Normalize sample abundance estimates by a spline fit to the nontargeting controls

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Description

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 the 'exprs' slot of the input eset.

Usage

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ct.normalizeSpline(eset, annotation, geneSymb = NULL, lib.size = NULL)

Arguments

eset

An ExpressionSet object containing, at minimum, count data accessible by exprs.

annotation

An annotation dataframe indicating the nontargeting controls in the geneID column.

geneSymb

The geneSymbol identifier in annotation that corresponds to nontargeting gRNAs. If absent, ct.gRNARankByReplicate will attempt to infer nontargeting guides by searching for "no_gid" or NA in the appropriate columns.

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 normalized eset.

Author(s)

Russell Bainer

Examples

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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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