normaliseGeneExpression {psichomics} | R Documentation

## Filter and normalise gene expression

### Description

Filter and normalise gene expression

### Usage

    normaliseGeneExpression(
      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
`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 (`voom`)?

### Details

`edgeR::calcNormFactors` will be used to normalise gene expression if one of the following methods is set: `TMM`, `RLE`, `upperquartile` or `none`. However, `voom` will be used for normalisation if `performVoom = TRUE` and the selected method is `quantile`.

### Value

Filtered and normalised gene expression

### See Also

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

### Examples

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

[Package psichomics version 1.14.1 Index]