ct.normalizeSpline: Normalize sample abundance estimates by a spline fit to...

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

Normalize sample abundance estimates by a spline fit to specific shared elements

Description

This function normalizes Crispr gRNA abundance estimates by fiting a smoothed spline to a subset of the 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 the "anchor" guides and their abundance estimates. It then adjusts the spline trends from each sample to the mean of all of the sample spline fits in a manner analogous to quantile normalization, interpolating the gRNA abundance values between the anchor points; these values are returned as normalized counts in the 'exprs' slot of the input eset.

Usage

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(s) in annotation that corresponds to the "anchor" gRNAs. If absent, the method 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

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)

OscarBrock/gCrisprTools documentation built on Oct. 25, 2022, 7:29 a.m.