RunCCA: Perform Canonical Correlation Analysis

In satijalab/seurat: Tools for Single Cell Genomics

Perform Canonical Correlation Analysis

Description

Runs a canonical correlation analysis using a diagonal implementation of CCA. For details about stored CCA calculation parameters, see PrintCCAParams.

Usage

RunCCA(object1, object2, ...)

## Default S3 method:
RunCCA(
  object1,
  object2,
  standardize = TRUE,
  num.cc = 20,
  seed.use = 42,
  verbose = FALSE,
  ...
)

## S3 method for class 'Seurat'
RunCCA(
  object1,
  object2,
  assay1 = NULL,
  assay2 = NULL,
  num.cc = 20,
  features = NULL,
  renormalize = FALSE,
  rescale = FALSE,
  compute.gene.loadings = TRUE,
  add.cell.id1 = NULL,
  add.cell.id2 = NULL,
  verbose = TRUE,
  ...
)

Arguments

object1	First Seurat object
object2	Second Seurat object.
...	Extra parameters (passed onto MergeSeurat in case with two objects passed, passed onto ScaleData in case with single object and rescale.groups set to TRUE)
standardize	Standardize matrices - scales columns to have unit variance and mean 0
num.cc	Number of canonical vectors to calculate
seed.use	Random seed to set. If NULL, does not set a seed
verbose	Show progress messages
assay1, assay2	Assays to pull from in the first and second objects, respectively
features	Set of genes to use in CCA. Default is the union of both the variable features sets present in both objects.
renormalize	Renormalize raw data after merging the objects. If FALSE, merge the data matrices also.
rescale	Rescale the datasets prior to CCA. If FALSE, uses existing data in the scale data slots.
compute.gene.loadings	Also compute the gene loadings. NOTE - this will scale every gene in the dataset which may impose a high memory cost.
add.cell.id1, add.cell.id2	Add ...

Value

Returns a combined Seurat object with the CCA results stored.

See Also

merge.Seurat

Examples

## Not run: 
data("pbmc_small")
pbmc_small
# As CCA requires two datasets, we will split our test object into two just for this example
pbmc1 <- subset(pbmc_small, cells = colnames(pbmc_small)[1:40])
pbmc2 <- subset(pbmc_small, cells = colnames(x = pbmc_small)[41:80])
pbmc1[["group"]] <- "group1"
pbmc2[["group"]] <- "group2"
pbmc_cca <- RunCCA(object1 = pbmc1, object2 = pbmc2)
# Print results
print(x = pbmc_cca[["cca"]])

## End(Not run)

satijalab/seurat documentation built on May 11, 2024, 4:04 a.m.