qualityScores_LMgenebody: Wrapper function to plot the scaled metagene- profile and to...

In carmencita/CHIC: Quality Control Pipeline for ChIP-Seq Data

Description

The scaled metagene profile that includes the gene body, the signal is captured on a real scale from the TSS and an upstream region of 2KB. From the TSS, the gene body is constructed with 0.5KB in real scale at the gene start (TSS + 0.5KB) and the gene end (TES - 0.5KB), whereas the remaining gene body is scaled to a virtual length of 2000. Considering the length of these regions, the minimum gene length required is 3KB and shorter genes are filtered out. From the profile, we take enrichment values at different coordinates: at -2KB, at the TSS, inner margin (0.5KB), gene body (2KB + 2 * inner margin), gene body+1KB. We collect in total 42 QC-metrics from the ChIP and normalized profile.

qualityScores_LMgenebody

Usage

qualityScores_LMgenebody(
  data,
  savePlotPath = NULL,
  tag = "geneBody",
  plot = "all"
)

Arguments

data	metagene-list for input and chip sample of the genebody profile returned by createMetageneProfile()
savePlotPath	if set the plot will be saved under 'savePlotPath'. Default=NULL and plot will be forwarded to stdout.
tag,	character, it should always be "geneBody" in the current impplementation. This is just for simmetry with qualityScores_LM function
plot	character, possible values "norm", "split", "all" (default) or "none" to plot metaprofiles for normalized ChIP/input enrichment, or the two samples reads densities separated, or both plots, or none (respectively)

Value

returnList

Examples

## This command is time intensive to run
##
## To run the example code the user must provide two bam files for the ChIP
## and the input and read them with the readBamFile() function. To make it
## easier for the user to run the example code we provide tow bam examples 
## (chip and input) in our ChIC.data package that have already been loaded 
## with the readBamFile() function.

mc=4
finalTagShift=82
## Not run: 

filepath=tempdir()
setwd(filepath)

data("chipBam", package = "ChIC.data", envir = environment())
data("inputBam", package = "ChIC.data", envir = environment())

## calculate binding characteristics 
chip_binding.characteristics<-spp::get.binding.characteristics(
    chipBam, srange=c(0,500), bin=5,accept.all.tags=TRUE)

input_binding.characteristics<-spp::get.binding.characteristics(
    inputBam, srange=c(0,500), bin=5,accept.all.tags=TRUE)

##get chromosome information and order chip and input by it
chrl_final=intersect(names(chipBam$tags), names(inputBam$tags))
chipBam$tags=chipBam$tags[chrl_final]
chipBam$quality=chipBam$quality[chrl_final]
inputBam$tags=inputBam$tags[chrl_final]
inputBam$quality=inputBam$quality[chrl_final]

##remove sigular positions with extremely high read counts with 
##respect to the neighbourhood
selectedTags=removeLocalTagAnomalies(chipBam, inputBam, 
chip_binding.characteristics, input_binding.characteristics)

inputBamSelected=selectedTags$input.dataSelected
chipBamSelected=selectedTags$chip.dataSelected

##smooth input and chip tags
smoothedChip=tagDensity(chipBamSelected, tag.shift=finalTagShift)
smoothedInput=tagDensity(inputBamSelected, tag.shift=finalTagShift)

##calculate metagene profiles
Meta_Result=createMetageneProfile(smoothed.densityChip=smoothedChip, 
    smoothedInput,tag.shift=finalTagShift, mc=mc)

#create scaled metagene profile
geneBody_Scores=qualityScores_LMgenebody(Meta_Result$geneBody,
savePlotPath=filepath)

## End(Not run)

carmencita/CHIC documentation built on May 2, 2021, 5:09 p.m.