runPCA: runPCA

In IMB-Computational-Genomics-Lab/ascend: ascend - Analysis of Single Cell Expression, Normalisation, and Differential expression

Description

Reduce the dimensions of an expression matrix stored in an EMSet based on the most variable genes. Datasets must be reduced prior to clustering analysis as it is used to construct the distance matrix.

Usage

runPCA(object, ..., ngenes, scaling)

## S4 method for signature 'EMSet'
runPCA(object, ..., ngenes = 1500, scaling = TRUE)

Arguments

object	An EMSet that has undergone filtering and normalisation.
...	Additional arguments to pass to irlba
ngenes	The top number of genes you would like to perform the reduction by. Default: 1500.
scaling	Boolean - set to FALSE if you do not want to scale your values. Default: TRUE.

Value

An EMSet with a PCA-reduced matrix stored under PCA in the reducedDims slot

Examples

# Load example EMSet
em_set <- ascend::analyzed_set

# Run PCA
em_set <- runPCA(em_set, scaling = TRUE, ngenes = 1500)

IMB-Computational-Genomics-Lab/ascend documentation built on Aug. 29, 2019, 4:10 a.m.