subset_genes: Subset Genes

In kgellatl/SignallingSingleCell: Network Analysis for single cell RNA sequencing Data (sc-RNASeq)

Description

This will select genes based on minimum expression, coefficient of variation, or by a preliminary PCA.

Usage

subset_genes(
  input,
  method,
  threshold = 1,
  minCells = 10,
  nComp = 10,
  cutoff = 0.85,
  log = F,
  output = "simple",
  fudge = F
)

Arguments

input	the input ex_sc.
method	can either be "Expression", CV", or "PCA".
threshold	UMI threshold for gene detection
minCells	number of cells expressed above threshold for a given gene
nComp	if method = PCA, the number of components to keep
cutoff	the percentile of genes to keep

Details

Genes will be first filtered by minimum expression selecting by subsetting to genes that are expressed above the threshold in more than minCells. If the method is CV, it will first subset the genes based on the expression cutoffs, then find the coefficient of variation across all genes. Next it will select the percentile of genes (cutoff) based on their coefficient of variation. The last method will perform PCA on the cells, and then look at the loadings of each gene. By finding genes that are off center (via malhanoobis distance) we can filter to include only genes that contribute significant variance to the data.

Examples

gene_subset <- subset_genes(input = exprs(ex_sc_example), method = "PCA", threshold = 3, minCells = 30, nComp = 15, cutoff = 0.75)

kgellatl/SignallingSingleCell documentation built on Dec. 29, 2021, 4:12 p.m.