pmartR

pmartR provides R-based quality control, statistical analysis, and visualization for mass spectrometry proteomics (isobaric-labeled and label-free), lipidomics, metabolomics including NMR, and transcriptomics to support differential and exploratory omics analyses.

Key Features:

• Quality Control (QC): Implements robust QC functions for mass spectrometry and other omics datasets to assess data integrity prior to downstream analysis.
• Statistical Analysis: Integrates DESeq2, edgeR, and limma-voom for differential and statistical analyses of transcriptomic and other omics data.
• Proteomics Support: Supports proteomic datasets including isobaric-labeled and unlabeled (label-free) mass spectrometry experiments.
• Lipidomics and Metabolomics Support: Processes lipidomic and metabolomic datasets and explicitly includes support for nuclear magnetic resonance (NMR) metabolomic data.
• Visualization: Provides visualization features including paired data analysis and integration with trelliscopejs for trellis displays.
• Unified Omics Processing Pipeline: Applies a unified processing approach across proteomics, metabolomics, lipidomics, and transcriptomics to standardize reporting and statistical workflows.

Scientific Applications:

• Proteomics: Quality control, differential expression, and exploratory analysis of mass spectrometry proteomic datasets including isobaric and label-free experiments.
• Metabolomics: Processing and statistical analysis of mass spectrometry and NMR metabolomic data for biomarker discovery and comparative studies.
• Lipidomics: Analysis and visualization of lipidomic mass spectrometry datasets for comparative and exploratory studies.
• Transcriptomics: Differential expression analysis and integration of transcriptomic results using DESeq2, edgeR, and limma-voom.
• Cross-omic Comparative Studies: Enables comparative analyses and enhances reproducibility across different biological conditions by unifying processing of diverse omic data types.

Methodology:

Implements QC, statistical analysis, and visualization workflows in R by leveraging packages such as DESeq2, edgeR, limma-voom, and trelliscopejs.