pmartR: Quality Control and Statistics for Mass Spectrometry-Based Biological Data
datasetposted on 2019-09-17, 00:43 authored by Kelly G. Stratton, Bobbie-Jo M. Webb-Robertson, Lee Ann McCue, Bryan Stanfill, Daniel Claborne, Iobani Godinez, Thomas Johansen, Allison M. Thompson, Kristin E. Burnum-Johnson, Katrina M. Waters, Lisa M. Bramer
Prior to statistical analysis of mass spectrometry (MS) data, quality control (QC) of the identified biomolecule peak intensities is imperative for reducing process-based sources of variation and extreme biological outliers. Without this step, statistical results can be biased. Additionally, liquid chromatography–MS proteomics data present 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 and normalization), exploratory data analysis (EDA), visualization, and statistical analysis robust to missing data. Example analysis using proteomics data from a mouse study comparing smoke exposure to control 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, 19 proteins whose statistical significance would have been missed by a quantitative test alone were identified. 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.
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R packageopen-source R packageproteomics dataquality controlmass spectrometrymouse studyR packagesprocess-based sourcesMS dataMass Spectrometry-Basedsmoke exposuredata analysispmartR packageExample analysisEDAbiomolecule peak intensitiescore functionalityQCQuality Controlsoftware tool19 proteinsvisualization capabilities