Normalize sample abundance estimates by median gRNA counts
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.
An optional vector of voom-appropriate library size adjustment factors, usually calculated with
A renormalized ExpressionSet object of the same type as the provided object.
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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)
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