Seurat (version 2.3.3)

RunMultiCCA: Perform Canonical Correlation Analysis with more than two groups

Description

Runs a canonical correlation analysis

Usage

RunMultiCCA(object.list, genes.use, add.cell.ids = NULL, niter = 25,
  num.ccs = 1, standardize = TRUE)

Arguments

object.list

List of Seurat objects

genes.use

Genes to use in mCCA.

add.cell.ids

Vector of strings to pass to RenameCells to give unique cell names

niter

Number of iterations to perform. Set by default to 25.

num.ccs

Number of canonical vectors to calculate

standardize

standardize scale.data matrices to be centered (mean zero) and scaled to have a standard deviation of 1.

Value

Returns a combined Seurat object with the CCA stored in the @dr$cca slot.

Examples

# NOT RUN {
pbmc_small
# As multi-set CCA requires more than two datasets, we will split our test object into
# three just for this example
pbmc1 <- SubsetData(pbmc_small,cells.use = [e-mail][1:30])
pbmc2 <- SubsetData(pbmc_small,cells.use = [e-mail][31:60])
pbmc3 <- SubsetData(pbmc_small,cells.use = [e-mail][61:80])
[e-mail]$group <- "group1"
[e-mail]$group <- "group2"
[e-mail]$group <- "group3"
pbmc.list <- list(pbmc1, pbmc2, pbmc3)
pbmc_cca <- RunMultiCCA(object.list = pbmc.list, genes.use = [e-mail], num.ccs = 3)
# Print results
PrintDim(pbmc_cca,reduction.type = 'cca')

# }