batchCorr

batchCorr reduces unwanted non-biological variation in LC-MS metabolomics data by way of between-batch alignment, within-batch drift correction, and between-batch normalization using batch-specific quality control samples and long-term reference QC samples.

Key Features:

• Between-batch alignment: Aligns features across different batches to correct for batch-to-batch variation.
• Within-batch drift correction: Corrects signal drift occurring within individual batches.
• Between-batch normalization: Normalizes data across batches using batch-specific quality control samples and long-term reference QC samples.
• Tabular data preprocessing: Processes tabular LC-MS metabolomics data for downstream analysis.
• Quality control: Implements quality control procedures for metabolomics data.
• Univariate analysis: Performs single-variable statistical analysis of metabolomics data.
• Multivariate analysis: Performs multi-variable statistical analysis of metabolomics data.
• Quality control visualizations: Generates visualizations for quality control assessment.
• Feature-wise visualizations: Generates visualizations for individual metabolite features.
• Results visualizations: Generates visualizations of analysis results.

Scientific Applications:

• Untargeted LC-MS metabolomics: Supports untargeted liquid chromatography-mass spectrometry metabolomics research as specified in the associated protocol article in the 'Metabolomics Data Processing and Data Analysis—Current Best Practices' special issue of the Metabolites journal (2020).
• Multi-batch data integration: Enables integration and correction of metabolomics data obtained from several batches to improve quality and reproducibility.

Methodology:

The batchCorr package uses batch-specific quality control samples and long-term reference QC samples for between-batch alignment, within-batch drift correction, and between-batch normalization. Please see the associated article for more thorough descriptions of algorithms.