
  counts = NULL,
  data = NULL,
  min.cells = 0,
  min.features = 0,
  csum = NULL,
  fsum = NULL,
  ...
)

Arguments

Value

An Assay5 object

Create an Assay object

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,
  key = NULL,
  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

See Also

v3 Assay object, validity, and interaction methods: $.Assay(), Assay-class, Assay-validity, [.Assay(), [[.Assay(), dim.Assay(), dimnames.Assay(), merge.Assay(), split.Assay(), subset.Assay()

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

Create a DimReduc 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

See Also

Dimensional reduction object, validity, and interaction methods DimReduc-class, DimReduc-validity, [.DimReduc(), [[.DimReduc(), dim.DimReduc(), merge.DimReduc(), print.DimReduc(), subset.DimReduc()

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"
)

Create Spatial Coordinates

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

Create a Seurat object

Description

Create a Seurat object from raw data

Usage

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

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

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

## S3 method for class 'Assay5'
CreateSeuratObject(
  counts,
  assay = "RNA",
  names.field = 1L,
  names.delim = "_",
  meta.data = NULL,
  project = "SeuratProject",
  ...
)

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)

Crop Coordinates

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

Default Assay

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 'Assay5'
DefaultAssay(object, ...)

## S3 replacement method for class 'Assay5'
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)

Default Assay

Description

Get and set the default assay

Usage

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

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

Arguments

Value

DefaultAssay: The name of the default assay

DefaultAssay<-: An object with the default assay updated

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)

Get and Set the Default FOV

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

Default Layer

Description

Get and set the default layer

Usage

DefaultLayer(object, ...)

DefaultLayer(object, ...) <- value

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

## S3 method for class 'Assay5'
DefaultLayer(object, ...)

## S3 replacement method for class 'Assay5'
DefaultLayer(object, ...) <- value

Arguments

Value

DefaultLayer: The name of the default layer

DefaultLayer<-: An object with the default layer updated

Default Layer

Description

Get and set the default layer

Usage

## S3 method for class 'StdAssay'
DefaultLayer(object, ...)

## S3 replacement method for class 'StdAssay'
DefaultLayer(object, ...) <- value

Arguments

Value

DefaultLayer: The name of the default layer

DefaultLayer<-: An object with the default layer updated

The Dimensional Reduction Class

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

jackstraw

A JackStrawData-class object associated with this DimReduc

misc

A named list of unstructured miscellaneous data

key

A one-length character vector with the object's key; keys must be one or more alphanumeric characters followed by an underscore “_” (regex pattern “^[a-zA-Z][a-zA-Z0-9]*_$”)

See Also

Dimensional reduction object, validity, and interaction methods CreateDimReducObject(), DimReduc-validity, [.DimReduc(), [[.DimReduc(), dim.DimReduc(), merge.DimReduc(), print.DimReduc(), subset.DimReduc()

Dimensional Reduction Validity

Description

Validation of DimReduc objects is handled by validObject

Cell Embeddings Validation

The cell embeddings matrix must be a numeric matrix of dimensions n_{cells} by d_{dimensions}; row names must be the cell names and column names must be the dimension identifier. The dimension identifier must be “key_dimension” (eg. “PC_1”). Dimension identifiers must be in order and cannot be skipped

Feature and Projected Feature Loadings Validation

blah

Standard Deviations Validation

blah

Key Validation

Keys must be a one-length character vector; a key must be composed of one of the following:

• An empty string (eg. “''”) where nchar() == 0

• An string composed of one or more alphanumeric values (both lower- and upper-case) that ends with an underscore (“_”); the first character must be a letter

Keys that are not empty strings are validated with the regex “^[a-zA-Z][a-zA-Z0-9]*_$”

See Also

Dimensional reduction object, validity, and interaction methods CreateDimReducObject(), DimReduc-class, [.DimReduc(), [[.DimReduc(), dim.DimReduc(), merge.DimReduc(), print.DimReduc(), subset.DimReduc()

Get the Neighbor nearest neighbors distance matrix

Description

Get the Neighbor nearest neighbors distance matrix

Usage

Distances(object, ...)

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

Arguments

Value

The distance matrix

Get Cell Embeddings

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]

Empty Data Frames

Description

Create an empty data frame with no row names and zero columns

Usage

EmptyDF(n)

Arguments

Value

A data frame with n rows and zero columns

Examples

EmptyDF(4L)

Empty Matrices

Description

Create empty 0x0 matrices of varying types

Usage

EmptyMatrix(repr = "C", type = "d")

Arguments

Value

A 0x0 matrix of the specified representation and type

See Also

IsMatrixEmpty()

Examples

EmptyMatrix()
EmptyMatrix("spam")

Extract delimiter information from a string.

Description

Parses a string (usually a cell name) and extracts fields based on a delimiter

Usage

ExtractField(string, field = 1, delim = "_")

Arguments

Value

A new string, that parses out the requested fields, and (if multiple), rejoins them with the same delimiter

Examples

ExtractField('Hello World', field = 1, delim = '_')

The Field of View Object

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

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

boundaries

A named list of Segmentation and Centroids objects defining spatially-resolved boundaries

assay

A character naming the associated assay of the spatial coordinates

key

A one-length character vector with the object's key; keys must be one or more alphanumeric characters followed by an underscore “_” (regex pattern “^[a-zA-Z][a-zA-Z0-9]*_$”)

See Also

FOV-methods

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

FOV Validity

Description

Validation of FOV objects is handled by validObject

Boundary Validation

blah

Molecule Validation

blah

See Also

validObject

Access cellular data

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, ...)

## S3 method for class 'Seurat'
FetchData(
  object,
  vars,
  cells = NULL,
  layer = NULL,
  clean = TRUE,
  slot = deprecated(),
  ...
)

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')))

Find Sub-objects of a Certain Class

Description

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

Usage

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

Arguments

Value

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

Lifecycle

FilterObjects was deprecated in version 5.0.0; use .FilterObjects instead

Examples

FilterObjects(pbmc_small)

Get image data

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

Get tissue coordinates

Description

Get tissue coordinates

Usage

GetTissueCoordinates(object, ...)

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

Arguments

Value

A data frame with tissue coordinates

The Graph Class

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

Other graph: as.Graph()

Highly Variable Features

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, method, status = FALSE, ...)

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

VariableFeatures(object, ...) <- value

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

SpatiallyVariableFeatures(object, method, ...)

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

## S3 method for class 'Seurat'
VariableFeatures(
  object,
  method = NULL,
  assay = NULL,
  nfeatures = NULL,
  layer = NA,
  simplify = TRUE,
  selection.method = deprecated(),
  ...
)

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

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

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

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

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

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

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

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

## S3 method for class 'Assay5'
HVFInfo(object, method = NULL, status = FALSE, layer = NA, strip = TRUE, ...)

## S3 method for class 'Assay5'
VariableFeatures(
  object,
  method = NULL,
  layer = NA,
  simplify = TRUE,
  nfeatures = NULL,
  selection.method = deprecated(),
  ...
)

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

## S3 method for class 'Assay5'
SVFInfo(
  object,
  method = c("markvariogram", "moransi"),
  status = FALSE,
  selection.method = deprecated(),
  ...
)

## S3 method for class 'Assay5'
SpatiallyVariableFeatures(
  object,
  method = "moransi",
  decreasing = TRUE,
  selection.method = deprecated(),
  ...
)

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"]], method = 'vst')[1:5, ]

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

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, replace = 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)

Pull spatial image names

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)

Get Neighbor algorithm index

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

Get Neighbor nearest neighbor index matrices

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

Is an object global/persistent?

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']])

Check if a matrix is empty

Description

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

Usage

IsMatrixEmpty(x)

## Default S3 method:
IsMatrixEmpty(x)

Arguments

Value

Whether or not x is empty

See Also

EmptyMatrix()

Examples

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

Check List Names

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

Examples

IsNamedList(list())
IsNamedList(list(), pass.zero = TRUE)
IsNamedList(list(1, 2, 3))
IsNamedList(list(a = 1, b = 2, c = 3))
IsNamedList(list(a = 1, 2, c = 3))
IsNamedList(list(a = 1, 2, c = 3), allow.empty = TRUE)
IsNamedList(list(a = 1, a = 2, a = 3))
IsNamedList(list(a = 1, a = 2, a = 3), all.unique = FALSE)

Is a Matrix Sparse

Description

Is a Matrix Sparse

Usage

IsSparse(x)

Arguments

Value

...

See Also

.SparseSlots(), RegisterSparseMatrix()

Examples

IsSparse(matrix())
IsSparse(LayerData(pbmc_small, "counts"))

Get and set JackStraw information

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

The JackStrawData Class

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

JackStrawData Methods

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

Split and Join Layers Together

Description

Split and Join Layers Together

Usage

JoinLayers(object, ...)

## S3 method for class 'Assay5'
JoinLayers(object, layers = NULL, new = NULL, ...)

## S3 method for class 'Seurat'
JoinLayers(object, assay = NULL, layers = NULL, new = NULL, ...)

Arguments

Value

object with the layers specified joined

Get and set object keys

Description

Get and set object keys

Usage

Key(object, ...)

Keys(object, ...)

Key(object, ...) <- value

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

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

## S3 method for class 'Assay5'
Key(object, ...)

## S3 replacement method for class 'Assay5'
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

Keys: a named vector of keys of sub-objects

Key<-: object with an updated key

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)

Key Validity

Description

Validation of KeyMixin objects is handled by validObject

Key Validation

Keys must be a one-length character vector; a key must be composed of one of the following:

• An empty string (eg. “''”) where nchar() == 0

• An string composed of one or more alphanumeric values (both lower- and upper-case) that ends with an underscore (“_”); the first character must be a letter

Keys that are not empty strings are validated with the regex “^[a-zA-Z][a-zA-Z0-9]*_$”

See Also

.KeyPattern(), .RandomKey(), KeyMixin-class

A Mixin for Keyed objects

Description

A mixin (virtual class) for enabling keyed objects; provides consistent behavior for getting, setting, and validating keys

Usage

## S3 method for class 'character'
Key(object, ..., quiet = FALSE)

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

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

Arguments

Details

Key.character: Update a character to a key

Key.KeyMixin: Get the key of a keyed object

Key<-: Set the key of a keyed object

Value

Key.character: object but as a syntactically-valid key

Key.KeyMixin: The key from object; if no key set, returns NULL

Key<-: object with the key set to value

Slots

key

A one-length character vector with the object's key; keys must be one or more alphanumeric characters followed by an underscore “_” (regex pattern “^[a-zA-Z][a-zA-Z0-9]*_$”)

See Also

.KeyPattern(), .RandomKey(), Key-validity

Query and Manipulate Assay Layers

Description

Query and Manipulate Assay Layers

Usage

LayerData(object, layer, ...)

LayerData(object, layer, ...) <- value

Layers(object, ...)

## S3 method for class 'Assay'
LayerData(
  object,
  layer = NULL,
  cells = NULL,
  features = NULL,
  slot = deprecated(),
  ...
)

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

## S3 method for class 'Assay'
Layers(object, search = NA, ...)

## S3 method for class 'Assay5'
LayerData(
  object,
  layer = NULL,
  cells = NULL,
  features = NULL,
  fast = FALSE,
  slot = deprecated(),
  ...
)

## S3 replacement method for class 'Assay5'
LayerData(object, layer, features = NULL, cells = NULL, ...) <- value

## S3 method for class 'Assay5'
Layers(object, search = NA, ...)

## S3 method for class 'Seurat'
LayerData(object, layer = NULL, assay = NULL, slot = deprecated(), ...)

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

## S3 method for class 'Seurat'
Layers(object, search = NA, assay = NULL, ...)

Arguments

Value

LayerData: the layer data for layer from object

Layer<-: object with value added as a layer named layer

Layers: the names of the layers present in object

Query and Manipulate Assay Layers

Description

Query and Manipulate Assay Layers

Usage

## S3 method for class 'StdAssay'
LayerData(
  object,
  layer = NULL,
  cells = NULL,
  features = NULL,
  fast = FALSE,
  slot = deprecated(),
  ...
)

## S3 replacement method for class 'StdAssay'
LayerData(object, layer, features = NULL, cells = NULL, ...) <- value

## S3 method for class 'StdAssay'
Layers(object, search = NA, ...)

Arguments

Value

LayerData: the layer data for layer from object

Layer<-: object with value added as a layer named layer

Layers: the names of the layers present in object

Get and set feature loadings

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]

A Logical Map

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

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

See Also

Logical map objects, validity, and interaction methods: LogMap-validity, as.matrix.LogMap(), droplevels.LogMap(), intersect.LogMap(), labels.LogMap()

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[['entry']] <- c(2, 7, 10)
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[['entry']] <- NULL
map

Logical Map Validity

Description

Validation of LogMap objects is handled by validObject

Data Validation

Logical maps must be a logical matrix containing only TRUE or FALSE values

Value Validation

All values must be named within the rownames of the object. Duplicate or empty ("") values are not allowed

Observation Validation

All observations must be named within the column names of the object. Duplicate or empty ("") observations are not allowed

See Also

validObject

Logical map objects, validity, and interaction methods: LogMap, as.matrix.LogMap(), droplevels.LogMap(), intersect.LogMap(), labels.LogMap()

Examples

map <- LogMap(letters[1:10])
map[['obs']] <- c(1, 3, 7)
map[['entry']] <- c(2, 7, 10)
validObject(map)

Log a command

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

Command log object and interaction methods $.SeuratCommand(), .DollarNames.SeuratCommand(), SeuratCommand-class, [.SeuratCommand(), as.list.SeuratCommand()

Match Cells

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

Get and set miscellaneous data

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 'Assay5'
Misc(object, slot = NULL, ...)

## S3 replacement method for class 'Assay5'
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"

Get and set miscellaneous data

Description

Get and set miscellaneous data

Usage

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

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

Arguments

Value

Miscellaneous data

An object with miscellaneous data added

The Spatial Molecules Class

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, Centroids-methods, Molecules-methods, Segmentation-class, Segmentation-methods

Molecules Methods

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

Segmentation layer classes: Centroids-class, Centroids-methods, Molecules-class, Segmentation-class, Segmentation-methods

The Neighbor 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.

Neighbor Methods

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

Overlay Spatial Objects Over One Another

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

Note

This function requires the sf package to be installed

Check the existence of a package

Description

Check the existence of a package

Usage

PackageCheck(..., error = TRUE)

Arguments

Value

Invisibly returns boolean denoting if the package is installed

Lifecycle

PackageCheck was deprecated in version 5.0.0; please use rlang::check_installed() instead

Examples

PackageCheck("SeuratObject", error = FALSE)

Polygon Vertices

Description

Calculate the vertices of a regular polygon given the number of sides and its radius (distance from center to vertex). Also permits transforming the resulting coordinates by moving the origin and altering the initial angle

Usage

PolyVtx(n, r = 1L, xc = 0L, yc = 0L, t1 = 0)

Arguments

Value

A data.frame with n rows and two columns:

x

X positions of each coordinate

y

Y positions of each coordinate

References

https://stackoverflow.com/questions/3436453/calculate-coordinates-of-a-regular-polygons-vertices

See Also

Angles

Examples

(coords <- PolyVtx(5, t1 = 90))
if (requireNamespace("ggplot2", quietly = TRUE)) {
  ggplot2::ggplot(coords, ggplot2::aes(x = x, y = y)) + ggplot2::geom_polygon()
}

Get and set project information

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

Get the spot radius from an image

Description

Get the spot radius from an image

Usage

Radius(object, ...)

Arguments

Value

The radius size

Generate a random name

Description

Make a name from randomly sampled characters, pasted together with no spaces

Usage

RandomName(length = 5L, chars = letters, ...)

Arguments

Value

A character with nchar == length of randomly sampled letters

See Also

sample

Examples

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

Register Sparse Matrix Classes

Description

Register Sparse Matrix Classes

Usage

RegisterSparseMatrix(class, package = NULL)

Arguments

Value

Invisibly returns NULL

See Also

.SparseSlots(), IsSparse()

Rename assays in a Seurat object

Description

Rename assays in a Seurat object

Usage

RenameAssays(
  object,
  assay.name = NULL,
  new.assay.name = NULL,
  verbose = TRUE,
  ...
)

Arguments

Value

object with assays renamed

Examples

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

Rename cells

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 'Assay5'
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 = missing_arg(),
  new.names = missing_arg(),
  for.merge = deprecated(),
  ...
)

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))

Rename cells

Description

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

Usage

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

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

Merge Sparse Matrices by Row

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

Save and Load Seurat Objects from Rds files

Description

Save and Load Seurat Objects from Rds files

Usage

SaveSeuratRds(
  object,
  file = NULL,
  move = TRUE,
  destdir = deprecated(),
  relative = FALSE,
  ...
)

LoadSeuratRds(file, ...)

Arguments

Value

Invisibly returns file

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")

Note

This function requires the fs package to be installed

See Also

saveRDS() readRDS()

Examples

if (requireNamespace("fs", quietly = TRUE)) {
  # Write out with DelayedArray
  if (requireNamespace("HDF5Array", quietly = TRUE)) {
    pbmc <- pbmc_small

    pbmc[["disk"]] <- CreateAssay5Object(list(
      mem = LayerData(pbmc, "counts"),
      disk = as(LayerData(pbmc, "counts"), "HDF5Array")
    ))

    # Save `pbmc` to an Rds file
    out <- tempfile(fileext = ".Rds")
    SaveSeuratRds(pbmc, file = out)

    # Object cache
    obj <- readRDS(out)
    Tool(obj, "SaveSeuratRds")

    # Load the saved object with on-disk layers back into memory
    pbmc2 <- LoadSeuratRds(out)
    pbmc2
    pbmc2[["disk"]]
  }

  # Write out with BPCells
  if (requireNamespace("BPCells", quietly = TRUE)) {
    pbmc <- pbmc_small

    bpm <- BPCells::write_matrix_dir(LayerData(pbmc, "counts"), dir = tempfile())
    bph <- BPCells::write_matrix_hdf5(
      LayerData(pbmc, "counts"),
      path = tempfile(fileext = ".h5"),
      group = "counts"
    )
    pbmc[["disk"]] <- CreateAssay5Object(list(dir = bpm, h5 = bph))

    # Save `pbmc` to an Rds file
    out <- tempfile(fileext = ".Rds")
    SaveSeuratRds(pbmc, file = out)

    # Object cache
    obj <- readRDS(out)
    Tool(obj, "SaveSeuratRds")

    # Load the saved object with on-disk layers back into memory
    pbmc2 <- LoadSeuratRds(out)
    pbmc2
    pbmc2[["disk"]]
  }
}

The Segmentation Class

Description

The Segmentation Class

See Also

Segmentation methods: Segmentation-methods

Segmentation layer classes: Centroids-class, Centroids-methods, Molecules-class, Molecules-methods, Segmentation-methods

Segmentation Methods

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

Segmentation layer classes: Centroids-class, Centroids-methods, Molecules-class, Molecules-methods, Segmentation-class

The Seurat Class

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<-

See Also

Seurat object, validity, and interaction methods $.Seurat(), Seurat-validity, [[.Seurat(), [[<-,Seurat, [[<-,Seurat,NULL, dim.Seurat(), dimnames.Seurat(), merge.Seurat(), names.Seurat(), subset.Seurat()

Seurat Object Validity

Description

Validation of Seurat objects is handled by validObject

See Also

validObject

Seurat object, validity, and interaction methods $.Seurat(), Seurat-class, [[.Seurat(), [[<-,Seurat, [[<-,Seurat,NULL, dim.Seurat(), dimnames.Seurat(), merge.Seurat(), names.Seurat(), subset.Seurat()

The SeuratCommand Class

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

See Also

Command log object and interaction methods $.SeuratCommand(), .DollarNames.SeuratCommand(), LogSeuratCommand(), [.SeuratCommand(), as.list.SeuratCommand()

Seurat Options

Description

Various options used in Seurat

Package Options

Seurat.coords.short_range

Defaults to “max”
Currently set to “max”

Seurat.input.sparse_ratio

Defaults to “0.4”
Currently set to “0.4”

Seurat.io.rds.strict

Defaults to “FALSE”
Currently set to “FALSE”

Seurat.object.assay.calcn

Run CalcN when adding assay data to a Seurat object
Defaults to “”
Currently set to “”

Seurat.object.assay.version

Defaults to “v5”
Currently set to “v5”

Seurat.object.assay.v3.missing_layer

Defaults to “matrix”
Currently set to “matrix”

Seurat.object.project

Default project for new Seurat objects
Defaults to “SeuratProject”
Currently set to “SeuratProject”

Simplify Geometry

Description

Simplify Geometry

Simplify segmentations by reducing the number of vertices

Usage

Simplify(coords, tol, topologyPreserve = TRUE)

## S3 method for class 'Spatial'
Simplify(coords, tol, topologyPreserve = TRUE)

Arguments

Value

A simplified version of coords

A 'Segmentation' object with simplified segmentation vertices

Generate empty dgC sparse matrix

Description

Generate empty dgC sparse matrix

Usage

SparseEmptyMatrix(nrow, ncol, rownames = NULL, colnames = NULL)

Arguments

The SpatialImage class

Description

The SpatialImage class is a virtual class representing spatial information for Seurat. All spatial image information must inherit from this class for use with Seurat objects

Slots

assay

Name of assay to associate image data with; will give this image priority for visualization when the assay is set as the active/default assay in a Seurat object

key

A one-length character vector with the object's key; keys must be one or more alphanumeric characters followed by an underscore “_” (regex pattern “^[a-zA-Z][a-zA-Z0-9]*_$”)

See Also

SpatialImage-methods for a list of required and provided methods

SpatialImage methods

Description

Methods defined on the SpatialImage class. Some of these methods must be overridden in order to ensure proper functionality of the derived classes (see Required methods below). Other methods are designed to work across all SpatialImage-derived subclasses, and should only be overridden if necessary

Usage

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

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

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

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

## S3 method for class 'SpatialImage'
GetTissueCoordinates(object, ...)

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

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

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

## S3 method for class 'SpatialImage'
Radius(object, ...)

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

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

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

## S3 method for class 'SpatialImage'
subset(x, cells, ...)

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

Arguments

Value

[Override] Cells: should return cell names

DefaultAssay: The associated assay of a SpatialImage-derived object

DefaultAssay<-: object with the associated assay updated

[Override] GetImage: The image data from a SpatialImage-derived object

[Override] GetTissueCoordinates: ...

IsGlobal: returns TRUE as images are, by default, global

Key: The key for a SpatialImage-derived object

Key<-: object with the key set to value

Radius: The spot radius size; by default, returns NULL

[Override] RenameCells: object with the new cell names

[, subset: x/object for only the cells requested

[Override] dim: The dimensions of the image data in (Y, X) format

show: Prints summary to stdout and invisibly returns NULL

Functions

• Cells(SpatialImage): Get the cell names from an image ([Override])

• DefaultAssay(SpatialImage): Get the associated assay of a SpatialImage-derived object

• DefaultAssay(SpatialImage) <- value: Set the associated assay of a SpatialImage-derived object

• GetImage(SpatialImage): Get the image data from a SpatialImage-derived object

• GetTissueCoordinates(SpatialImage): Get tissue coordinates for a SpatialImage-derived object ([Override])

• IsGlobal(SpatialImage): Globality test for SpatialImage-derived object

• Key(SpatialImage): Get the key for a SpatialImage-derived object

• Key(SpatialImage) <- value: Set the key for a SpatialImage-derived object

• Radius(SpatialImage): Get the spot radius size

• RenameCells(SpatialImage): Rename cells in a SpatialImage-derived object ([Override])

• [: Subset a SpatialImage-derived object

• dim(SpatialImage): Get the plotting dimensions of an image ([Override])

• subset(SpatialImage): Subset a SpatialImage-derived object ([Override])

• show(SpatialImage): Overview of a SpatialImage-derived object

Provided methods

These methods are defined on the SpatialImage object and should not be overridden without careful thought

• DefaultAssay and DefaultAssay<-

• Key and Key<-

• GetImage; this method can be overridden to provide image data, normally returns empty image data. If overridden, should default to returning a grob object

• IsGlobal

• Radius; this method can be overridden to provide a spot radius for image objects

• [; this method can be overridden to change default subset behavior, normally returns subset(x = x, cells = i). If overridden, should only accept i

Required methods

All subclasses of the SpatialImage class must define the following methods; simply relying on the SpatialImage method will result in errors. For required parameters and their values, see the Usage and Arguments sections

Cells

Return the cell/spot barcodes associated with each position

dim

Return the dimensions of the image for plotting in (Y, X) format

GetTissueCoordinates

Return tissue coordinates; by default, must return a two-column data.frame with x-coordinates in the first column and y-coordinates in the second

Radius

Return the spot radius; returns NULL by default for use with non-spot image technologies

RenameCells

Rename the cell/spot barcodes for this image

subset

Subset the image data by cells/spots

These methods are used throughout Seurat, so defining them and setting the proper defaults will allow subclasses of SpatialImage to work seamlessly

See Also

DefaultAssay

GetImage

GetTissueCoordinates

IsGlobal

Key

RenameCells

Core Assay Infrastructure

Description

The StdAssay class is a virtual class that provides core infrastructure for assay data in Seurat. Assays contain expression data (layers) and associated feature-level meta data. Derived classes (eg. the v5 Assay) may optionally define additional functionality

Slots

layers

A named list containing expression matrices; each matrix should be a two-dimensional object containing some subset of cells and features defined in the cells and features slots. Cell and feature membership is recorded in the cells and features slots, respectively

cells

A logical mapping of cell names and layer membership; this map contains all the possible cells that this assay can contain. New layers must have some subset of cells present in this map

features

A logical mapping of feature names and layer membership; this map contains all the possible features that this assay can contain. New layers must have some subset of features present in this map

default

A one-length integer with the end index of the default layer; the default layer be all layers up to and including the layer at index default

assay.orig

Original assay that this assay is based off of; used to track assay provenance

meta.data

A data frame with feature-level meta data; should have the same number of rows as features

misc

A named list of unstructured miscellaneous data

key

A one-length character vector with the object's key; keys must be one or more alphanumeric characters followed by an underscore “_” (regex pattern “^[a-zA-Z][a-zA-Z0-9]*_$”)

See Also

Assay5-class Assay5T-class

v5 Standard Assay object, validity, and interaction methods $.StdAssay(), .DollarNames.StdAssay(), StdAssay-validity, [.StdAssay(), [[.StdAssay(), dim.StdAssay(), dimnames.StdAssay(), show,StdAssay-method, split.StdAssay(), subset.StdAssay()

V5 Assay Validity

Description

Validation of StdAssay objects is handled by validObject

Layer Validation

blah

Key Validation

Keys must be a one-length character vector; a key must be composed of one of the following:

• An empty string (eg. “''”) where nchar() == 0

• An string composed of one or more alphanumeric values (both lower- and upper-case) that ends with an underscore (“_”); the first character must be a letter

Keys that are not empty strings are validated with the regex “^[a-zA-Z][a-zA-Z0-9]*_$”

See Also

validObject

v5 Standard Assay object, validity, and interaction methods $.StdAssay(), .DollarNames.StdAssay(), StdAssay-class, [.StdAssay(), [[.StdAssay(), dim.StdAssay(), dimnames.StdAssay(), show,StdAssay-method, split.StdAssay(), subset.StdAssay()

Get the standard deviations for an object

Description

Get the standard deviations for an object

Usage

Stdev(object, ...)

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

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

Arguments

Value

The standard deviations

Examples

# Get the standard deviations for each PC from the DimReduc object
Stdev(object = pbmc_small[["pca"]])

# Get the standard deviations for each PC from the Seurat object
Stdev(object = pbmc_small, reduction = "pca")

Stitch Matrices Together

Description

Stitch Matrices Together

Usage

StitchMatrix(x, y, rowmap, colmap, ...)

Arguments

Value

A single matrix of type class(x) consisting of all values in component matrices

Get the offset angle

Description

Get the offset angle

Usage

Theta(object)

Arguments

Get and Set Additional Tool Data

Description

Use Tool to get tool data. If no additional arguments are provided, will return a vector with the names of tools in the object.

Usage

Tool(object, ...)

Tool(object, ...) <- value

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

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

Arguments

Value

If no additional arguments, returns the names of the tools in the object; otherwise returns the data placed by the tool requested

Note

For developers: set tool data using Tool<-. Tool<- will automatically set the name of the tool to the function that called Tool<-, so each function gets one entry in the tools list and cannot overwrite another function's entry. The automatic naming will also remove any method identifiers (eg. RunPCA.Seurat will become RunPCA); please plan accordingly

Examples

# Example function that adds unstructured data to tools
MyTool <- function(object) {
  sample.tool.output <- matrix(rnorm(n = 16), nrow = 4)
  # Note: `Tool<-` must be called from within a function
  # and the name of the tool will be generated from the function name
  Tool(object) <- sample.tool.output
  return(object)
}

# Run our tool
set.seed(42L)
pbmc_small <- MyTool(pbmc_small)

# Get a list of tools run
Tool(pbmc_small)

# Access specific tool data
Tool(pbmc_small, slot = "MyTool")

Update old Seurat object to accommodate new features

Description

Updates Seurat objects to new structure for storing data/calculations. For Seurat v3 objects, will validate object structure ensuring all keys and feature names are formed properly.

Usage

UpdateSeuratObject(object)

Arguments

Value

Returns a Seurat object compatible with latest changes

Examples

## Not run: 
updated_seurat_object = UpdateSeuratObject(object = old_seurat_object)

## End(Not run)

Update slots in an object

Description

Update slots in an object

Usage

UpdateSlots(object)

Arguments

Value

object with the latest slot definitions

Highly Variable Features

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

## S3 method for class 'StdAssay'
HVFInfo(object, method = NULL, status = FALSE, layer = NA, strip = TRUE, ...)

## S3 method for class 'StdAssay'
VariableFeatures(
  object,
  method = NULL,
  layer = NA,
  simplify = TRUE,
  nfeatures = NULL,
  selection.method = deprecated(),
  ...
)

## S3 replacement method for class 'StdAssay'
VariableFeatures(object, method = "custom", layer = NULL, ...) <- value

## S3 replacement method for class 'Assay5'
VariableFeatures(object, method = "custom", layer = NULL, ...) <- value

## S3 method for class 'StdAssay'
SpatiallyVariableFeatures(
  object,
  method = "moransi",
  decreasing = TRUE,
  selection.method = deprecated(),
  ...
)

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

Get Version Information

Description

Get Version Information

Usage

Version(object, ...)

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

Arguments

Examples

Version(pbmc_small)

Identify cells matching certain criteria

Description

Returns a list of cells that match a particular set of criteria such as identity class, high/low values for particular PCs, etc.

Usage

WhichCells(object, ...)

## S3 method for class 'Assay'
WhichCells(object, cells = NULL, expression, invert = FALSE, ...)

## S3 method for class 'Seurat'
WhichCells(
  object,
  cells = NULL,
  idents = NULL,
  expression,
  slot = "data",
  invert = FALSE,
  downsample = Inf,
  seed = 1,
  ...
)

Arguments

Value

A vector of cell names

See Also

FetchData

Examples

WhichCells(pbmc_small, idents = 2)
WhichCells(pbmc_small, expression = MS4A1 > 3)
levels(pbmc_small)
WhichCells(pbmc_small, idents = c(1, 2), invert = TRUE)

Matrix-like Subsetting for Logical Maps

Description

Matrix-like Subsetting for Logical Maps

Usage

## S4 method for signature 'LogMap,missing,missing,ANY'
x[i, j, ..., drop = FALSE]

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

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

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

## S4 method for signature 'LogMap,numeric,missing,ANY'
x[i, j, ..., drop = FALSE]

## S4 method for signature 'LogMap,missing,numeric,ANY'
x[i, j, ..., drop = FALSE]

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

Arguments

Note

i is not reordable; passing a different order for i will return a subset with rows in the same order as x

Examples

map <- LogMap(letters[1:10])
map[['obs']] <- c(1, 3, 7)
map[['entry']] <- c(2, 7, 10)

map[]
map[1:5, 2L]
map[c("b", "c", "f"), "obs"]

# Pass `drop = TRUE` to cast to `matrix`
map[1:3, , drop = TRUE]

# Note that `i` is non-reordable
rownames(map)[1:3]
map[c("b", "c", "a"), , drop = TRUE]

Layer Data

Description

Get and set layer data

Usage

## S3 method for class 'Assay'
x[i = missing_arg(), j = missing_arg(), ...]

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

Arguments

Value

[: The layer data for layer i

[<-: x with layer data value saved as i

See Also

LayerData

v3 Assay object, validity, and interaction methods: $.Assay(), Assay-class, Assay-validity, CreateAssayObject(), [[.Assay(), dim.Assay(), dimnames.Assay(), merge.Assay(), split.Assay(), subset.Assay()

Examples

rna <- pbmc_small[["RNA"]]

# Get a vector of layer names in this assay
rna[]

# Fetch layer data
rna["data"][1:10, 1:4]

# Set layer data
rna["data"] <- rna["counts"]
rna["data"][1:10, 1:4]

Layer Data

Description

Get and set layer data

Usage

## S3 method for class 'Assay5'
x[i = missing_arg(), j = missing_arg(), ...]

## S4 replacement method for signature 'Assay5,character,ANY,ANY'
x[i, j, ...] <- value

Arguments