PNNL

Abstract

Summary: Performing rigorous evaluation of high-throughput omics data for quality issues of either biological samples or measured components (e.g., peptides, metabolites) is essential to robust down-stream statistical analyses. With many datasets, particularly generated via mass spectrometry (MS), challenges with measurement error and missing values complicate the process. While numerous R packages exist for analyzing proteomic and metabolomics data, these do not provide quality control methods robust to missing data and are not broadly applicable to MS data from multiple omics types. We have developed a new R package that offers a suite of quality control processing functions that work with the common data issues associated with MS data. Availability and Implementation: The pmartRqc R package is freely available on the web at https://github.com/pmartR/pmartRqc Contact: kelly.stratton@pnnl.gov Supplementary information: Supplementary information is available at Bioinformatics online, and example data is available at https://github.com/pmartR/pmartRdata.