MBQN Package

Mean/Median-balanced quantile normalization for preprocessing omics data

Description

This package contains a modified quantile normalization (QN) for preprocessing and analysis of omics or other matrix-like organized data with intensity values biased by global, columnwise distortions of intensity mean and scale. The modification balances the mean intensity of features (rows) which are rank invariant (RI) or nearly rank invariant (NRI) across samples (columns) before quantile normalization [1]. This helps to prevent an over-correction of the intensity profiles of RI and NRI features by classical QN and therefore supports the reduction of systematics in downstream analyses. Additional package functions help to detect, identify, and visualize potential RI or NRI features in the data and demonstrate the use of the modification.

Installation

To install this package, you need R version >= 3.6.

For installation from Bioconductor run in R:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("MBQN")

For installation from Github run in R:

install.packages("devtools")
devtools::install_github("arianeschad/MBQN")

or

install.packages("githubinstall")
githubinstall::githubinstall("MBQN")

Dependencies

The core of the MBQN package uses normalizeQuantiles() from the package limma[2], available at https://bioconductor.org/packages/release/bioc/html/limma.html, for computation of the quantile normalization. Optionally, normalize.quantiles() from the package preprocessCore[3], available at https://bioconductor.org/packages/release/bioc/html/preprocessCore.html, can be used.

To install these packages in R run:

if (!requireNamespace("BiocManager", quietly = TRUE))
   install.packages("BiocManager")
BiocManager::install(pkgs = c("preprocessCore","limma", "SummarizedExperiment"))

Usage

Further information about the package is provided at the wiki
https://github.com/arianeschad/MBQN/wiki

References

[1] Brombacher, E., Schad, A., Kreutz, C. (2020). Tail-Robust Quantile Normalization. Proteomics.
[2] Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., and Smyth, G.K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43(7), e47.
[3] Ben Bolstad (2018). preprocessCore: A collection of pre-processing functions. R package version 1.44.0. https://github.com/bmbolstad/preprocessCore.