ct.normalizeFactoredQuantiles {gCrisprTools} | R Documentation

## Apply Factored Quantile Normalization to gRNA counts

### Description

This function normalizes Crispr gRNA abundance estimates by equalizing the median gRNA abundance values after correcting for library size. It does this by converting raw count values to log2 counts per million and optionally adjusting further in the usual way by dividing these values by user-specified library size factors. THis method should be more stable than the endogenous scaling functions used in `voom` in th especific case of Crispr screens or other cases where the median number of observed counts may be low.

### Usage

    ct.normalizeFactoredQuantiles(eset, lib.size = NULL)

### Arguments

`eset` | An `ExpressionSet` containing, at minimum, count data accessible by `exprs`.
`lib.size` | An optional vector of voom-appropriate library size adjustment factors, usually calculated with `calcNormFactors` and transformed to reflect the appropriate library size. These adjustment factors are interpreted as the total library sizes for each sample, and if absent will be extrapolated from the columnwise count sums of the `exprs` slot of the `eset`.

### Value

A renormalized ExpressionSet object of the same type as the provided object.

### Author(s)

Russell Bainer

### Examples

    data('es')

    #Build the sample key and library sizes for visualization
    library(Biobase)
    sk <- ordered(relevel(as.factor(pData(es)$TREATMENT_NAME), "ControlReference"))
    names(sk) <- row.names(pData(es))
    ls <- colSums(exprs(es))

    es.norm <- ct.normalizeMedians(es, lib.size= ls)
    ct.gRNARankByReplicate(es, sampleKey = sk, lib.size= ls)
    ct.gRNARankByReplicate(es.norm, sampleKey = sk, lib.size= ls)

[Package gCrisprTools version 1.18.0 Index]