Prior to statistical analysis of mass spectrometry (MS) data, quality control (QC) of the identified biomolecule peak intensities is imperative to remove outliers and random effects that arise from the mapping of raw mass spectra to identified biomolecules with observed values. Without this step, statistical results can be biased. Additionally, liquid-chromatography-MS proteomics data presents inherent challenges due to large amounts of missing data that require special consideration during statistical analysis. While a number of R packages exist to address these challenges individually, there is no single R package that addresses all of them. We present pmartR, an open-source R package, for QC (filtering, normalization, exploratory data analysis (EDA)), visualization, and statistical analysis robust to missing data. Example analysis using proteomics data from a virology study comparing infected and control samples demonstrates the core functionality of the package and highlights the capabilities for handling missing data. In particular, using a combined quantitative and qualitative statistical test 56 proteins were identified whose statistical significance would have been missed by a quantitative test alone. The pmartR package provides a single software tool for QC, EDA, and statistical comparisons of MS data that is robust to missing data and includes numerous visualization capabilities.
Revised: April 17, 2019 |
Published: March 1, 2019