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Title: pmartR : Quality Control and Statistics for Mass Spectrometry-Based Biological Data

Abstract

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.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [3]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [1]
  1. National Security Directorate, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99354, United States
  2. Earth &, Biological Sciences Directorate, Pacific Northwest National Laboratory, 902 Battelle Boulavard, Richland, Washington 99354, United States
  3. Department of Statistics, Florida State University, 117 North Woodward Avenue, Tallahassee, Florida 32306, United States
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1507351
Report Number(s):
PNNL-SA-138429
Journal ID: ISSN 1535-3893
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Journal of Proteome Research
Additional Journal Information:
Journal Volume: 18; Journal Issue: 3; Journal ID: ISSN 1535-3893
Publisher:
American Chemical Society (ACS)
Country of Publication:
United States
Language:
English

Citation Formats

Stratton, Kelly G., Webb-Robertson, Bobbie-Jo M., McCue, Lee Ann, Stanfill, Bryan, Claborne, Daniel, Godinez, Iobani, Johansen, Thomas, Thompson, Allison M., Burnum-Johnson, Kristin E., Waters, Katrina M., and Bramer, Lisa M. pmartR : Quality Control and Statistics for Mass Spectrometry-Based Biological Data. United States: N. p., 2019. Web. doi:10.1021/acs.jproteome.8b00760.
Stratton, Kelly G., Webb-Robertson, Bobbie-Jo M., McCue, Lee Ann, Stanfill, Bryan, Claborne, Daniel, Godinez, Iobani, Johansen, Thomas, Thompson, Allison M., Burnum-Johnson, Kristin E., Waters, Katrina M., & Bramer, Lisa M. pmartR : Quality Control and Statistics for Mass Spectrometry-Based Biological Data. United States. doi:10.1021/acs.jproteome.8b00760.
Stratton, Kelly G., Webb-Robertson, Bobbie-Jo M., McCue, Lee Ann, Stanfill, Bryan, Claborne, Daniel, Godinez, Iobani, Johansen, Thomas, Thompson, Allison M., Burnum-Johnson, Kristin E., Waters, Katrina M., and Bramer, Lisa M. Mon . "pmartR : Quality Control and Statistics for Mass Spectrometry-Based Biological Data". United States. doi:10.1021/acs.jproteome.8b00760.
@article{osti_1507351,
title = {pmartR : Quality Control and Statistics for Mass Spectrometry-Based Biological Data},
author = {Stratton, Kelly G. and Webb-Robertson, Bobbie-Jo M. and McCue, Lee Ann and Stanfill, Bryan and Claborne, Daniel and Godinez, Iobani and Johansen, Thomas and Thompson, Allison M. and Burnum-Johnson, Kristin E. and Waters, Katrina M. and Bramer, Lisa M.},
abstractNote = {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.},
doi = {10.1021/acs.jproteome.8b00760},
journal = {Journal of Proteome Research},
issn = {1535-3893},
number = 3,
volume = 18,
place = {United States},
year = {2019},
month = {1}
}