PmartR: Quality Control and Statistics for Mass Spectrometry-Based Biological Data
Document Type
Article
Publication Date
3-1-2019
Abstract
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.
Publication Title
Journal of Proteome Research
Volume
18
Issue
3
First Page
1418
Last Page
1425
Digital Object Identifier (DOI)
10.1021/acs.jproteome.8b00760
PubMed ID
30638385
ISSN
15353893
E-ISSN
15353907
Citation Information
Stratton, K.G., Webb-Robertson, B.J.M., McCue, L.A., Stanfill, B., Claborne, D., Godinez, I., Johansen, T., Thompson, A. M., Burnum-Johnson, K.E., Waters, K. M., Bramer, L.M. (2019) Quality Control and Statistics for Mass Spectrometry-Based Biological Data. Journal of Proteome Research, 18(3), 1418-1425.