normaliseGeneExpression {psichomics}R Documentation

Filter and normalise gene expression

Description

Gene expression is filtered and normalised in the following steps:

Usage

normaliseGeneExpression(
  geneExpr,
  geneFilter = NULL,
  method = "TMM",
  p = 0.75,
  log2transform = TRUE,
  priorCount = 0.25,
  performVoom = FALSE
)

normalizeGeneExpression(
  geneExpr,
  geneFilter = NULL,
  method = "TMM",
  p = 0.75,
  log2transform = TRUE,
  priorCount = 0.25,
  performVoom = FALSE
)

Arguments

geneExpr

Matrix or data frame: gene expression

geneFilter

Boolean: filtered genes (if NULL, skip filtering)

method

Character: normalisation method, including TMM, RLE, upperquartile, none or quantile (see Details)

p

percentile (between 0 and 1) of the counts that is aligned when method="upperquartile"

log2transform

Boolean: perform log2-transformation?

priorCount

Average count to add to each observation to avoid zeroes after log-transformation

performVoom

Boolean: perform mean-variance modelling (using voom)?

Details

edgeR::calcNormFactors will be used to normalise gene expression if method is TMM, RLE, upperquartile or none. If performVoom = TRUE, voom will only normalise if method = "quantile".

Available normalisation methods:

Value

Filtered and normalised gene expression

See Also

Other functions for gene expression pre-processing: convertGeneIdentifiers(), filterGeneExpr(), plotGeneExprPerSample(), plotLibrarySize(), plotRowStats()

Examples

geneExpr <- readFile("ex_gene_expression.RDS")
normaliseGeneExpression(geneExpr)

[Package psichomics version 1.16.0 Index]