PCA: PCA

In scGPS: A complete analysis of single cell subpopulations, from identifying subpopulations to analysing their relationship (scGPS = single cell Global Predictions of Subpopulation)

Description

Select top variable genes and perform prcomp

Usage

PCA(expression.matrix = NULL, ngenes = 1500, scaling = TRUE,
  npcs = 50)

Arguments

expression.matrix	An expression matrix, with genes in rows
ngenes	number of genes used for clustering calculations.
scaling	a logical of whether we want to scale the matrix
npcs	an integer specifying the number of principal components to use.

Value

a list containing PCA results and variance explained

Examples

day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
t <-PCA(expression.matrix=assay(mixedpop1))

scGPS documentation built on Nov. 8, 2020, 5:22 p.m.