malbacR

malbacR performs batch effect correction for omics data derived from LC-MS/MS and GC-MS mass spectrometry studies to enable standardized comparison across large, batched sample sets.

Key Features:

• Supported data types: Processes omics datasets generated by LC-MS/MS and GC-MS mass spectrometry.
• Included algorithms: Implements 11 batch effect correction algorithms: pareto scaling, power scaling, range scaling, ComBat, EigenMS, NOMIS, RUV-random, QC-RLSC, WaveICA2.0, TIGER, and SERRF.
• Standardized formats: Standardizes input and output data formats across all implemented correction methods.
• Integration: Integrates with the pmartR package to apply batch correction within a pmartR workflow.
• Comparative analysis: Enables implementation and direct comparison of multiple correction approaches on the same dataset.
• Bias correction: Corrects systematic inter-batch biases arising from sample handling, temperature fluctuations, timing variability, and column degradation.

Scientific Applications:

• Inter-batch correction: Removes technical inter-batch variation in large-scale LC-MS/MS and GC-MS omics experiments.
• Method benchmarking: Facilitates benchmarking and selection of batch correction methods for comparative studies.
• Reproducibility and comparability: Improves reproducibility and comparability of quantitative omics results across studies and batches.
• Workflow integration: Applies correction within pmartR-based analyses to produce corrected datasets for downstream analyses.

Methodology:

Consolidates and applies 11 explicit batch correction algorithms (pareto scaling, power scaling, range scaling, ComBat, EigenMS, NOMIS, RUV-random, QC-RLSC, WaveICA2.0, TIGER, SERRF) and standardizes data input and output formats, with compatibility for integration into pmartR workflows.