Normalize sample abundance estimates by a spline fit to the nontargeting controls
This function normalizes Crispr gRNA abundance estimates by fiting a smoothed spline to the nontargeting gRNAs within each sample
and then equalizing these curves across the experiment. Specifically, the algorithm ranks the gRNA abundance estimates within each sample and
uses a smoothed spline to determine a relationship between the ranks of nontargeting guides and their abundance estimates. It then removes the
spline trend from each sample, centering each experiment around the global median abundance; these values are returned as normalized counts in
exprs' slot of the input eset.
An ExpressionSet object containing, at minimum, count data accessible by
An annotation dataframe indicating the nontargeting controls in the geneID column.
An optional vector of voom-appropriate library size adjustment factors, usually calculated with
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data('es') data('ann') #Build the sample key and library sizes for visualization library(Biobase) sk <- (relevel(as.factor(pData(es)$TREATMENT_NAME), "ControlReference")) names(sk) <- row.names(pData(es)) ls <- colSums(exprs(es)) es.norm <- ct.normalizeSpline(es, ann, 'NoTarget', lib.size = ls) ct.gRNARankByReplicate(es, sk, lib.size = ls) ct.gRNARankByReplicate(es.norm, sk, lib.size = ls)
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