malbacR: A Package for Standardized Implementation of Batch Correction Methods for Omics Data

Mass spectrometry is a powerful tool for identifyingand analyzingbiomolecules such as metabolites and lipids in complex biologicalsamples. Liquid chromatography and gas chromatography mass spectrometrystudies quite commonly involve large numbers of samples, which canrequire significant time for sample preparation and analyses. To accommodatesuch studies, the samples are commonly split into batches. Inevitably,variations in sample handling, temperature fluctuation, imprecisetiming, column degradation, and other factors result in systematicerrors or biases of the measured abundances between the batches. Numerousmethods are available via R packages to assist with batch correctionfor omics data; however, since these methods were developed by differentresearch teams, the algorithms are available in separate R packages,each with different data input and output formats. We introduce themalbacR package, which consolidates 11 common batch effect correctionmethods for omics data into one place so users can easily implementand compare the following: pareto scaling, power scaling, range scaling,ComBat, EigenMS, NOMIS, RUV-random, QC-RLSC, WaveICA2.0, TIGER, andSERRF. The malbacR package standardizes data input and output formatsacross these batch correction methods. The package works in conjunctionwith the pmartR package, allowing users to seamlessly include thebatch effect correction in a pmartR workflow without needing any additionaldata manipulation.

Automated summary

Mass spectrometry is a powerful tool for identifying and analyzing biomolecules in complex biological samples. However, variations between sample batches can lead to systematic errors or biases in the measured abundances. To address this issue, we introduce the malbacR package, which consolidates 11 commonly used batch effect correction methods, allowing users to easily implement and compare different methods.