maplet

maplet implements statistical pipelines for metabolomics data analysis using the SummarizedExperiment data structure in R.

Key Features:

• SummarizedExperiment framework: Uses the SummarizedExperiment class to centralize data management, execution of analysis steps, storage of results, and generation of visualizations.
• Modularity: Provides modular functions that can be developed and tested independently to compose analysis pipelines.
• Comprehensive functionality: Exposes over 90 functions covering a broad spectrum of statistical approaches and data-visualization techniques for metabolomics.
• Reproducibility: Integrates pipeline components so that analysis steps and outputs can be documented and reproduced.
• Customization: Allows tailoring of pipelines and analytical workflows to specific research needs.

Scientific Applications:

• Metabolomics data analysis: Analysis of complex metabolomics datasets derived from biological samples.
• Biomarker discovery: Identification of metabolic biomarkers from processed metabolomics data.
• Pathway analysis: Support for investigating metabolic pathways from metabolomics results.
• Intervention and condition profiling: Exploration of biochemical effects of interventions or physiological conditions.

Methodology:

Computations operate on data organized as SummarizedExperiment objects and use modular functions to execute analysis steps, store results, and generate visualizations.