ct.rawCountDensities: Visualization of Raw gRNA Count Densities

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

Visualization of Raw gRNA Count Densities

Description

This function plots the per-sample densities of raw gRNA read counts on the log10 scale. The curve colors are assigned based on a user- specified sampleKey. This function is primarily useful to determine whether libraries are undersequenced (low mean raw gRNA counts), contaminated (many low-abundance gRNAs present), or if PCR artifacts may be present (subset of extremely abundant guides, multiple gRNA distribution modes). In most well-executed experiments the majority of gRNAs will form a tight distribution around some reasonably high average read count (hundreds of reads), at least among the control samples. Excessively low raw count values can compromise normalization steps and subsequent estimation of gRNA levels, especially in screens in which most gRNAs have minimal effects on cell viability.

Usage

ct.rawCountDensities(eset, sampleKey = NULL, lib.size = NULL)

Arguments

eset	An ExpressionSet object containing, at minimum, count data accessible by exprs() and some phenoData.
sampleKey	A sample key, supplied as a (possibly ordered) factor linking the samples to experimental variables. The names attribute should exactly match those present in eset, and the control set is assumed to be the first level.
lib.size	Optional named vector of library sizes (total reads within the library) to enable normalization

Value

A density plot as specified on the default device.

Author(s)

Russell Bainer

Examples

data('es')

#Build the sample key
library(Biobase)
sk <- relevel(as.factor(pData(es)$TREATMENT_NAME), 'ControlReference')
names(sk) <- row.names(pData(es))

ct.rawCountDensities(es, sk)

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