Description Usage Arguments Value Examples
View source: R/ComparisonFunctions.R
QC-metrics of newly analysed ChIP-seq samples can be compared with the reference values of the compendium and enrichment profiles can be plotted against pre-computed profiles of published datasets. The metagene profiles show the problematic samples signal (red line) for the ChIP, for the input and their relative enrichment when compared to the compendium<e2><80><99>s mean signal (black line) and its 2 x standard error (blue shadow). Additionally the function plots the desired QC-metric as a red dashed line for the sample plotted against the reference distribution (density plots) of the compendium values stratified by chromatin marks.
metagenePlotsForComparison
1 | metagenePlotsForComparison(data, target, tag, savePlotPath = NULL)
|
data |
metagene-object of metagene profile by createMetageneProfile() containing input and chip profile |
target |
String, chromatin mark or transcription factor to be analysed. Use listAvailableElements() function to check availability. |
tag |
indicating the kind of profile to plot. Can be either: geneBody, TES or TSS. |
savePlotPath |
if set the plot will be saved under 'savePlotPath'. Default=NULL and plot will be forwarded to stdout. |
Creates a pdf figure under 'savePlotPath'
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | ## 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=98
## Not run:
filepath=tempdir()
setwd(filepath)
data("chipSubset", package = "ChIC.data", envir = environment())
chipBam=chipSubset
data("inputSubset", package = "ChIC.data", envir = environment())
inputBam=inputSubset
## 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, mc = mc)
smoothedInput <- tagDensity(inputBamSelected,
tag.shift = finalTagShift, mc = mc)
##calculate metagene profiles
Meta_Result <- createMetageneProfile(
smoothed.densityChip = smoothedChip,
smoothed.densityInput = smoothedInput,
tag.shift = finalTagShift, mc = mc)
##compare metagene features of the geneBody with the compendium
metagenePlotsForComparison(data = Meta_Result$geneBody,
target = "H3K4me3", tag = "geneBody")
## End(Not run)
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