Seurat (version 2.3.4)

RunICA: Run Independent Component Analysis on gene expression

Description

Run fastica algorithm from the ica package for ICA dimensionality reduction. For details about stored ICA calculation parameters, see PrintICAParams.

Usage

RunICA(object, ic.genes = NULL, ics.compute = 50, use.imputed = FALSE,
  rev.ica = FALSE, print.results = TRUE, ics.print = 1:5,
  genes.print = 50, ica.function = "icafast", seed.use = 1,
  reduction.name = "ica", reduction.key = "IC", ...)

Arguments

object

Seurat object

ic.genes

Genes to use as input for ICA. Default is [e-mail]

ics.compute

Number of ICs to compute

use.imputed

Run ICA on imputed values (FALSE by default)

rev.ica

By default, computes the dimensional reduction on the cell x gene matrix. Setting to true will compute it on the transpose (gene x cell matrix).

print.results

Print the top genes associated with each dimension

ics.print

ICs to print genes for

genes.print

Number of genes to print for each IC

ica.function

ICA function from ica package to run (options: icafast, icaimax, icajade)

seed.use

Random seed to use for fastica

reduction.name

dimensional reduction name, specifies the position in the object$dr list. ica by default

reduction.key

dimensional reduction key, specifies the string before the number for the dimension names. IC by default

…

Additional arguments to be passed to fastica

Value

Returns Seurat object with an ICA calculation stored in object@dr$ica

Examples

# NOT RUN {
pbmc_small
# Run ICA on variable genes (default)
pbmc_small <- RunICA(pbmc_small, ics.compute=5)
# Run ICA on different gene set (in this case all genes)
pbmc_small <- RunICA(pbmc_small, ic.genes = rownames(pbmc_small@data))
# Plot results
ICAPlot(pbmc_small)

# }