apLCMS

apLCMS processes Liquid Chromatography–Mass Spectrometry (LC/MS) metabolomics data as an R package to detect, align, and quantify peaks and to correct batch effects in large-scale studies.

Key Features:

• Batch Effect Management: Applies preprocessing to detect and correct batch effects arising from samples processed across multiple LC/MS batches, improving peak alignment and quantification.
• Unsupervised Analysis: Performs de novo peak detection directly from raw LC/MS data without relying on prior feature lists.
• Hybrid Analysis: Integrates de novo peak detection with known metabolites and historically detected features from the same LC/MS system to improve feature identification.
• Improved Consistency and Downstream Analysis: Produces consistent feature tables that facilitate statistical modeling and biomarker discovery.
• Integration with Quality Control Samples: Validated using standardized quality control (QC) plasma samples and real biological study data.

Scientific Applications:

• Large-scale metabolomics studies: Enables reliable processing of studies with large sample sizes that require multi-batch LC/MS acquisition.
• Biomarker discovery: Supports identification of metabolic biomarkers through improved preprocessing and feature quantification.
• Disease mechanism studies: Facilitates metabolomic profiling to explore biochemical changes underlying disease mechanisms.
• Environmental metabolomics: Supports investigation of environmental impacts on metabolism through consistent feature detection across batches.

Methodology:

Preprocessing to manage batch effects; de novo peak detection; hybrid analysis combining de novo peaks with known metabolites and historically detected features; peak alignment and quantification; generation of feature tables.