BatchQC: Batch Effects Quality Control

Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. Moreover, BatchQCvinteractively applies multiple common batch effect approaches to the data, and the user can quickly see the benefits of each method. BatchQC is developed as a Shiny App. The output is organized into multiple tabs, and each tab features an important part of the batch effect analysis and visualization of the data. The BatchQC interface has the following analysis groups: Summary, Differential Expression, Median Correlations, Heatmaps, Circular Dendrogram, PCA Analysis, Shape, ComBat and SVA.

The package includes:

1. Summary and Sample Diagnostics
2. Differential Expression Plots and Analysis using LIMMA
3. Principal Component Analysis and plots to check batch effects
4. Heatmap plot of gene expressions
5. Median Correlation Plot
6. Circular Dendrogram clustered and colored by batch and condition
7. Shape Analysis for the distribution curve based on HTShape package
8. Batch Adjustment using ComBat
9. Surrogate Variable Analysis using sva package
10. Function to generate simulated RNA-Seq data

batchQC is the pipeline function that generates the BatchQC report and launches the Shiny App in interactive mode. It combines all the functions into one step.

Installation

To begin, install Bioconductor and simply run the following to automatically install BatchQC and all the dependencies, except pandoc, which you have to manually install as follows.

source("http://bioconductor.org/biocLite.R")
biocLite("BatchQC")

Install 'pandoc' package by following the instructions at the following URL: http://pandoc.org/installing.html

Rstudio also provides pandoc binaries at the following location for Windows, Linux and Mac: https://s3.amazonaws.com/rstudio-buildtools/pandoc-1.13.1.zip

If all went well you should now be able to load BatchQC:

require(BatchQC)
browseVignettes("BatchQC")
vignette('BatchQCIntro', package='BatchQC')
vignette('BatchQC_examples', package='BatchQC')
vignette('BatchQC_usage_advanced', package='BatchQC')

If you want to install the latest development version of BatchQC from Github, Use devtools to install it as follows:

require(devtools)
install_github("mani2012/BatchQC", build_vignettes=TRUE)

If you want to manually install the BatchQC dependencies, run the following:

source("http://bioconductor.org/biocLite.R")
biocLite(c('MCMCpack', 'limma', 'preprocessCore', 'sva', 'devtools', 'corpcor', 
'matrixStats', 'shiny', 'ggvis', 'd3heatmap', 'reshape2', 'moments', 
'rmarkdown', 'pander', 'gplots'))

Troubleshooting with Installation

Please make sure you have installed pandoc by following the instructions from http://pandoc.org/installing.html. Otherwise, you may get an error such as the following:

* creating vignettes ... ERROR
Warning in engine$weave(file, quiet = quiet, encoding = enc) :
  Pandoc (>= 1.12.3) and/or pandoc-citeproc is not available. Please install 
  both.
Error: processing vignette 'BatchQCIntro.Rmd' failed with diagnostics:
It seems you should call rmarkdown::render() instead of knitr::knit2html() 
because BatchQCIntro.Rmd appears to be an R Markdown v2 document.
Execution halted
Error: Command failed (1)

For generating pdf vignettes in Linux, you need to install texlive and lmodern as follows:

sudo apt-get install texlive
sudo apt-get install lmodern

If you do not have permissions to install in the default location for R, you may have to setup local directory. You may also want to load a version of R 3.3.0 or higher.

export R_LIBS="/my_own_local_directory/R_libs"
module load R/R-3.3.0

And do something like the following

install.packages("devtools", repos="http://cran.r-project.org", lib="/my_own_local_directory/R_libs")