Version:	5.3.0
Title:	Tools for Single Cell Genomics
Description:	A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>, and Hao, Hao, et al (2020) <doi:10.1101/2020.10.12.335331> for more details.
License:	MIT + file LICENSE
URL:	https://satijalab.org/seurat, https://github.com/satijalab/seurat
BugReports:	https://github.com/satijalab/seurat/issues
Additional_repositories:	https://satijalab.r-universe.dev, https://bnprks.r-universe.dev
Depends:	R (≥ 4.0.0), methods, SeuratObject (≥ 5.0.2)
Imports:	cluster, cowplot, fastDummies, fitdistrplus, future, future.apply, generics (≥ 0.1.3), ggplot2 (≥ 3.3.0), ggrepel, ggridges, graphics, grDevices, grid, httr, ica, igraph, irlba, jsonlite, KernSmooth, leidenbase, lifecycle, lmtest, MASS, Matrix (≥ 1.5-0), matrixStats, miniUI, patchwork, pbapply, plotly (≥ 4.9.0), png, progressr, RANN, RColorBrewer, Rcpp (≥ 1.0.7), RcppAnnoy (≥ 0.0.18), RcppHNSW, reticulate, rlang, ROCR, RSpectra, Rtsne, scales, scattermore (≥ 1.2), sctransform (≥ 0.4.1), shiny, spatstat.explore, spatstat.geom, stats, tibble, tools, utils, uwot (≥ 0.1.10)
Suggests:	ape, arrow, Biobase, BiocGenerics, BPCells, data.table, DESeq2, DelayedArray, enrichR, GenomicRanges, GenomeInfoDb, glmGamPoi, ggrastr, harmony, hdf5r, IRanges, limma, MAST, metap, mixtools, monocle, presto, rsvd, R.utils, Rfast2, rtracklayer, S4Vectors, sf (≥ 1.0.0), SingleCellExperiment, SummarizedExperiment, testthat, VGAM
LinkingTo:	Rcpp (≥ 0.11.0), RcppEigen, RcppProgress
BuildManual:	true
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
Collate:	'RcppExports.R' 'reexports.R' 'generics.R' 'clustering.R' 'visualization.R' 'convenience.R' 'data.R' 'differential_expression.R' 'dimensional_reduction.R' 'integration.R' 'zzz.R' 'integration5.R' 'mixscape.R' 'objects.R' 'preprocessing.R' 'preprocessing5.R' 'roxygen.R' 'sketching.R' 'tree.R' 'utilities.R'
NeedsCompilation:	yes
Packaged:	2025-04-23 19:32:38 UTC; root
Author:	Andrew Butler [ctb], Saket Choudhary [ctb], David Collins [ctb], Charlotte Darby [ctb], Jeff Farrell [ctb], Isabella Grabski [ctb], Christoph Hafemeister [ctb], Yuhan Hao [ctb], Austin Hartman [ctb], Paul Hoffman [ctb], Jaison Jain [ctb], Longda Jiang [ctb], Madeline Kowalski [ctb], Skylar Li [ctb], Gesmira Molla [ctb], Efthymia Papalexi [ctb], Patrick Roelli [ctb], Rahul Satija [aut, cre], Karthik Shekhar [ctb], Avi Srivastava [ctb], Tim Stuart [ctb], Kristof Torkenczy [ctb], Shiwei Zheng [ctb], Satija Lab and Collaborators [fnd]
Maintainer:	Rahul Satija <seurat@nygenome.org>
Repository:	CRAN
Date/Publication:	2025-04-23 22:10:02 UTC

Seurat: Tools for Single Cell Genomics

Description

A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) doi:10.1038/nbt.3192, Macosko E, Basu A, Satija R, et al (2015) doi:10.1016/j.cell.2015.05.002, Stuart T, Butler A, et al (2019) doi:10.1016/j.cell.2019.05.031, and Hao, Hao, et al (2020) doi:10.1101/2020.10.12.335331 for more details.

Package options

Seurat uses the following [options()] to configure behaviour:

Seurat.memsafe

global option to call gc() after many operations. This can be helpful in cleaning up the memory status of the R session and prevent use of swap space. However, it does add to the computational overhead and setting to FALSE can speed things up if you're working in an environment where RAM availability is not a concern.

Seurat.warn.umap.uwot

Show warning about the default backend for RunUMAP changing from Python UMAP via reticulate to UWOT

Seurat.checkdots

For functions that have ... as a parameter, this controls the behavior when an item isn't used. Can be one of warn, stop, or silent.

Seurat.limma.wilcox.msg

Show message about more efficient Wilcoxon Rank Sum test available via the limma package

Seurat.Rfast2.msg

Show message about more efficient Moran's I function available via the Rfast2 package

Seurat.warn.vlnplot.split

Show message about changes to default behavior of split/multi violin plots

Author(s)

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

Other contributors:

• Andrew Butler abutler@nygenome.org (ORCID) [contributor]

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

• David Collins dcollins@nygenome.org (ORCID) [contributor]

• Charlotte Darby cdarby@nygenome.org (ORCID) [contributor]

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

• Isabella Grabski igrabski@nygenome.org (ORCID) [contributor]

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

• Yuhan Hao yhao@nygenome.org (ORCID) [contributor]

• Austin Hartman ahartman@nygenome.org (ORCID) [contributor]

• Paul Hoffman hoff0792@umn.edu (ORCID) [contributor]

• Jaison Jain jjain@nygenome.org (ORCID) [contributor]

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

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

• Skylar Li sli@nygenome.org [contributor]

• Gesmira Molla gmolla@nygenome.org (ORCID) [contributor]

• Efthymia Papalexi epapalexi@nygenome.org (ORCID) [contributor]

• Patrick Roelli proelli@nygenome.org [contributor]

• Karthik Shekhar kshekhar@berkeley.edu [contributor]

• Avi Srivastava asrivastava@nygenome.org (ORCID) [contributor]

• Tim Stuart tstuart@nygenome.org (ORCID) [contributor]

• Kristof Torkenczy (ORCID) [contributor]

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

• Satija Lab and Collaborators [funder]

See Also

Useful links:

• https://satijalab.org/seurat

• https://github.com/satijalab/seurat

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

Add Azimuth Results

Description

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

Usage

AddAzimuthResults(object = NULL, filename)

Arguments

Value

object with Azimuth results added

Examples

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

## End(Not run)

Add Azimuth Scores

Description

Add mapping and prediction scores from Azimuth to a Seurat object

Usage

AddAzimuthScores(object, filename)

Arguments

Value

object with the mapping scores added

Examples

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

## End(Not run)

Calculate module scores for feature expression programs in single cells

Description

Calculate the average expression levels of each program (cluster) on single cell level, subtracted by the aggregated expression of control feature sets. All analyzed features are binned based on averaged expression, and the control features are randomly selected from each bin.

Calculate the average expression levels of each program (cluster) on single cell level, subtracted by the aggregated expression of control feature sets. All analyzed features are binned based on averaged expression, and the control features are randomly selected from each bin.

Usage

AddModuleScore(object, ...)

## S3 method for class 'Seurat'
AddModuleScore(
  object,
  features,
  pool = NULL,
  nbin = 24,
  ctrl = 100,
  k = FALSE,
  assay = NULL,
  name = "Cluster",
  seed = 1,
  search = FALSE,
  slot = "data",
  ...
)

## S3 method for class 'StdAssay'
AddModuleScore(
  object,
  features,
  kmeans.obj,
  pool = NULL,
  nbin = 24,
  ctrl = 100,
  k = FALSE,
  name = "Cluster",
  seed = 1,
  search = FALSE,
  slot = "data",
  ...
)

## S3 method for class 'Assay'
AddModuleScore(
  object,
  features,
  kmeans.obj,
  pool = NULL,
  nbin = 24,
  ctrl = 100,
  k = FALSE,
  name = "Cluster",
  seed = 1,
  search = FALSE,
  slot = "data",
  ...
)

Arguments

Value

Returns a Seurat object with module scores added to object meta data; each module is stored as name# for each module program present in features

Returns a Seurat object with module scores added to object meta data; each module is stored as name# for each module program present in features

References

Tirosh et al, Science (2016)

Tirosh et al, Science (2016)

Tirosh et al, Science (2016)

Examples

## Not run: 
data("pbmc_small")
cd_features <- list(c(
  'CD79B',
  'CD79A',
  'CD19',
  'CD180',
  'CD200',
  'CD3D',
  'CD2',
  'CD3E',
  'CD7',
  'CD8A',
  'CD14',
  'CD1C',
  'CD68',
  'CD9',
  'CD247'
))
pbmc_small <- AddModuleScore(
  object = pbmc_small,
  features = cd_features,
  ctrl = 5,
  name = 'CD_Features'
)
head(x = pbmc_small[])

## End(Not run)

Aggregated feature expression by identity class

Description

Returns summed counts ("pseudobulk") for each identity class.

Usage

AggregateExpression(
  object,
  assays = NULL,
  features = NULL,
  return.seurat = FALSE,
  group.by = "ident",
  add.ident = NULL,
  normalization.method = "LogNormalize",
  scale.factor = 10000,
  margin = 1,
  verbose = TRUE,
  ...
)

Arguments

Details

If return.seurat = TRUE, aggregated values are placed in the 'counts' layer of the returned object. The data is then normalized by running NormalizeData on the aggregated counts. ScaleData is then run on the default assay before returning the object.

Value

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

Examples

## Not run: 
data("pbmc_small")
head(AggregateExpression(object = pbmc_small)$RNA)
head(AggregateExpression(object = pbmc_small, group.by = c('ident', 'groups'))$RNA)

## End(Not run)

The AnchorSet Class

Description

The AnchorSet class is an intermediate data storage class that stores the anchors and other related information needed for performing downstream analyses - namely data integration (IntegrateData) and data transfer (TransferData).

Slots

object.list

List of objects used to create anchors

reference.cells

List of cell names in the reference dataset - needed when performing data transfer.

reference.objects

Position of reference object/s in object.list

query.cells

List of cell names in the query dataset - needed when performing data transfer

anchors

The anchor matrix. This contains the cell indices of both anchor pair cells, the anchor score, and the index of the original dataset in the object.list for cell1 and cell2 of the anchor.

offsets

The offsets used to enable cell look up in downstream functions

weight.reduction

The weight dimensional reduction used to calculate weight matrix

anchor.features

The features used when performing anchor finding.

neighbors

List containing Neighbor objects for reuse later (e.g. mapping)

command

Store log of parameters that were used

Add info to anchor matrix

Description

Add info to anchor matrix

Usage

AnnotateAnchors(anchors, vars, slot, ...)

## Default S3 method:
AnnotateAnchors(
  anchors,
  vars = NULL,
  slot = NULL,
  object.list,
  assay = NULL,
  ...
)

## S3 method for class 'IntegrationAnchorSet'
AnnotateAnchors(
  anchors,
  vars = NULL,
  slot = NULL,
  object.list = NULL,
  assay = NULL,
  ...
)

## S3 method for class 'TransferAnchorSet'
AnnotateAnchors(
  anchors,
  vars = NULL,
  slot = NULL,
  reference = NULL,
  query = NULL,
  assay = NULL,
  ...
)

Arguments

Value

Returns the anchor dataframe with additional columns for annotation metadata

The Assay Class

Description

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

See Also

SeuratObject::Assay-class

Augments ggplot2-based plot with a PNG image.

Description

Creates "vector-friendly" plots. Does this by saving a copy of the plot as a PNG file, then adding the PNG image with annotation_raster to a blank plot of the same dimensions as plot. Please note: original legends and axes will be lost during augmentation.

Usage

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

Arguments

Value

A ggplot object

Examples

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

## End(Not run)

Automagically calculate a point size for ggplot2-based scatter plots

Description

It happens to look good

Usage

AutoPointSize(data, raster = NULL)

Arguments

Value

The "optimal" point size for visualizing these data

Examples

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

Averaged feature expression by identity class

Description

Returns averaged expression values for each identity class.

Usage

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

Arguments

Details

If layer is set to 'data', this function assumes that the data has been log normalized and therefore feature values are exponentiated prior to averaging so that averaging is done in non-log space. Otherwise, if layer is set to either 'counts' or 'scale.data', no exponentiation is performed prior to averaging. If return.seurat = TRUE and layer is not 'scale.data', averaged values are placed in the 'counts' layer of the returned object and 'log1p' is run on the averaged counts and placed in the 'data' layer ScaleData is then run on the default assay before returning the object. If return.seurat = TRUE and layer is 'scale.data', the 'counts' layer contains average counts and 'scale.data' is set to the averaged values of 'scale.data'.

Value

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

Examples

data("pbmc_small")
head(AverageExpression(object = pbmc_small)$RNA)
head(AverageExpression(object = pbmc_small, group.by = c('ident', 'groups'))$RNA)

Determine text color based on background color

Description

Determine text color based on background color

Usage

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

Arguments

Value

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

Source

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

Examples

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

Plot the Barcode Distribution and Calculated Inflection Points

Description

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

Usage

BarcodeInflectionsPlot(object)

Arguments

Details

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

Value

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

Author(s)

Robert A. Amezquita, robert.amezquita@fredhutch.org

See Also

CalculateBarcodeInflections SubsetByBarcodeInflections

Examples

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

Create a custom color palette

Description

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

Usage

BlackAndWhite(mid = NULL, k = 50)

BlueAndRed(k = 50)

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

PurpleAndYellow(k = 50)

Arguments

Value

A color palette for plotting

Examples

df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2))
plot(df, col = BlackAndWhite())

df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2))
plot(df, col = BlueAndRed())

myPalette <- CustomPalette()
myPalette

df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2))
plot(df, col = PurpleAndYellow())

Construct a dictionary representation for each unimodal dataset

Description

Construct a dictionary representation for each unimodal dataset

Usage

BridgeCellsRepresentation(
  object.list,
  bridge.object,
  object.reduction,
  bridge.reduction,
  laplacian.reduction = "lap",
  laplacian.dims = 1:50,
  bridge.assay.name = "Bridge",
  return.all.assays = FALSE,
  l2.norm = TRUE,
  verbose = TRUE
)

Arguments

Value

Returns a object list in which each object has a bridge cell derived assay

The BridgeReferenceSet Class The BridgeReferenceSet is an output from PrepareBridgeReference

Description

The BridgeReferenceSet Class The BridgeReferenceSet is an output from PrepareBridgeReference

Slots

bridge

The multi-omic object

reference

The Reference object only containing bridge representation assay

params

A list of parameters used in the PrepareBridgeReference

command

Store log of parameters that were used

Phylogenetic Analysis of Identity Classes

Description

Constructs a phylogenetic tree relating the 'aggregate' cell from each identity class. Tree is estimated based on a distance matrix constructed in either gene expression space or PCA space.

Usage

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

Arguments

Details

Note that the tree is calculated for an 'aggregate' cell, so gene expression or PC scores are summed across all cells in an identity class before the tree is constructed.

Value

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

Examples

## Not run: 
if (requireNamespace("ape", quietly = TRUE)) {
  data("pbmc_small")
  pbmc_small
  pbmc_small <- BuildClusterTree(object = pbmc_small)
  Tool(object = pbmc_small, slot = 'BuildClusterTree')
}

## End(Not run)

Construct an assay for spatial niche analysis

Description

This function will construct a new assay where each feature is a cell label The values represents the sum of a particular cell label neighboring a given cell.

Usage

BuildNicheAssay(
  object,
  fov,
  group.by,
  assay = "niche",
  cluster.name = "niches",
  neighbors.k = 20,
  niches.k = 4
)

Arguments

Value

Seurat object containing a new assay

Seurat-CCA Integration

Description

Seurat-CCA Integration

Usage

CCAIntegration(
  object = NULL,
  assay = NULL,
  layers = NULL,
  orig = NULL,
  new.reduction = "integrated.dr",
  reference = NULL,
  features = NULL,
  normalization.method = c("LogNormalize", "SCT"),
  dims = 1:30,
  k.filter = NA,
  scale.layer = "scale.data",
  dims.to.integrate = NULL,
  k.weight = 100,
  weight.reduction = NULL,
  sd.weight = 1,
  sample.tree = NULL,
  preserve.order = FALSE,
  verbose = TRUE,
  ...
)

Arguments

            [,1]  [,2]
       [1,]   -2   -3
       [2,]    1   -1

Examples

## Not run: 
# Preprocessing
obj <- SeuratData::LoadData("pbmcsca")
obj[["RNA"]] <- split(obj[["RNA"]], f = obj$Method)
obj <- NormalizeData(obj)
obj <- FindVariableFeatures(obj)
obj <- ScaleData(obj)
obj <- RunPCA(obj)

# After preprocessing, we integrate layers.
obj <- IntegrateLayers(object = obj, method = CCAIntegration,
  orig.reduction = "pca", new.reduction = "integrated.cca",
  verbose = FALSE)

# Modifying parameters
# We can also specify parameters such as `k.anchor` to increase the strength of integration
obj <- IntegrateLayers(object = obj, method = CCAIntegration,
  orig.reduction = "pca", new.reduction = "integrated.cca",
  k.anchor = 20, verbose = FALSE)

# Integrating SCTransformed data
obj <- SCTransform(object = obj)
obj <- IntegrateLayers(object = obj, method = CCAIntegration,
  orig.reduction = "pca", new.reduction = "integrated.cca",
  assay = "SCT", verbose = FALSE)

## End(Not run)

Calculate dispersion of features

Description

Calculate dispersion of features

Usage

CalcDispersion(
  object,
  mean.function = FastExpMean,
  dispersion.function = FastLogVMR,
  num.bin = 20,
  binning.method = "equal_width",
  verbose = TRUE,
  ...
)

Arguments

Calculate a perturbation Signature

Description

Function to calculate perturbation signature for pooled CRISPR screen datasets. For each target cell (expressing one target gRNA), we identified 20 cells from the control pool (non-targeting cells) with the most similar mRNA expression profiles. The perturbation signature is calculated by subtracting the averaged mRNA expression profile of the non-targeting neighbors from the mRNA expression profile of the target cell.

Usage

CalcPerturbSig(
  object,
  assay = NULL,
  features = NULL,
  slot = "data",
  gd.class = "guide_ID",
  nt.cell.class = "NT",
  split.by = NULL,
  num.neighbors = NULL,
  reduction = "pca",
  ndims = 15,
  new.assay.name = "PRTB",
  verbose = TRUE
)

Arguments

Value

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

Calculate the Barcode Distribution Inflection

Description

This function calculates an adaptive inflection point ("knee") of the barcode distribution for each sample group. This is useful for determining a threshold for removing low-quality samples.

Usage

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

Arguments

Details

The function operates by calculating the slope of the barcode number vs. rank distribution, and then finding the point at which the distribution changes most steeply (the "knee"). Of note, this calculation often must be restricted as to the range at which it performs, so 'threshold' parameters are provided to restrict the range of the calculation based on the rank of the barcodes. [BarcodeInflectionsPlot()] is provided as a convenience function to visualize and test different thresholds and thus provide more sensical end results.

See [BarcodeInflectionsPlot()] to visualize the calculated inflection points and [SubsetByBarcodeInflections()] to subsequently subset the Seurat object.

Value

Returns Seurat object with a new list in the 'tools' slot, 'CalculateBarcodeInflections' with values:

* 'barcode_distribution' - contains the full barcode distribution across the entire dataset * 'inflection_points' - the calculated inflection points within the thresholds * 'threshold_values' - the provided (or default) threshold values to search within for inflections * 'cells_pass' - the cells that pass the inflection point calculation

Author(s)

Robert A. Amezquita, robert.amezquita@fredhutch.org

See Also

BarcodeInflectionsPlot SubsetByBarcodeInflections

Examples

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

Match the case of character vectors

Description

Match the case of character vectors

Usage

CaseMatch(search, match)

Arguments

Value

Values from search present in match with the case of match

Examples

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

Score cell cycle phases

Description

Score cell cycle phases

Usage

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

Arguments

Value

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

See Also

AddModuleScore

Examples

## Not run: 
data("pbmc_small")
# pbmc_small doesn't have any cell-cycle genes
# To run CellCycleScoring, please use a dataset with cell-cycle genes
# An example is available at http://satijalab.org/seurat/cell_cycle_vignette.html
pbmc_small <- CellCycleScoring(
  object = pbmc_small,
  g2m.features = cc.genes$g2m.genes,
  s.features = cc.genes$s.genes
)
head(x = pbmc_small@meta.data)

## End(Not run)

Cell-cell scatter plot

Description

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

Usage

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

Arguments

Value

A ggplot object

Examples

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

Cell Selector

Description

Select points on a scatterplot and get information about them

Usage

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

FeatureLocator(plot, ...)

Arguments

Value

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

See Also

DimPlot FeaturePlot

Examples

## Not run: 
data("pbmc_small")
plot <- DimPlot(object = pbmc_small)
# Follow instructions in the terminal to select points
cells.located <- CellSelector(plot = plot)
cells.located
# Automatically set the identity class of selected cells and return a new Seurat object
pbmc_small <- CellSelector(plot = plot, object = pbmc_small, ident = 'SelectedCells')

## End(Not run)

Get Cell Names

Description

Get Cell Names

Usage

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

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

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

## S3 method for class 'VisiumV1'
Cells(x, ...)

Arguments

See Also

SeuratObject::Cells

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)

Move outliers towards center on dimension reduction plot

Description

Move outliers towards center on dimension reduction plot

Usage

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

Arguments

Value

Returns a DimReduc object with the modified embeddings

Examples

## Not run: 
data("pbmc_small")
pbmc_small <- FindClusters(pbmc_small, resolution = 1.1)
pbmc_small <- RunUMAP(pbmc_small, dims = 1:5)
DimPlot(pbmc_small, reduction = "umap")
pbmc_small[["umap_new"]] <- CollapseEmbeddingOutliers(pbmc_small,
    reduction = "umap", reduction.key = 'umap_', outlier.sd = 0.5)
DimPlot(pbmc_small, reduction = "umap_new")

## End(Not run)

Slim down a multi-species expression matrix, when only one species is primarily of interenst.

Description

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

Usage

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

Arguments

Value

A UMI count matrix. Rownames that started with prefix have this prefix discarded. For rownames starting with controls, only the ncontrols most highly expressed features are kept, and the prefix is kept. All other rows are retained.

Examples

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

## End(Not run)

Color dimensional reduction plot by tree split

Description

Returns a DimPlot colored based on whether the cells fall in clusters to the left or to the right of a node split in the cluster tree.

Usage

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

Arguments

Value

Returns a DimPlot

See Also

DimPlot

Examples

## Not run: 
if (requireNamespace("ape", quietly = TRUE)) {
  data("pbmc_small")
  pbmc_small <- BuildClusterTree(object = pbmc_small, verbose = FALSE)
  PlotClusterTree(pbmc_small)
  ColorDimSplit(pbmc_small, node = 5)
}

## End(Not run)

Combine ggplot2-based plots into a single plot

Description

Combine ggplot2-based plots into a single plot

Usage

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

Arguments

Value

A combined plot

Examples

data("pbmc_small")
pbmc_small[['group']] <- sample(
  x = c('g1', 'g2'),
  size = ncol(x = pbmc_small),
  replace = TRUE
)
plot1 <- FeaturePlot(
  object = pbmc_small,
  features = 'MS4A1',
  split.by = 'group'
)
plot2 <- FeaturePlot(
  object = pbmc_small,
  features = 'FCN1',
  split.by = 'group'
)
CombinePlots(
  plots = list(plot1, plot2),
  legend = 'none',
  nrow = length(x = unique(x = pbmc_small[['group', drop = TRUE]]))
)

Generate CountSketch random matrix

Description

Generate CountSketch random matrix

Usage

CountSketch(nsketch, ncells, seed = NA_integer_, ...)

Arguments

Value

...

References

Clarkson, KL. & Woodruff, DP. Low-rank approximation and regression in input sparsity time. Journal of the ACM (JACM). 2017 Jan 30;63(6):1-45. doi:10.1145/3019134;

Create one hot matrix for a given label

Description

Create one hot matrix for a given label

Usage

CreateCategoryMatrix(
  labels,
  method = c("aggregate", "average"),
  cells.name = NULL
)

Arguments

Create a SCT Assay object

Description

Create a SCT object from a feature (e.g. gene) expression matrix and a list of SCTModels. The expected format of the input matrix is features x cells.

Usage

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

Arguments

Details

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

Run a custom distance function on an input data matrix

Description

Run a custom distance function on an input data matrix

Usage

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

Arguments

Value

A distance matrix

Author(s)

Jean Fan

Examples

data("pbmc_small")
# Define custom distance matrix
manhattan.distance <- function(x, y) return(sum(abs(x-y)))

input.data <- GetAssayData(pbmc_small, assay.type = "RNA", slot = "scale.data")
cell.manhattan.dist <- CustomDistance(input.data, manhattan.distance)

DE and EnrichR pathway visualization barplot

Description

DE and EnrichR pathway visualization barplot

Usage

DEenrichRPlot(
  object,
  ident.1 = NULL,
  ident.2 = NULL,
  balanced = TRUE,
  logfc.threshold = 0.25,
  assay = NULL,
  max.genes,
  test.use = "wilcox",
  p.val.cutoff = 0.05,
  cols = NULL,
  enrich.database = NULL,
  num.pathway = 10,
  return.gene.list = FALSE,
  ...
)

Arguments

Value

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

Find variable features based on dispersion

Description

Find variable features based on dispersion

Usage

DISP(data, nselect = 2000L, verbose = TRUE, ...)

Arguments

Slim down a Seurat object

Description

Keep only certain aspects of the Seurat object. Can be useful in functions that utilize merge as it reduces the amount of data in the merge

Usage

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

Arguments

Value

object with only the sub-object specified retained

Dimensional reduction heatmap

Description

Draws a heatmap focusing on a principal component. Both cells and genes are sorted by their principal component scores. Allows for nice visualization of sources of heterogeneity in the dataset.

Usage

DimHeatmap(
  object,
  dims = 1,
  nfeatures = 30,
  cells = NULL,
  reduction = "pca",
  disp.min = -2.5,
  disp.max = NULL,
  balanced = TRUE,
  projected = FALSE,
  ncol = NULL,
  fast = TRUE,
  raster = TRUE,
  slot = "scale.data",
  assays = NULL,
  combine = TRUE
)

PCHeatmap(object, ...)

Arguments

Value

No return value by default. If using fast = FALSE, will return a patchworked ggplot object if combine = TRUE, otherwise returns a list of ggplot objects

See Also

image geom_raster

Examples

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

Dimensional reduction plot

Description

Graphs the output of a dimensional reduction technique on a 2D scatter plot where each point is a cell and it's positioned based on the cell embeddings determined by the reduction technique. By default, cells are colored by their identity class (can be changed with the group.by parameter).

Usage

DimPlot(
  object,
  dims = c(1, 2),
  cells = NULL,
  cols = NULL,
  pt.size = NULL,
  reduction = NULL,
  group.by = NULL,
  split.by = NULL,
  shape.by = NULL,
  order = NULL,
  shuffle = FALSE,
  seed = 1,
  label = FALSE,
  label.size = 4,
  label.color = "black",
  label.box = FALSE,
  repel = FALSE,
  alpha = 1,
  stroke.size = NULL,
  cells.highlight = NULL,
  cols.highlight = "#DE2D26",
  sizes.highlight = 1,
  na.value = "grey50",
  ncol = NULL,
  combine = TRUE,
  raster = NULL,
  raster.dpi = c(512, 512)
)

PCAPlot(object, ...)

TSNEPlot(object, ...)

UMAPPlot(object, ...)

Arguments

Value

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

Note

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

See Also

FeaturePlot HoverLocator CellSelector FetchData

Examples

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

The DimReduc Class

Description

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

See Also

SeuratObject::DimReduc-class

Discrete colour palettes from pals

Description

These are included here because pals depends on a number of compiled packages, and this can lead to increases in run time for Travis, and generally should be avoided when possible.

Usage

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

Arguments

Details

These palettes are a much better default for data with many classes than the default ggplot2 palette.

Many thanks to Kevin Wright for writing the pals package.

Taken from the pals package (Licence: GPL-3). https://cran.r-project.org/package=pals Credit: Kevin Wright

Value

A vector of colors

Feature expression heatmap

Description

Draws a heatmap of single cell feature expression.

Usage

DoHeatmap(
  object,
  features = NULL,
  cells = NULL,
  group.by = "ident",
  group.bar = TRUE,
  group.colors = NULL,
  disp.min = -2.5,
  disp.max = NULL,
  slot = "scale.data",
  assay = NULL,
  label = TRUE,
  size = 5.5,
  hjust = 0,
  vjust = 0,
  angle = 45,
  raster = TRUE,
  draw.lines = TRUE,
  lines.width = NULL,
  group.bar.height = 0.02,
  combine = TRUE
)

Arguments

Value

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

Examples

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

Dot plot visualization

Description

Intuitive way of visualizing how feature expression changes across different identity classes (clusters). The size of the dot encodes the percentage of cells within a class, while the color encodes the AverageExpression level across all cells within a class (blue is high).

Usage

DotPlot(
  object,
  features,
  assay = NULL,
  cols = c("lightgrey", "blue"),
  col.min = -2.5,
  col.max = 2.5,
  dot.min = 0,
  dot.scale = 6,
  idents = NULL,
  group.by = NULL,
  split.by = NULL,
  cluster.idents = FALSE,
  scale = TRUE,
  scale.by = "radius",
  scale.min = NA,
  scale.max = NA
)

Arguments

Value

A ggplot object

See Also

RColorBrewer::brewer.pal.info

Examples

data("pbmc_small")
cd_genes <- c("CD247", "CD3E", "CD9")
DotPlot(object = pbmc_small, features = cd_genes)
pbmc_small[['groups']] <- sample(x = c('g1', 'g2'), size = ncol(x = pbmc_small), replace = TRUE)
DotPlot(object = pbmc_small, features = cd_genes, split.by = 'groups')

Quickly Pick Relevant Dimensions

Description

Plots the standard deviations (or approximate singular values if running PCAFast) of the principle components for easy identification of an elbow in the graph. This elbow often corresponds well with the significant dims and is much faster to run than Jackstraw

Usage

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

Arguments

Value

A ggplot object

Examples

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

Calculate the mean of logged values

Description

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

Usage

ExpMean(x, ...)

Arguments

Value

Returns the mean in log-space

Examples

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

Calculate the standard deviation of logged values

Description

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

Usage

ExpSD(x)

Arguments

Value

Returns the standard deviation in log-space

Examples

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

Calculate the variance of logged values

Description

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

Usage

ExpVar(x)

Arguments

Value

Returns the variance in log-space

Examples

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

Perform integration on the joint PCA cell embeddings.

Description

This is a convenience wrapper function around the following three functions that are often run together when perform integration. FindIntegrationAnchors, RunPCA, IntegrateEmbeddings.

Usage

FastRPCAIntegration(
  object.list,
  reference = NULL,
  anchor.features = 2000,
  k.anchor = 20,
  dims = 1:30,
  scale = TRUE,
  normalization.method = c("LogNormalize", "SCT"),
  new.reduction.name = "integrated_dr",
  npcs = 50,
  findintegrationanchors.args = list(),
  verbose = TRUE
)

Arguments

Value

Returns a Seurat object with integrated dimensional reduction

Scale and/or center matrix rowwise

Description

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

Usage

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

Arguments

Value

Returns the center/scaled matrix

Visualize 'features' on a dimensional reduction plot

Description

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

Usage

FeaturePlot(
  object,
  features,
  dims = c(1, 2),
  cells = NULL,
  cols = if (blend) {
     c("lightgrey", "#ff0000", "#00ff00")
 } else {
    
    c("lightgrey", "blue")
 },
  pt.size = NULL,
  alpha = 1,
  order = FALSE,
  min.cutoff = NA,
  max.cutoff = NA,
  reduction = NULL,
  split.by = NULL,
  keep.scale = "feature",
  shape.by = NULL,
  slot = "data",
  blend = FALSE,
  blend.threshold = 0.5,
  label = FALSE,
  label.size = 4,
  label.color = "black",
  repel = FALSE,
  ncol = NULL,
  coord.fixed = FALSE,
  by.col = TRUE,
  sort.cell = deprecated(),
  interactive = FALSE,
  combine = TRUE,
  raster = NULL,
  raster.dpi = c(512, 512)
)

Arguments

Value

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

Note

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

See Also

DimPlot HoverLocator CellSelector

Examples

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

Scatter plot of single cell data

Description

Creates a scatter plot of two features (typically feature expression), across a set of single cells. Cells are colored by their identity class. Pearson correlation between the two features is displayed above the plot.

Usage

FeatureScatter(
  object,
  feature1,
  feature2,
  cells = NULL,
  shuffle = FALSE,
  seed = 1,
  group.by = NULL,
  split.by = NULL,
  cols = NULL,
  pt.size = 1,
  shape.by = NULL,
  span = NULL,
  smooth = FALSE,
  combine = TRUE,
  slot = "data",
  plot.cor = TRUE,
  ncol = NULL,
  raster = NULL,
  raster.dpi = c(512, 512),
  jitter = FALSE,
  log = FALSE
)

Arguments

Value

A ggplot object

Examples

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

Calculate pearson residuals of features not in the scale.data This function is the secondary function under FetchResiduals

Description

Calculate pearson residuals of features not in the scale.data This function is the secondary function under FetchResiduals

Usage

FetchResidualSCTModel(
  object,
  umi.object,
  layer = "counts",
  chunk_size = 2000,
  layer.cells = NULL,
  SCTModel = NULL,
  reference.SCT.model = NULL,
  new_features = NULL,
  clip.range = NULL,
  replace.value = FALSE,
  verbose = FALSE
)

Arguments

Value

Returns a matrix containing centered pearson residuals of added features

Get the Pearson residuals from an sctransform-normalized dataset.

Description

This function calls sctransform::get_residuals.

Usage

FetchResiduals(object, ...)

## S3 method for class 'Seurat'
FetchResiduals(
  object,
  features,
  assay = NULL,
  umi.assay = "RNA",
  layer = "counts",
  clip.range = NULL,
  reference.SCT.model = NULL,
  replace.value = FALSE,
  na.rm = TRUE,
  verbose = TRUE,
  ...
)

## S3 method for class 'SCTAssay'
FetchResiduals(
  object,
  umi.object,
  features,
  layer = "counts",
  clip.range = NULL,
  reference.SCT.model = NULL,
  replace.value = FALSE,
  na.rm = TRUE,
  verbose = TRUE,
  ...
)

Arguments

Value

A matrix containing the requested pearson residuals.

See Also

get_residuals

temporal function to get residuals from reference

Description

temporal function to get residuals from reference

Usage

FetchResiduals_reference(
  object,
  reference.SCT.model = NULL,
  features = NULL,
  nCount_UMI = NULL,
  verbose = FALSE
)

Arguments

Filter stray beads from Slide-seq puck

Description

This function is useful for removing stray beads that fall outside the main Slide-seq puck area. Essentially, it's a circular filter where you set a center and radius defining a circle of beads to keep. If the center is not set, it will be estimated from the bead coordinates (removing the 1st and 99th quantile to avoid skewing the center by the stray beads). By default, this function will display a SpatialDimPlot showing which cells were removed for easy adjustment of the center and/or radius.

Usage

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

Arguments

Value

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

Examples

## Not run: 
# This example uses the ssHippo dataset which you can download
# using the SeuratData package.
library(SeuratData)
data('ssHippo')
# perform filtering of beads
ssHippo.filtered <- FilterSlideSeq(ssHippo, radius = 2300)
# This radius looks to small so increase and repeat until satisfied

## End(Not run)

Gene expression markers for all identity classes

Description

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

Usage

FindAllMarkers(
  object,
  assay = NULL,
  features = NULL,
  group.by = NULL,
  logfc.threshold = 0.1,
  test.use = "wilcox",
  slot = "data",
  min.pct = 0.01,
  min.diff.pct = -Inf,
  node = NULL,
  verbose = TRUE,
  only.pos = FALSE,
  max.cells.per.ident = Inf,
  random.seed = 1,
  latent.vars = NULL,
  min.cells.feature = 3,
  min.cells.group = 3,
  mean.fxn = NULL,
  fc.name = NULL,
  base = 2,
  return.thresh = 0.01,
  densify = FALSE,
  ...
)

Arguments

Value

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

Examples

data("pbmc_small")
# Find markers for all clusters
all.markers <- FindAllMarkers(object = pbmc_small)
head(x = all.markers)
## Not run: 
# Pass a value to node as a replacement for FindAllMarkersNode
pbmc_small <- BuildClusterTree(object = pbmc_small)
all.markers <- FindAllMarkers(object = pbmc_small, node = 4)
head(x = all.markers)

## End(Not run)

Find bridge anchors between two unimodal datasets

Description

First, bridge object is used to reconstruct two single-modality profiles and then project those cells into bridage graph laplacian space. Next, find a set of anchors between two single-modality objects. These anchors can later be used to integrate embeddings or transfer data from the reference to query object using the MapQuery object.

Usage

FindBridgeAnchor(
  object.list,
  bridge.object,
  object.reduction,
  bridge.reduction,
  anchor.type = c("Transfer", "Integration"),
  reference = NULL,
  laplacian.reduction = "lap",
  laplacian.dims = 1:50,
  reduction = c("direct", "cca"),
  bridge.assay.name = "Bridge",
  reference.bridge.stored = FALSE,
  k.anchor = 20,
  k.score = 50,
  verbose = TRUE,
  ...
)

Arguments

Details

• Bridge cells reconstruction

• Find anchors between objects. It can be either IntegrationAnchors or TransferAnchor.

Value

Returns an AnchorSet object that can be used as input to IntegrateEmbeddings.or MapQuery

Find integration bridge anchors between query and extended bridge-reference

Description

Find a set of anchors between unimodal query and the other unimodal reference using a pre-computed BridgeReferenceSet. These integration anchors can later be used to integrate query and reference using the IntegrateEmbeddings object.

Usage

FindBridgeIntegrationAnchors(
  extended.reference,
  query,
  query.assay = NULL,
  dims = 1:30,
  scale = FALSE,
  reduction = c("lsiproject", "pcaproject"),
  integration.reduction = c("direct", "cca"),
  verbose = TRUE
)

Arguments

Value

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

Find bridge anchors between query and extended bridge-reference

Description

Find a set of anchors between unimodal query and the other unimodal reference using a pre-computed BridgeReferenceSet. This function performs three steps: 1. Harmonize the bridge and query cells in the bridge query reduction space 2. Construct the bridge dictionary representations for query cells 3. Find a set of anchors between query and reference in the bridge graph laplacian eigenspace These anchors can later be used to integrate embeddings or transfer data from the reference to query object using the MapQuery object.

Usage

FindBridgeTransferAnchors(
  extended.reference,
  query,
  query.assay = NULL,
  dims = 1:30,
  scale = FALSE,
  reduction = c("lsiproject", "pcaproject"),
  bridge.reduction = c("direct", "cca"),
  verbose = TRUE
)

Arguments

Value

Returns an AnchorSet object that can be used as input to TransferData, IntegrateEmbeddings and MapQuery.

Cluster Determination

Description

Identify clusters of cells by a shared nearest neighbor (SNN) modularity optimization based clustering algorithm. First calculate k-nearest neighbors and construct the SNN graph. Then optimize the modularity function to determine clusters. For a full description of the algorithms, see Waltman and van Eck (2013) The European Physical Journal B. Thanks to Nigel Delaney (evolvedmicrobe@github) for the rewrite of the Java modularity optimizer code in Rcpp!

Usage

FindClusters(object, ...)

## Default S3 method:
FindClusters(
  object,
  modularity.fxn = 1,
  initial.membership = NULL,
  node.sizes = NULL,
  resolution = 0.8,
  method = deprecated(),
  algorithm = 1,
  n.start = 10,
  n.iter = 10,
  random.seed = 0,
  group.singletons = TRUE,
  temp.file.location = NULL,
  edge.file.name = NULL,
  verbose = TRUE,
  ...
)

## S3 method for class 'Seurat'
FindClusters(
  object,
  graph.name = NULL,
  cluster.name = NULL,
  modularity.fxn = 1,
  initial.membership = NULL,
  node.sizes = NULL,
  resolution = 0.8,
  method = NULL,
  algorithm = 1,
  n.start = 10,
  n.iter = 10,
  random.seed = 0,
  group.singletons = TRUE,
  temp.file.location = NULL,
  edge.file.name = NULL,
  verbose = TRUE,
  ...
)

Arguments

Details

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

Value

Returns a Seurat object where the idents have been updated with new cluster info; latest clustering results will be stored in object metadata under 'seurat_clusters'. Note that 'seurat_clusters' will be overwritten everytime FindClusters is run

Finds markers that are conserved between the groups

Description

Finds markers that are conserved between the groups

Usage

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

Arguments

Value

data.frame containing a ranked list of putative conserved markers, and associated statistics (p-values within each group and a combined p-value (such as Fishers combined p-value or others from the metap package), percentage of cells expressing the marker, average differences). Name of group is appended to each associated output column (e.g. CTRL_p_val). If only one group is tested in the grouping.var, max and combined p-values are not returned.

Examples

## Not run: 
data("pbmc_small")
pbmc_small
# Create a simulated grouping variable
pbmc_small[['groups']] <- sample(x = c('g1', 'g2'), size = ncol(x = pbmc_small), replace = TRUE)
FindConservedMarkers(pbmc_small, ident.1 = 0, ident.2 = 1, grouping.var = "groups")

## End(Not run)

Find integration anchors

Description

Find a set of anchors between a list of Seurat objects. These anchors can later be used to integrate the objects using the IntegrateData function.

Usage

FindIntegrationAnchors(
  object.list = NULL,
  assay = NULL,
  reference = NULL,
  anchor.features = 2000,
  scale = TRUE,
  normalization.method = c("LogNormalize", "SCT"),
  sct.clip.range = NULL,
  reduction = c("cca", "rpca", "jpca", "rlsi"),
  l2.norm = TRUE,
  dims = 1:30,
  k.anchor = 5,
  k.filter = 200,
  k.score = 30,
  max.features = 200,
  nn.method = "annoy",
  n.trees = 50,
  eps = 0,
  verbose = TRUE
)

Arguments

Details

The main steps of this procedure are outlined below. For a more detailed description of the methodology, please see Stuart, Butler, et al Cell 2019: doi:10.1016/j.cell.2019.05.031; doi:10.1101/460147

First, determine anchor.features if not explicitly specified using SelectIntegrationFeatures. Then for all pairwise combinations of reference and query datasets:

• Perform dimensional reduction on the dataset pair as specified via the reduction parameter. If l2.norm is set to TRUE, perform L2 normalization of the embedding vectors.

• Identify anchors - pairs of cells from each dataset that are contained within each other's neighborhoods (also known as mutual nearest neighbors).

• Filter low confidence anchors to ensure anchors in the low dimension space are in broad agreement with the high dimensional measurements. This is done by looking at the neighbors of each query cell in the reference dataset using max.features to define this space. If the reference cell isn't found within the first k.filter neighbors, remove the anchor.

• Assign each remaining anchor a score. For each anchor cell, determine the nearest k.score anchors within its own dataset and within its pair's dataset. Based on these neighborhoods, construct an overall neighbor graph and then compute the shared neighbor overlap between anchor and query cells (analogous to an SNN graph). We use the 0.01 and 0.90 quantiles on these scores to dampen outlier effects and rescale to range between 0-1.

Value

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

References

Stuart T, Butler A, et al. Comprehensive Integration of Single-Cell Data. Cell. 2019;177:1888-1902 doi:10.1016/j.cell.2019.05.031

Examples

## Not run: 
# to install the SeuratData package see https://github.com/satijalab/seurat-data
library(SeuratData)
data("panc8")

# panc8 is a merged Seurat object containing 8 separate pancreas datasets
# split the object by dataset
pancreas.list <- SplitObject(panc8, split.by = "tech")

# perform standard preprocessing on each object
for (i in 1:length(pancreas.list)) {
  pancreas.list[[i]] <- NormalizeData(pancreas.list[[i]], verbose = FALSE)
  pancreas.list[[i]] <- FindVariableFeatures(
    pancreas.list[[i]], selection.method = "vst",
    nfeatures = 2000, verbose = FALSE
  )
}

# find anchors
anchors <- FindIntegrationAnchors(object.list = pancreas.list)

# integrate data
integrated <- IntegrateData(anchorset = anchors)

## End(Not run)

Gene expression markers of identity classes

Description

Finds markers (differentially expressed genes) for identity classes

Usage

FindMarkers(object, ...)

## Default S3 method:
FindMarkers(
  object,
  slot = "data",
  cells.1 = NULL,
  cells.2 = NULL,
  features = NULL,
  logfc.threshold = 0.1,
  test.use = "wilcox",
  min.pct = 0.01,
  min.diff.pct = -Inf,
  verbose = TRUE,
  only.pos = FALSE,
  max.cells.per.ident = Inf,
  random.seed = 1,
  latent.vars = NULL,
  min.cells.feature = 3,
  min.cells.group = 3,
  fc.results = NULL,
  densify = FALSE,
  ...
)

## S3 method for class 'Assay'
FindMarkers(
  object,
  slot = "data",
  cells.1 = NULL,
  cells.2 = NULL,
  features = NULL,
  test.use = "wilcox",
  fc.slot = "data",
  pseudocount.use = 1,
  norm.method = NULL,
  mean.fxn = NULL,
  fc.name = NULL,
  base = 2,
  ...
)

## S3 method for class 'SCTAssay'
FindMarkers(
  object,
  cells.1 = NULL,
  cells.2 = NULL,
  features = NULL,
  test.use = "wilcox",
  pseudocount.use = 1,
  slot = "data",
  fc.slot = "data",
  mean.fxn = NULL,
  fc.name = NULL,
  base = 2,
  recorrect_umi = TRUE,
  ...
)

## S3 method for class 'DimReduc'
FindMarkers(
  object,
  cells.1 = NULL,
  cells.2 = NULL,
  features = NULL,
  logfc.threshold = 0.1,
  test.use = "wilcox",
  min.pct = 0.01,
  min.diff.pct = -Inf,
  verbose = TRUE,
  only.pos = FALSE,
  max.cells.per.ident = Inf,
  random.seed = 1,
  latent.vars = NULL,
  min.cells.feature = 3,
  min.cells.group = 3,
  densify = FALSE,
  mean.fxn = rowMeans,
  fc.name = NULL,
  ...
)

## S3 method for class 'Seurat'
FindMarkers(
  object,
  ident.1 = NULL,
  ident.2 = NULL,
  latent.vars = NULL,
  group.by = NULL,
  subset.ident = NULL,
  assay = NULL,
  reduction = NULL,
  ...
)

Arguments

Details

p-value adjustment is performed using bonferroni correction based on the total number of genes in the dataset. Other correction methods are not recommended, as Seurat pre-filters genes using the arguments above, reducing the number of tests performed. Lastly, as Aaron Lun has pointed out, p-values should be interpreted cautiously, as the genes used for clustering are the same genes tested for differential expression.

Value

data.frame with a ranked list of putative markers as rows, and associated statistics as columns (p-values, ROC score, etc., depending on the test used (test.use)). The following columns are always present:

• avg_logFC: log fold-chage of the average expression between the two groups. Positive values indicate that the gene is more highly expressed in the first group

• pct.1: The percentage of cells where the gene is detected in the first group

• pct.2: The percentage of cells where the gene is detected in the second group

• p_val_adj: Adjusted p-value, based on bonferroni correction using all genes in the dataset

References

McDavid A, Finak G, Chattopadyay PK, et al. Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments. Bioinformatics. 2013;29(4):461-467. doi:10.1093/bioinformatics/bts714

Trapnell C, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature Biotechnology volume 32, pages 381-386 (2014)

Andrew McDavid, Greg Finak and Masanao Yajima (2017). MAST: Model-based Analysis of Single Cell Transcriptomics. R package version 1.2.1. https://github.com/RGLab/MAST/

Love MI, Huber W and Anders S (2014). "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2." Genome Biology. https://bioconductor.org/packages/release/bioc/html/DESeq2.html

See Also

FoldChange

Examples

## Not run: 
data("pbmc_small")
# Find markers for cluster 2
markers <- FindMarkers(object = pbmc_small, ident.1 = 2)
head(x = markers)

# Take all cells in cluster 2, and find markers that separate cells in the 'g1' group (metadata
# variable 'group')
markers <- FindMarkers(pbmc_small, ident.1 = "g1", group.by = 'groups', subset.ident = "2")
head(x = markers)

# Pass 'clustertree' or an object of class phylo to ident.1 and
# a node to ident.2 as a replacement for FindMarkersNode
if (requireNamespace("ape", quietly = TRUE)) {
  pbmc_small <- BuildClusterTree(object = pbmc_small)
  markers <- FindMarkers(object = pbmc_small, ident.1 = 'clustertree', ident.2 = 5)
  head(x = markers)
}

## End(Not run)

Construct weighted nearest neighbor graph

Description

This function will construct a weighted nearest neighbor (WNN) graph. For each cell, we identify the nearest neighbors based on a weighted combination of two modalities. Takes as input two dimensional reductions, one computed for each modality.Other parameters are listed for debugging, but can be left as default values.

Usage

FindMultiModalNeighbors(
  object,
  reduction.list,
  dims.list,
  k.nn = 20,
  l2.norm = TRUE,
  knn.graph.name = "wknn",
  snn.graph.name = "wsnn",
  weighted.nn.name = "weighted.nn",
  modality.weight.name = NULL,
  knn.range = 200,
  prune.SNN = 1/15,
  sd.scale = 1,
  cross.contant.list = NULL,
  smooth = FALSE,
  return.intermediate = FALSE,
  modality.weight = NULL,
  verbose = TRUE
)

Arguments

Value

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

(Shared) Nearest-neighbor graph construction

Description

Computes the k.param nearest neighbors for a given dataset. Can also optionally (via compute.SNN), construct a shared nearest neighbor graph by calculating the neighborhood overlap (Jaccard index) between every cell and its k.param nearest neighbors.

Usage

FindNeighbors(object, ...)

## Default S3 method:
FindNeighbors(
  object,
  query = NULL,
  distance.matrix = FALSE,
  k.param = 20,
  return.neighbor = FALSE,
  compute.SNN = !return.neighbor,
  prune.SNN = 1/15,
  nn.method = "annoy",
  n.trees = 50,
  annoy.metric = "euclidean",
  nn.eps = 0,
  verbose = TRUE,
  l2.norm = FALSE,
  cache.index = FALSE,
  index = NULL,
  ...
)

## S3 method for class 'Assay'
FindNeighbors(
  object,
  features = NULL,
  k.param = 20,
  return.neighbor = FALSE,
  compute.SNN = !return.neighbor,
  prune.SNN = 1/15,
  nn.method = "annoy",
  n.trees = 50,
  annoy.metric = "euclidean",
  nn.eps = 0,
  verbose = TRUE,
  l2.norm = FALSE,
  cache.index = FALSE,
  ...
)

## S3 method for class 'dist'
FindNeighbors(
  object,
  k.param = 20,
  return.neighbor = FALSE,
  compute.SNN = !return.neighbor,
  prune.SNN = 1/15,
  nn.method = "annoy",
  n.trees = 50,
  annoy.metric = "euclidean",
  nn.eps = 0,
  verbose = TRUE,
  l2.norm = FALSE,
  cache.index = FALSE,
  ...
)

## S3 method for class 'Seurat'
FindNeighbors(
  object,
  reduction = "pca",
  dims = 1:10,
  assay = NULL,
  features = NULL,
  k.param = 20,
  return.neighbor = FALSE,
  compute.SNN = !return.neighbor,
  prune.SNN = 1/15,
  nn.method = "annoy",
  n.trees = 50,
  annoy.metric = "euclidean",
  nn.eps = 0,
  verbose = TRUE,
  do.plot = FALSE,
  graph.name = NULL,
  l2.norm = FALSE,
  cache.index = FALSE,
  ...
)

Arguments

Value

This function can either return a Neighbor object with the KNN information or a list of Graph objects with the KNN and SNN depending on the settings of return.neighbor and compute.SNN. When running on a Seurat object, this returns the Seurat object with the Graphs or Neighbor objects stored in their respective slots. Names of the Graph or Neighbor object can be found with Graphs or Neighbors.

Examples

data("pbmc_small")
pbmc_small
# Compute an SNN on the gene expression level
pbmc_small <- FindNeighbors(pbmc_small, features = VariableFeatures(object = pbmc_small))

# More commonly, we build the SNN on a dimensionally reduced form of the data
# such as the first 10 principle components.

pbmc_small <- FindNeighbors(pbmc_small, reduction = "pca", dims = 1:10)

Find spatially variable features

Description

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

Usage

FindSpatiallyVariableFeatures(object, ...)

## Default S3 method:
FindSpatiallyVariableFeatures(
  object,
  spatial.location,
  selection.method = c("markvariogram", "moransi"),
  r.metric = 5,
  x.cuts = NULL,
  y.cuts = NULL,
  verbose = TRUE,
  ...
)

## S3 method for class 'Assay'
FindSpatiallyVariableFeatures(
  object,
  layer = "scale.data",
  slot = deprecated(),
  spatial.location,
  selection.method = c("markvariogram", "moransi"),
  features = NULL,
  r.metric = 5,
  x.cuts = NULL,
  y.cuts = NULL,
  nfeatures = nfeatures,
  verbose = TRUE,
  ...
)

## S3 method for class 'Seurat'
FindSpatiallyVariableFeatures(
  object,
  assay = NULL,
  layer = "scale.data",
  slot = NULL,
  features = NULL,
  image = NULL,
  selection.method = c("markvariogram", "moransi"),
  r.metric = 5,
  x.cuts = NULL,
  y.cuts = NULL,
  nfeatures = 2000,
  verbose = TRUE,
  ...
)

## S3 method for class 'StdAssay'
FindSpatiallyVariableFeatures(
  object,
  layer = "scale.data",
  slot = deprecated(),
  spatial.location,
  selection.method = c("markvariogram", "moransi"),
  features = NULL,
  r.metric = 5,
  x.cuts = NULL,
  y.cuts = NULL,
  nfeatures = nfeatures,
  verbose = TRUE,
  ...
)

Arguments

Find subclusters under one cluster

Description

Find subclusters under one cluster

Usage

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

Arguments

Value

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

Find transfer anchors

Description

Find a set of anchors between a reference and query object. These anchors can later be used to transfer data from the reference to query object using the TransferData object.

Usage

FindTransferAnchors(
  reference,
  query,
  normalization.method = "LogNormalize",
  recompute.residuals = TRUE,
  reference.assay = NULL,
  reference.neighbors = NULL,
  query.assay = NULL,
  reduction = "pcaproject",
  reference.reduction = NULL,
  project.query = FALSE,
  features = NULL,
  scale = TRUE,
  npcs = 30,
  l2.norm = TRUE,
  dims = 1:30,
  k.anchor = 5,
  k.filter = NA,
  k.score = 30,
  max.features = 200,
  nn.method = "annoy",
  n.trees = 50,
  eps = 0,
  approx.pca = TRUE,
  mapping.score.k = NULL,
  verbose = TRUE
)

Arguments

Details

The main steps of this procedure are outlined below. For a more detailed description of the methodology, please see Stuart, Butler, et al Cell 2019. doi:10.1016/j.cell.2019.05.031; doi:10.1101/460147

• Perform dimensional reduction. Exactly what is done here depends on the values set for the reduction and project.query parameters. If reduction = "pcaproject", a PCA is performed on either the reference (if project.query = FALSE) or the query (if project.query = TRUE), using the features specified. The data from the other dataset is then projected onto this learned PCA structure. If reduction = "cca", then CCA is performed on the reference and query for this dimensional reduction step. If reduction = "lsiproject", the stored LSI dimension reduction in the reference object is used to project the query dataset onto the reference. If l2.norm is set to TRUE, perform L2 normalization of the embedding vectors.

• Identify anchors between the reference and query - pairs of cells from each dataset that are contained within each other's neighborhoods (also known as mutual nearest neighbors).

• Filter low confidence anchors to ensure anchors in the low dimension space are in broad agreement with the high dimensional measurements. This is done by looking at the neighbors of each query cell in the reference dataset using max.features to define this space. If the reference cell isn't found within the first k.filter neighbors, remove the anchor.

• Assign each remaining anchor a score. For each anchor cell, determine the nearest k.score anchors within its own dataset and within its pair's dataset. Based on these neighborhoods, construct an overall neighbor graph and then compute the shared neighbor overlap between anchor and query cells (analogous to an SNN graph). We use the 0.01 and 0.90 quantiles on these scores to dampen outlier effects and rescale to range between 0-1.

Value

Returns an AnchorSet object that can be used as input to TransferData, IntegrateEmbeddings and MapQuery. The dimension reduction used for finding anchors is stored in the AnchorSet object and can be used for computing anchor weights in downstream functions. Note that only the requested dimensions are stored in the dimension reduction object in the AnchorSet. This means that if dims=2:20 is used, for example, the dimension of the stored reduction is 1:19.

References

Stuart T, Butler A, et al. Comprehensive Integration of Single-Cell Data. Cell. 2019;177:1888-1902 doi:10.1016/j.cell.2019.05.031;

Examples

## Not run: 
# to install the SeuratData package see https://github.com/satijalab/seurat-data
library(SeuratData)
data("pbmc3k")

# for demonstration, split the object into reference and query
pbmc.reference <- pbmc3k[, 1:1350]
pbmc.query <- pbmc3k[, 1351:2700]

# perform standard preprocessing on each object
pbmc.reference <- NormalizeData(pbmc.reference)
pbmc.reference <- FindVariableFeatures(pbmc.reference)
pbmc.reference <- ScaleData(pbmc.reference)

pbmc.query <- NormalizeData(pbmc.query)
pbmc.query <- FindVariableFeatures(pbmc.query)
pbmc.query <- ScaleData(pbmc.query)

# find anchors
anchors <- FindTransferAnchors(reference = pbmc.reference, query = pbmc.query)

# transfer labels
predictions <- TransferData(
  anchorset = anchors,
  refdata = pbmc.reference$seurat_annotations
)
pbmc.query <- AddMetaData(object = pbmc.query, metadata = predictions)

## End(Not run)

Find variable features

Description

Identifies features that are outliers on a 'mean variability plot'.

Usage

FindVariableFeatures(object, ...)

## S3 method for class 'V3Matrix'
FindVariableFeatures(
  object,
  selection.method = "vst",
  loess.span = 0.3,
  clip.max = "auto",
  mean.function = FastExpMean,
  dispersion.function = FastLogVMR,
  num.bin = 20,
  binning.method = "equal_width",
  verbose = TRUE,
  ...
)

## S3 method for class 'Assay'
FindVariableFeatures(
  object,
  selection.method = "vst",
  loess.span = 0.3,
  clip.max = "auto",
  mean.function = FastExpMean,
  dispersion.function = FastLogVMR,
  num.bin = 20,
  binning.method = "equal_width",
  nfeatures = 2000,
  mean.cutoff = c(0.1, 8),
  dispersion.cutoff = c(1, Inf),
  verbose = TRUE,
  ...
)

## S3 method for class 'SCTAssay'
FindVariableFeatures(object, nfeatures = 2000, ...)

## S3 method for class 'Seurat'
FindVariableFeatures(
  object,
  assay = NULL,
  selection.method = "vst",
  loess.span = 0.3,
  clip.max = "auto",
  mean.function = FastExpMean,
  dispersion.function = FastLogVMR,
  num.bin = 20,
  binning.method = "equal_width",
  nfeatures = 2000,
  mean.cutoff = c(0.1, 8),
  dispersion.cutoff = c(1, Inf),
  verbose = TRUE,
  ...
)

Arguments

Details

For the mean.var.plot method: Exact parameter settings may vary empirically from dataset to dataset, and based on visual inspection of the plot. Setting the y.cutoff parameter to 2 identifies features that are more than two standard deviations away from the average dispersion within a bin. The default X-axis function is the mean expression level, and for Y-axis it is the log(Variance/mean). All mean/variance calculations are not performed in log-space, but the results are reported in log-space - see relevant functions for exact details.

Fold Change

Description

Calculate log fold change and percentage of cells expressing each feature for different identity classes.

Usage

FoldChange(object, ...)

## Default S3 method:
FoldChange(object, cells.1, cells.2, mean.fxn, fc.name, features = NULL, ...)

## S3 method for class 'Assay'
FoldChange(
  object,
  cells.1,
  cells.2,
  features = NULL,
  slot = "data",
  pseudocount.use = 1,
  fc.name = NULL,
  mean.fxn = NULL,
  base = 2,
  norm.method = NULL,
  ...
)

## S3 method for class 'SCTAssay'
FoldChange(
  object,
  cells.1,
  cells.2,
  features = NULL,
  slot = "data",
  pseudocount.use = 1,
  fc.name = NULL,
  mean.fxn = NULL,
  base = 2,
  ...
)

## S3 method for class 'DimReduc'
FoldChange(
  object,
  cells.1,
  cells.2,
  features = NULL,
  slot = NULL,
  pseudocount.use = 1,
  fc.name = NULL,
  mean.fxn = NULL,
  ...
)

## S3 method for class 'Seurat'
FoldChange(
  object,
  ident.1 = NULL,
  ident.2 = NULL,
  group.by = NULL,
  subset.ident = NULL,
  assay = NULL,
  slot = "data",
  reduction = NULL,
  features = NULL,
  pseudocount.use = 1,
  mean.fxn = NULL,
  base = 2,
  fc.name = NULL,
  ...
)

Arguments

Details

If the slot is scale.data or a reduction is specified, average difference is returned instead of log fold change and the column is named "avg_diff". Otherwise, log2 fold change is returned with column named "avg_log2_FC".

Value

Returns a data.frame

See Also

FindMarkers

Examples

## Not run: 
data("pbmc_small")
FoldChange(pbmc_small, ident.1 = 1)

## End(Not run)

Gaussian sketching

Description

Gaussian sketching

Usage

GaussianSketch(nsketch, ncells, seed = NA_integer_, ...)

Arguments

Value

...

Get an Assay object from a given Seurat object.

Description

Get an Assay object from a given Seurat object.

Usage

GetAssay(object, ...)

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

Arguments

Value

Returns an Assay object

Examples

data("pbmc_small")
GetAssay(object = pbmc_small, assay = "RNA")

Get Image Data

Description

Get Image Data

Usage

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

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

## S3 method for class 'VisiumV1'
GetImage(object, mode = c("grob", "raster", "plotly", "raw"), ...)

## S3 method for class 'VisiumV2'
GetImage(object, mode = c("grob", "raster", "plotly", "raw"), ...)

Arguments

See Also

SeuratObject::GetImage

Get integration data

Description

Get integration data

Usage

GetIntegrationData(object, integration.name, slot)

Arguments

Value

Returns data from the requested slot within the integrated object

Calculate pearson residuals of features not in the scale.data

Description

This function calls sctransform::get_residuals.

Usage

GetResidual(
  object,
  features,
  assay = NULL,
  umi.assay = "RNA",
  clip.range = NULL,
  replace.value = FALSE,
  na.rm = TRUE,
  verbose = TRUE
)

Arguments

Value

Returns a Seurat object containing Pearson residuals of added features in its scale.data

See Also

get_residuals

Examples

## Not run: 
data("pbmc_small")
pbmc_small <- SCTransform(object = pbmc_small, variable.features.n = 20)
pbmc_small <- GetResidual(object = pbmc_small, features = c('MS4A1', 'TCL1A'))

## End(Not run)

Get Tissue Coordinates

Description

Get Tissue Coordinates

Usage

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

## S3 method for class 'STARmap'
GetTissueCoordinates(object, qhulls = FALSE, ...)

## S3 method for class 'VisiumV1'
GetTissueCoordinates(
  object,
  scale = "lowres",
  cols = c("imagerow", "imagecol"),
  ...
)

## S3 method for class 'VisiumV2'
GetTissueCoordinates(object, scale = NULL, ...)

Arguments

See Also

SeuratObject::GetTissueCoordinates

Get the predicted identity

Description

Utility function to easily pull out the name of the class with the maximum prediction. This is useful if you've set prediction.assay = TRUE in TransferData and want to have a vector with the predicted class.

Usage

GetTransferPredictions(
  object,
  assay = "predictions",
  slot = "data",
  score.filter = 0.75
)

Arguments

Value

Returns a vector of predicted class names

Examples

## Not run: 
  prediction.assay <- TransferData(anchorset = anchors, refdata = reference$class)
  query[["predictions"]] <- prediction.assay
  query$predicted.id <- GetTransferPredictions(query)

## End(Not run)

The Graph Class

Description

For more details, please see the documentation in SeuratObject

See Also

SeuratObject::Graph-class

Compute the correlation of features broken down by groups with another covariate

Description

Compute the correlation of features broken down by groups with another covariate

Usage

GroupCorrelation(
  object,
  assay = NULL,
  slot = "scale.data",
  var = NULL,
  group.assay = NULL,
  min.cells = 5,
  ngroups = 6,
  do.plot = TRUE
)

Arguments

Value

A Seurat object with the correlation stored in metafeatures

Boxplot of correlation of a variable (e.g. number of UMIs) with expression data

Description

Boxplot of correlation of a variable (e.g. number of UMIs) with expression data

Usage

GroupCorrelationPlot(
  object,
  assay = NULL,
  feature.group = "feature.grp",
  cor = "nCount_RNA_cor"
)

Arguments

Value

Returns a ggplot boxplot of correlations split by group

Demultiplex samples based on data from cell 'hashing'

Description

Assign sample-of-origin for each cell, annotate doublets.

Usage

HTODemux(
  object,
  assay = "HTO",
  positive.quantile = 0.99,
  init = NULL,
  nstarts = 100,
  kfunc = "clara",
  nsamples = 100,
  seed = 42,
  verbose = TRUE
)

Arguments

Value

The Seurat object with the following demultiplexed information stored in the meta data:

hash.maxID

Name of hashtag with the highest signal

hash.secondID

Name of hashtag with the second highest signal

hash.margin

The difference between signals for hash.maxID and hash.secondID

classification

Classification result, with doublets/multiplets named by the top two highest hashtags

classification.global

Global classification result (singlet, doublet or negative)

hash.ID

Classification result where doublet IDs are collapsed

See Also

HTOHeatmap

Examples

## Not run: 
object <- HTODemux(object)

## End(Not run)

Hashtag oligo heatmap

Description

Draws a heatmap of hashtag oligo signals across singlets/doublets/negative cells. Allows for the visualization of HTO demultiplexing results.

Usage

HTOHeatmap(
  object,
  assay = "HTO",
  classification = paste0(assay, "_classification"),
  global.classification = paste0(assay, "_classification.global"),
  ncells = 5000,
  singlet.names = NULL,
  raster = TRUE
)

Arguments

Value

Returns a ggplot2 plot object.

See Also

HTODemux

Examples

## Not run: 
object <- HTODemux(object)
HTOHeatmap(object)

## End(Not run)

Get Variable Feature Information

Description

Get variable feature information from SCTAssay objects

Usage

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

Arguments

See Also

HVFInfo

Examples

## Not run: 
# Get the HVF info directly from an SCTAssay object
pbmc_small <- SCTransform(pbmc_small)
HVFInfo(pbmc_small[["SCT"]], method = 'sct')[1:5, ]

## End(Not run)

Harmony Integration

Description

Harmony Integration

Usage

HarmonyIntegration(
  object,
  orig,
  features = NULL,
  scale.layer = "scale.data",
  new.reduction = "harmony",
  layers = NULL,
  npcs = NULL,
  key = "harmony_",
  theta = NULL,
  lambda = NULL,
  sigma = 0.1,
  nclust = NULL,
  tau = 0,
  block.size = 0.05,
  max.iter.harmony = 10L,
  max.iter.cluster = 20L,
  epsilon.cluster = 1e-05,
  epsilon.harmony = 0.01,
  verbose = TRUE,
  ...
)

Arguments

Value

...

Note

This function requires the harmony package to be installed

See Also

harmony::HarmonyMatrix()

Examples

## Not run: 
# Preprocessing
obj <- SeuratData::LoadData("pbmcsca")
obj[["RNA"]] <- split(obj[["RNA"]], f = obj$Method)
obj <- NormalizeData(obj)
obj <- FindVariableFeatures(obj)
obj <- ScaleData(obj)
obj <- RunPCA(obj)

# After preprocessing, we integrate layers with added parameters specific to Harmony:
obj <- IntegrateLayers(object = obj, method = HarmonyIntegration, orig.reduction = "pca",
  new.reduction = 'harmony', verbose = FALSE)

# Modifying Parameters
# We can also add arguments specific to Harmony such as theta, to give more diverse clusters
obj <- IntegrateLayers(object = obj, method = HarmonyIntegration, orig.reduction = "pca",
  new.reduction = 'harmony', verbose = FALSE, theta = 3)
# Integrating SCTransformed data
obj <- SCTransform(object = obj)
obj <- IntegrateLayers(object = obj, method = HarmonyIntegration,
  orig.reduction = "pca", new.reduction = 'harmony',
  assay = "SCT", verbose = FALSE)

## End(Not run)

Hover Locator

Description

Get quick information from a scatterplot by hovering over points

Usage

HoverLocator(plot, information = NULL, axes = TRUE, dark.theme = FALSE, ...)

Arguments

See Also

layout ggplot_build DimPlot FeaturePlot

Examples

## Not run: 
data("pbmc_small")
plot <- DimPlot(object = pbmc_small)
HoverLocator(plot = plot, information = FetchData(object = pbmc_small, vars = 'percent.mito'))

## End(Not run)

Visualize features in dimensional reduction space interactively

Description

Visualize features in dimensional reduction space interactively

Usage

IFeaturePlot(object, feature, dims = c(1, 2), reduction = NULL, slot = "data")

Arguments

Value

Returns the final plot as a ggplot object

Visualize clusters spatially and interactively

Description

Visualize clusters spatially and interactively

Usage

ISpatialDimPlot(
  object,
  image = NULL,
  image.scale = "lowres",
  group.by = NULL,
  alpha = c(0.3, 1)
)

Arguments

Value

Returns final plot as a ggplot object

Visualize features spatially and interactively

Description

Visualize features spatially and interactively

Usage

ISpatialFeaturePlot(
  object,
  feature,
  image = NULL,
  image.scale = "lowres",
  slot = "data",
  alpha = c(0.1, 1)
)

Arguments

Value

Returns final plot as a ggplot object

Spatial Cluster Plots

Description

Visualize clusters or other categorical groupings in a spatial context

Usage

ImageDimPlot(
  object,
  fov = NULL,
  boundaries = NULL,
  group.by = NULL,
  split.by = NULL,
  cols = NULL,
  shuffle.cols = FALSE,
  size = 0.5,
  molecules = NULL,
  mols.size = 0.1,
  mols.cols = NULL,
  mols.alpha = 1,
  nmols = 1000,
  alpha = 1,
  border.color = "white",
  border.size = NULL,
  na.value = "grey50",
  dark.background = TRUE,
  crop = FALSE,
  cells = NULL,
  overlap = FALSE,
  axes = FALSE,
  combine = TRUE,
  coord.fixed = TRUE,
  flip_xy = TRUE
)

Arguments

Value

If combine = TRUE, a patchwork ggplot object; otherwise, a list of ggplot objects

Spatial Feature Plots

Description

Visualize expression in a spatial context

Usage

ImageFeaturePlot(
  object,
  features,
  fov = NULL,
  boundaries = NULL,
  cols = if (isTRUE(x = blend)) {
     c("lightgrey", "#ff0000", "#00ff00")
 } else {
   
     c("lightgrey", "firebrick1")
 },
  size = 0.5,
  min.cutoff = NA,
  max.cutoff = NA,
  split.by = NULL,
  molecules = NULL,
  mols.size = 0.1,
  mols.cols = NULL,
  nmols = 1000,
  alpha = 1,
  border.color = "white",
  border.size = NULL,
  dark.background = TRUE,
  blend = FALSE,
  blend.threshold = 0.5,
  crop = FALSE,
  cells = NULL,
  scale = c("feature", "all", "none"),
  overlap = FALSE,
  axes = FALSE,
  combine = TRUE,
  coord.fixed = TRUE
)

Arguments

Value

If combine = TRUE, a patchwork ggplot object; otherwise, a list of ggplot objects

Integrate data

Description

Perform dataset integration using a pre-computed AnchorSet.

Usage

IntegrateData(
  anchorset,
  new.assay.name = "integrated",
  normalization.method = c("LogNormalize", "SCT"),
  features = NULL,
  features.to.integrate = NULL,
  dims = 1:30,
  k.weight = 100,
  weight.reduction = NULL,
  sd.weight = 1,
  sample.tree = NULL,
  preserve.order = FALSE,
  eps = 0,
  verbose = TRUE
)

Arguments

            [,1]  [,2]
       [1,]   -2   -3
       [2,]    1   -1

Details

The main steps of this procedure are outlined below. For a more detailed description of the methodology, please see Stuart, Butler, et al Cell 2019. doi:10.1016/j.cell.2019.05.031; doi:10.1101/460147

For pairwise integration:

• Construct a weights matrix that defines the association between each query cell and each anchor. These weights are computed as 1 - the distance between the query cell and the anchor divided by the distance of the query cell to the k.weightth anchor multiplied by the anchor score computed in FindIntegrationAnchors. We then apply a Gaussian kernel width a bandwidth defined by sd.weight and normalize across all k.weight anchors.

• Compute the anchor integration matrix as the difference between the two expression matrices for every pair of anchor cells

• Compute the transformation matrix as the product of the integration matrix and the weights matrix.

• Subtract the transformation matrix from the original expression matrix.

For multiple dataset integration, we perform iterative pairwise integration. To determine the order of integration (if not specified via sample.tree), we

• Define a distance between datasets as the total number of cells in the smaller dataset divided by the total number of anchors between the two datasets.

• Compute all pairwise distances between datasets

• Cluster this distance matrix to determine a guide tree

Value

Returns a Seurat object with a new integrated Assay. If normalization.method = "LogNormalize", the integrated data is returned to the data slot and can be treated as log-normalized, corrected data. If normalization.method = "SCT", the integrated data is returned to the scale.data slot and can be treated as centered, corrected Pearson residuals.

References

Stuart T, Butler A, et al. Comprehensive Integration of Single-Cell Data. Cell. 2019;177:1888-1902 doi:10.1016/j.cell.2019.05.031

Examples

## Not run: 
# to install the SeuratData package see https://github.com/satijalab/seurat-data
library(SeuratData)
data("panc8")

# panc8 is a merged Seurat object containing 8 separate pancreas datasets
# split the object by dataset
pancreas.list <- SplitObject(panc8, split.by = "tech")

# perform standard preprocessing on each object
for (i in 1:length(pancreas.list)) {
  pancreas.list[[i]] <- NormalizeData(pancreas.list[[i]], verbose = FALSE)
  pancreas.list[[i]] <- FindVariableFeatures(
    pancreas.list[[i]], selection.method = "vst",
    nfeatures = 2000, verbose = FALSE
  )
}

# find anchors
anchors <- FindIntegrationAnchors(object.list = pancreas.list)

# integrate data
integrated <- IntegrateData(anchorset = anchors)

## End(Not run)

Integrate low dimensional embeddings

Description

Perform dataset integration using a pre-computed Anchorset of specified low dimensional representations.

Usage

IntegrateEmbeddings(anchorset, ...)

## S3 method for class 'IntegrationAnchorSet'
IntegrateEmbeddings(
  anchorset,
  new.reduction.name = "integrated_dr",
  reductions = NULL,
  dims.to.integrate = NULL,
  k.weight = 100,
  weight.reduction = NULL,
  sd.weight = 1,
  sample.tree = NULL,
  preserve.order = FALSE,
  verbose = TRUE,
  ...
)

## S3 method for class 'TransferAnchorSet'
IntegrateEmbeddings(
  anchorset,
  reference,
  query,
  query.assay = NULL,
  new.reduction.name = "integrated_dr",
  reductions = "pcaproject",
  dims.to.integrate = NULL,
  k.weight = 100,
  weight.reduction = NULL,
  reuse.weights.matrix = TRUE,
  sd.weight = 1,
  preserve.order = FALSE,
  verbose = TRUE,
  ...
)

Arguments

            [,1]  [,2]
       [1,]   -2   -3
       [2,]    1   -1

Details

The main steps of this procedure are identical to IntegrateData with one key distinction. When computing the weights matrix, the distance calculations are performed in the full space of integrated embeddings when integrating more than two datasets, as opposed to a reduced PCA space which is the default behavior in IntegrateData.

Value

When called on a TransferAnchorSet (from FindTransferAnchors), this will return the query object with the integrated embeddings stored in a new reduction. When called on an IntegrationAnchorSet (from IntegrateData), this will return a merged object with the integrated reduction stored.

Integrate Layers

Description

Integrate Layers

Usage

IntegrateLayers(
  object,
  method,
  orig.reduction = "pca",
  assay = NULL,
  features = NULL,
  layers = NULL,
  scale.layer = "scale.data",
  ...
)

Arguments

Value

object with integration data added to it

Integration Method Functions

The following integration method functions are available:

See Also

Writing integration method functions

The IntegrationAnchorSet Class

Description

Inherits from the Anchorset class. Implemented mainly for method dispatch purposes. See AnchorSet for slot details.

The IntegrationData Class

Description

The IntegrationData object is an intermediate storage container used internally throughout the integration procedure to hold bits of data that are useful downstream.

Slots

neighbors

List of neighborhood information for cells (outputs of RANN::nn2)

weights

Anchor weight matrix

integration.matrix

Integration matrix

anchors

Anchor matrix

offsets

The offsets used to enable cell look up in downstream functions

objects.ncell

Number of cells in each object in the object.list

sample.tree

Sample tree used for ordering multi-dataset integration

Determine statistical significance of PCA scores.

Description

Randomly permutes a subset of data, and calculates projected PCA scores for these 'random' genes. Then compares the PCA scores for the 'random' genes with the observed PCA scores to determine statistical significance. End result is a p-value for each gene's association with each principal component.

Usage

JackStraw(
  object,
  reduction = "pca",
  assay = NULL,
  dims = 20,
  num.replicate = 100,
  prop.freq = 0.01,
  verbose = TRUE,
  maxit = 1000
)

Arguments

Value

Returns a Seurat object where JS(object = object[['pca']], slot = 'empirical') represents p-values for each gene in the PCA analysis. If ProjectPCA is subsequently run, JS(object = object[['pca']], slot = 'full') then represents p-values for all genes.

References

Inspired by Chung et al, Bioinformatics (2014)

Examples

## Not run: 
data("pbmc_small")
pbmc_small = suppressWarnings(JackStraw(pbmc_small))
head(JS(object = pbmc_small[['pca']], slot = 'empirical'))

## End(Not run)

The JackStrawData Class

Description

For more details, please see the documentation in SeuratObject

See Also

SeuratObject::JackStrawData-class

JackStraw Plot

Description

Plots the results of the JackStraw analysis for PCA significance. For each PC, plots a QQ-plot comparing the distribution of p-values for all genes across each PC, compared with a uniform distribution. Also determines a p-value for the overall significance of each PC (see Details).

Usage

JackStrawPlot(
  object,
  dims = 1:5,
  cols = NULL,
  reduction = "pca",
  xmax = 0.1,
  ymax = 0.3
)

Arguments

Details

Significant PCs should show a p-value distribution (black curve) that is strongly skewed to the left compared to the null distribution (dashed line) The p-value for each PC is based on a proportion test comparing the number of genes with a p-value below a particular threshold (score.thresh), compared with the proportion of genes expected under a uniform distribution of p-values.

Value

A ggplot object

Author(s)

Omri Wurtzel

See Also

ScoreJackStraw

Examples

data("pbmc_small")
JackStrawPlot(object = pbmc_small)

Seurat-Joint PCA Integration

Description

Seurat-Joint PCA Integration

Usage

JointPCAIntegration(
  object = NULL,
  assay = NULL,
  layers = NULL,
  orig = NULL,
  new.reduction = "integrated.dr",
  reference = NULL,
  features = NULL,
  normalization.method = c("LogNormalize", "SCT"),
  dims = 1:30,
  k.anchor = 20,
  scale.layer = "scale.data",
  dims.to.integrate = NULL,
  k.weight = 100,
  weight.reduction = NULL,
  sd.weight = 1,
  sample.tree = NULL,
  preserve.order = FALSE,
  verbose = TRUE,
  ...
)

Arguments

            [,1]  [,2]
       [1,]   -2   -3
       [2,]    1   -1

L2-Normalize CCA

Description

Perform l2 normalization on CCs

Usage

L2CCA(object, ...)

Arguments

L2-normalization

Description

Perform l2 normalization on given dimensional reduction

Usage

L2Dim(object, reduction, new.dr = NULL, new.key = NULL)

Arguments

Value

Returns a Seurat object

Label clusters on a ggplot2-based scatter plot

Description

Label clusters on a ggplot2-based scatter plot

Usage

LabelClusters(
  plot,
  id,
  clusters = NULL,
  labels = NULL,
  split.by = NULL,
  repel = TRUE,
  box = FALSE,
  geom = "GeomPoint",
  position = "median",
  ...
)

Arguments

Value

A ggplot2-based scatter plot with cluster labels

See Also

geom_text_repel geom_text

Examples

data("pbmc_small")
plot <- DimPlot(object = pbmc_small)
LabelClusters(plot = plot, id = 'ident')

Add text labels to a ggplot2 plot

Description

Add text labels to a ggplot2 plot

Usage

LabelPoints(
  plot,
  points,
  labels = NULL,
  repel = FALSE,
  xnudge = 0.3,
  ynudge = 0.05,
  ...
)

Arguments

Value

A ggplot object

See Also

geom_text

Examples

data("pbmc_small")
ff <- TopFeatures(object = pbmc_small[['pca']])
cc <- TopCells(object = pbmc_small[['pca']])
plot <- FeatureScatter(object = pbmc_small, feature1 = ff[1], feature2 = ff[2])
LabelPoints(plot = plot, points = cc)

Leverage Score Calculation

Description

This function computes the leverage scores for a given object It uses the concept of sketching and random projections. The function provides an approximation to the leverage scores using a scalable method suitable for large matrices.

Usage

LeverageScore(object, ...)

## Default S3 method:
LeverageScore(
  object,
  nsketch = 5000L,
  ndims = NULL,
  method = CountSketch,
  eps = 0.5,
  seed = 123L,
  verbose = TRUE,
  ...
)

## S3 method for class 'StdAssay'
LeverageScore(
  object,
  nsketch = 5000L,
  ndims = NULL,
  method = CountSketch,
  vf.method = NULL,
  layer = "data",
  eps = 0.5,
  seed = 123L,
  verbose = TRUE,
  features = NULL,
  ...
)

## S3 method for class 'Assay'
LeverageScore(
  object,
  nsketch = 5000L,
  ndims = NULL,
  method = CountSketch,
  vf.method = NULL,
  layer = "data",
  eps = 0.5,
  seed = 123L,
  verbose = TRUE,
  features = NULL,
  ...
)

## S3 method for class 'Seurat'
LeverageScore(
  object,
  assay = NULL,
  nsketch = 5000L,
  ndims = NULL,
  var.name = "leverage.score",
  over.write = FALSE,
  method = CountSketch,
  vf.method = NULL,
  layer = "data",
  eps = 0.5,
  seed = 123L,
  verbose = TRUE,
  features = NULL,
  ...
)

Arguments

References

Clarkson, K. L. & Woodruff, D. P. Low-rank approximation and regression in input sparsity time. JACM 63, 1–45 (2017). doi:10.1145/3019134;

Visualize spatial and clustering (dimensional reduction) data in a linked, interactive framework

Description

Visualize spatial and clustering (dimensional reduction) data in a linked, interactive framework

Usage

LinkedDimPlot(
  object,
  dims = 1:2,
  reduction = NULL,
  image = NULL,
  image.scale = "lowres",
  group.by = NULL,
  alpha = c(0.1, 1),
  combine = TRUE
)

LinkedFeaturePlot(
  object,
  feature,
  dims = 1:2,
  reduction = NULL,
  image = NULL,
  image.scale = "lowres",
  slot = "data",
  alpha = c(0.1, 1),
  combine = TRUE
)

Arguments

Value

Returns final plots. If combine, plots are stiched together using CombinePlots; otherwise, returns a list of ggplot objects

Examples

## Not run: 
LinkedDimPlot(seurat.object)
LinkedFeaturePlot(seurat.object, feature = 'Hpca')

## End(Not run)

Load a 10x Genomics Visium Spatial Experiment into a Seurat object

Description

Load a 10x Genomics Visium Spatial Experiment into a Seurat object

Usage

Load10X_Spatial(
  data.dir,
  filename = "filtered_feature_bc_matrix.h5",
  assay = "Spatial",
  slice = "slice1",
  bin.size = NULL,
  filter.matrix = TRUE,
  to.upper = FALSE,
  image = NULL,
  ...
)

Arguments

Value

A Seurat object

Examples

## Not run: 
data_dir <- 'path/to/data/directory'
list.files(data_dir) # Should show filtered_feature_bc_matrix.h5
Load10X_Spatial(data.dir = data_dir)

## End(Not run)

Load the Annoy index file

Description

Load the Annoy index file

Usage

LoadAnnoyIndex(object, file)

Arguments

Value

Returns the Neighbor object with the index stored

Load Curio Seeker data

Description

Load Curio Seeker data

Usage

LoadCurioSeeker(data.dir, assay = "Spatial")

Arguments

Value

A Seurat object

Load STARmap data

Description

Load STARmap data

Usage

LoadSTARmap(
  data.dir,
  counts.file = "cell_barcode_count.csv",
  gene.file = "genes.csv",
  qhull.file = "qhulls.tsv",
  centroid.file = "centroids.tsv",
  assay = "Spatial",
  image = "image"
)

Arguments

Value

A Seurat object

See Also

STARmap

Read and Load 10x Genomics Xenium in-situ data

Description

Read and Load 10x Genomics Xenium in-situ data

Usage

LoadXenium(
  data.dir,
  fov = "fov",
  assay = "Xenium",
  mols.qv.threshold = 20,
  cell.centroids = TRUE,
  molecule.coordinates = TRUE,
  segmentations = NULL,
  flip.xy = FALSE
)

ReadXenium(
  data.dir,
  outs = c("segmentation_method", "matrix", "microns"),
  type = "centroids",
  mols.qv.threshold = 20,
  flip.xy = F
)

Arguments

Value

LoadXenium: A Seurat object

ReadXenium: A list with some combination of the following values:

• “matrix”: a sparse matrix with expression data; cells are columns and features are rows

• “centroids”: a data frame with cell centroid coordinates in three columns: “x”, “y”, and “cell”

• “pixels”: a data frame with molecule pixel coordinates in three columns: “x”, “y”, and “gene”

Calculate the local structure preservation metric

Description

Calculates a metric that describes how well the local structure of each group prior to integration is preserved after integration. This procedure works as follows: For each group, compute a PCA, compute the top num.neighbors in pca space, compute the top num.neighbors in corrected pca space, compute the size of the intersection of those two sets of neighbors. Return the average over all groups.

Usage

LocalStruct(
  object,
  grouping.var,
  idents = NULL,
  neighbors = 100,
  reduction = "pca",
  reduced.dims = 1:10,
  orig.dims = 1:10,
  verbose = TRUE
)

Arguments