RunPCA: PCA transformation of the joint probability matrix

In elolab/ILoReg: ILoReg: a tool for high-resolution cell population identification from scRNA-Seq data

PCA transformation of the joint probability matrix

Description

Perform the PCA transformation of the joint probability matrix, which reduces the dimensionality from k*L to p

Usage

RunPCA.SingleCellExperiment(object, p, scale, threshold)

## S4 method for signature 'SingleCellExperiment'
RunPCA(object, p = 50, scale = FALSE, threshold = 0)

Arguments

object	object of SingleCellExperiment class
p	a positive integer denoting the number of principal components to calculate and select. Default is 50.
scale	a logical specifying whether the probabilities should be standardized to unit-variance before running PCA. Default is FALSE.
threshold	a thresfold for filtering out ICP runs before PCA with the lower terminal projection accuracy below the threshold. Default is 0.

Value

object of SingleCellExperiment class

Examples

library(SingleCellExperiment)
sce <- SingleCellExperiment(assays = list(logcounts = pbmc3k_500))
sce <- PrepareILoReg(sce)
## These settings are just to accelerate the example, use the defaults.
sce <- RunParallelICP(sce,L=2,threads=1,C=0.1,k=5,r=1)
sce <- RunPCA(sce,p=5)

elolab/ILoReg documentation built on March 28, 2022, 1:17 a.m.