ct.GCbias: Visualization of gRNA GC Content Trends

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

Visualization of gRNA GC Content Trends

Description

This function visualizes relationships between gRNA GC content and their measured abundance or various differential expression model estimates.

Usage

ct.GCbias(data.obj, ann, sampleKey = NULL, lib.size = NULL)

Arguments

data.obj	An ExpressionSet or fit (MArrayLM) object to be analyzed for the presence of GC content bias.
ann	An annotation data.frame, used to estimate GC content for each guide. Guides are annotated by row, and the object must minimally contain a target column containing a character vector that indicates the corresponding nucleotide sequences.
sampleKey	An optional sample key, supplied as a 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. Ignored in the analysis of model fit objects.
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

An image relating GC content to experimental observations on the default device. If the provided data.obj is an ExpressionSet, this takes the form of a scatter plot where the GC with a smoothed trendline within each sample. If data.obj is a fit object describing a linear model contrast, then four panels are returned describing the GC content distribution and its relationship to guide-level fold change, standard deviation, and P-value estimates.

Author(s)

Russell Bainer

Examples

data('es')
data('ann')
data('fit')

ct.GCbias(es, ann)
ct.GCbias(fit, ann)

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