RunPCA: Run Principal Component Analysis on gene expression using...

In mayer-lab/SeuratForMayer2018: Seurat : R Toolkit for Single Cell Genomics

Description

Run a PCA dimensionality reduction. For details about stored PCA calculation parameters, see PrintPCAParams.

Usage

RunPCA(object, pc.genes = NULL, pcs.compute = 20, use.imputed = FALSE,
  rev.pca = FALSE, weight.by.var = TRUE, do.print = TRUE,
  pcs.print = 1:5, genes.print = 30, reduction.name = "pca",
  reduction.key = "PC", assay.type = "RNA", ...)

Arguments

object	Seurat object
pc.genes	Genes to use as input for PCA. Default is [e-mail]
pcs.compute	Total Number of PCs to compute and store
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.
weight.by.var	Weight the cell embeddings by the variance of each PC (weights the gene loadings if rev.pca is TRUE)
do.print	Print the top genes associated with high/low loadings for the PCs
pcs.print	PCs to print genes for
genes.print	Number of genes to print for each PC
reduction.name	dimensional reduction name, specifies the position in the object$dr list. pca by default
reduction.key	dimensional reduction key, specifies the string before the number for the dimension names. PC by default
assay.type	Data type, RNA by default. Can be changed for multimodal
...	Additional arguments to be passed to IRLBA

Value

Returns Seurat object with the PCA calculation stored in object@dr$pca.

mayer-lab/SeuratForMayer2018 documentation built on May 25, 2019, 9:34 p.m.