ansTransform: Perform quantification and normalization of count data

In CSSQ: Chip-seq Signal Quantifier Pipeline

Description

This function quantifies each each region for a sample and performs background correction and normalization as instructed. Returns a vector of count information for the input regions.

Usage

ansTransform(countData, noNeg = TRUE, plotDataToPDF = FALSE)

Arguments

countData	A RangedSummarizedExperiment-class object from getRegionCounts with count data.
noNeg	A Logical parameter indicating how to deal with negative values. When TRUE (default), all negative values will be moved to 0 before transforming. When FALSE, the signs will be maintained while the transformation will be applied to the absolute value. (default: TRUE)
plotDataToPDF	A logical parameter indicating whether to make plots of the data distribution to a separate PDF file for each sample. When TRUE, a histogram will be plotted for the data before and after transformation. When FALSE, no plots will be made. (default: FALSE)

Value

A RangedSummarizedExperiment-class object containing the anscombe transformed count data as the assay.

Examples

exRange <- GRanges(seqnames=c("chr1","chr2","chr3","chr4"),
ranges=IRanges(start=c(1000,2000,3000,4000),end=c(1500,2500,3500,4500)))
sampleInfo <- read.table(system.file("extdata", "sample_info.txt", 
package="CSSQ",mustWork = TRUE),sep="\t",header=TRUE)
exCount <- matrix(c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16),nrow=4,ncol=4)
exData <- SummarizedExperiment(assays = list(countData=exCount),
rowRanges=exRange,colData=sampleInfo)
ansExData <- ansTransform(exData)
assays(ansExData)$ansCount

CSSQ documentation built on Nov. 8, 2020, 6:47 p.m.