pmartR 2.0: A Quality Control, Visualization, and Statistics Pipeline for Multiple Omics Datatypes.
Journal of Proteome Research
|January 9, 2023
Summary
The pmartR package now offers unified omics data analysis, including quality control and statistics for proteomic, metabolomic, lipidomic, and transcriptomic data. New features enhance statistical capabilities and data visualization for the omics community.
Area of Science:
- Multi-omics data analysis
- Bioinformatics tools
- Statistical computing
Background:
- The pmartR package facilitates quality control (QC) and analysis of mass spectrometry data.
- Initial focus on proteomic, metabolomic, and lipidomic datasets.
- Growing user base necessitates expanded functionality.
Purpose of the Study:
- To present enhancements to the pmartR package.
- To highlight new QC and statistical capabilities for diverse omics data.
- To demonstrate improved data visualization and unified omics processing.
Main Methods:
- Integration of DESeq2, edgeR, and limma-voom for transcriptomic data analysis.
- Inclusion of QC and statistics for nuclear magnetic resonance metabolomic data.
- Implementation of paired data support and trelliscopejs integration for visualization.
Main Results:
- pmartR now supports a wider range of omics data types, including transcriptomics and NMR metabolomics.
- Enhanced statistical analysis options, including paired data.
- Improved visualization capabilities through trelliscopejs integration.
Conclusions:
- The expanded pmartR package provides a more unified pipeline for omics data processing and analysis.
- New features streamline reporting and statistical analysis for researchers.
- The enhancements benefit the broader omics community by offering versatile and integrated tools.
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