ct.normalizeMedians: Normalize sample abundance estimates by median gRNA counts

In RussBainer/gCrisprTools: Suite of Functions for Pooled Crispr Screen QC and Analysis

Normalize sample abundance estimates by median 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.normalizeMedians(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)

RussBainer/gCrisprTools documentation built on Nov. 5, 2022, 2:35 p.m.