PCA: Run Principal Component Analysis on gene expression

In nukappa/seurat_v2: Seurat : R toolkit for single cell genomics

Description

Run prcomp for PCA dimensionality reduction

Usage

PCA(object, pc.genes = NULL, do.print = TRUE, pcs.print = 5,
  pcs.store = 40, genes.print = 30, use.imputed = FALSE,
  rev.pca = FALSE, ...)

Arguments

object	Seurat object
pc.genes	Genes to use as input for PCA. Default is [e-mail]
do.print	Print the top genes associated with high/low loadings for the PCs
pcs.print	Number of PCs to print genes for
pcs.store	Number of PCs to store
genes.print	Number of genes to print for each PC
use.imputed	Run PCA on imputed values (FALSE by default)
rev.pca	By default computes the PCA on the cell x gene matrix. Setting to true will compute it on gene x cell matrix.
...	Additional arguments to be passed to prcomp

Value

Returns Seurat object with an PCA embedding ([e-mail]) and gene projection matrix (object@pca.x). The PCA object itself is stored in [e-mail][[1]]

nukappa/seurat_v2 documentation built on May 24, 2019, 9:57 a.m.