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pmartR: Quality Control and Statistics for Mass Spectrometry-Based Biological Data
Kelly G Stratton1, Bobbie-Jo M Webb-Robertson1, Lee Ann McCue2
1National Security Directorate , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99354 , United States.
Journal of Proteome Research
|January 15, 2019
Summary
Quality control and statistical analysis of mass spectrometry (MS) data are crucial. The new pmartR R package offers a unified solution for MS data quality control, exploratory analysis, and robust statistical comparisons, even with missing data.
Area of Science:
- Proteomics
- Bioinformatics
- Computational Biology
Background:
- Mass spectrometry (MS) data require rigorous quality control (QC) to minimize bias from process variation and outliers.
- Proteomics data, particularly from liquid chromatography-MS, often contain substantial missing values, complicating statistical analysis.
- Existing R packages address individual MS data challenges, but a comprehensive tool is lacking.
Purpose of the Study:
- To introduce pmartR, an open-source R package designed for comprehensive quality control, exploratory data analysis, and statistical analysis of mass spectrometry data.
- To provide a robust solution for handling missing data inherent in liquid chromatography-MS proteomics datasets.
- To offer integrated visualization capabilities for MS data analysis.
Main Methods:
- Development of the pmartR R package, incorporating modules for data filtering, normalization, and exploratory data analysis.
- Implementation of statistical methods robust to missing data, including a combined quantitative and qualitative statistical test.
- Application of pmartR to a mouse proteomics study comparing smoke exposure to control groups.
Main Results:
- The pmartR package successfully performs QC, EDA, and statistical comparisons on MS data, effectively handling missing values.
- A mouse smoke exposure study demonstrated pmartR's capability to identify proteins missed by quantitative tests alone, revealing 19 significant proteins via a combined statistical approach.
- The package integrates various analytical steps, from QC to statistical comparison, with enhanced visualization tools.
Conclusions:
- pmartR offers a unified, open-source software solution for robust analysis of mass spectrometry proteomics data.
- The package's ability to handle missing data and employ advanced statistical tests improves the identification of biologically relevant proteins.
- pmartR facilitates more reliable and comprehensive interpretation of MS-based proteomics studies.
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