Introduction

Here, we will explore the alternative quality control workflow, using Bioconductor ChIPQC package. ChIPQC computes quality metrics for aligned data from ChIP-seq experiments. It also provides simple ways to generate a ChIP-seq experiment quality report which can be examined to asses the absolute and relative quality of individual ChIP-seq samples (and their associated controls, as well as overall quality of the experimental data.)

Learning outcomes

Using ChIPQC package

• to generate a summary QC report for experimental sample groups
• to be able to understand and assess QC metrics and plots

Setting-up

In principle one can run ChIPQC both on Uppmax or locally. However, today we will test the package locally.

Follow set-up instructions from Downstream analysis tutorial, differential binding part. We will need the same files and we can work in the same directory.

Install ChIPQC library and any required dependencies

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("ChIPQC", version = "3.8")

Running ChIPQC

While running commands feel free to have a look at ChIPQC package documentation to learn more about different steps and/or build upon them. Here we will just show you the very basics.

library(DiffBind)
library(ChIPQC)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)


#	reading in the sample information (metadata)
samples = read.csv("samples_REST.txt", sep="\t")

#	inspecting the metadata
samples

#	creating an object containing data
res=dba(sampleSheet=samples, config=data.frame(RunParallel=FALSE))

# inspecting the object
res

#	performing quality control
resqc = ChIPQC(res,annotation="hg19", config=data.frame(RunParallel=TRUE))

#	creating the quality control report in html format
ChIPQCreport(resqc)

Examine the html report.

What do you think?

Are results in line with the previous quality control workflow?

The report can be also downloaded from Box here