runICA: Run Independent Component Analysis on gene expression

In NMikolajewicz/scMiko: scRNAseq analysis functions. Developed using Seurat framework.

Run Independent Component Analysis on gene expression

Description

Run fastica algorithm from the ica package for ICA dimensionality reduction. Wrapper for Seurat's RunICA function, with additional significant gene identification step.

Usage

runICA(
  object,
  assay = DefaultAssay(object),
  features = NULL,
  max_cells = 20000,
  verbose = T,
  ...
)

Arguments

object	Seurat object
assay	name of assay to use for ICA analysis. Expression data from 'scale.data' slot will be used.
features	Features to compute ICA on. If not specified, all features present in 'object' are used (not recommended, ICA is computationally expensive).
max_cells	Max number of cells to run ICA on. If number of cells in 'object' exceeded 'max_cells', object is subsampled prior to running ICA. Default is 20000.
verbose	Print progress. Default is T.
...	Additional parameters passed to Seurat::RunICA(...)

Value

seurat object with significant genes stored in "misc" slot of ICA reduction slot.

Author(s)

Nicholas Mikolajewicz

References

https://nmikolajewicz.github.io/scMiko/articles/Module_Detection.html

See Also

RunICA for Seurat's independent component analysis, getICAGenes for significant ICA gene identification.

NMikolajewicz/scMiko documentation built on June 28, 2023, 1:41 p.m.