qmtools

qmtools processes quantitative metabolomics data using the standard SummarizedExperiment class to prepare data for statistical analysis.

Key Features:

• Data Imputation: Provides functions for imputing missing values in quantitative metabolomics datasets.
• Normalization: Includes normalization methods to adjust for technical variation across samples.
• Feature Filtering: Enables filtering of metabolic features based on specified criteria.
• Feature Clustering: Supports clustering of metabolic features to identify patterns in the data.
• Dimension Reduction: Implements dimension-reduction techniques for data visualization and exploratory analysis.
• Visualization: Offers visualization functions to aid in data exploration and quality assessment.
• Differential Analysis: Computes empirical Bayes statistics to identify metabolic features that differ between two sets of study samples.
• Cross-Omics Compatibility: Several functions are applicable to other types of omics data beyond metabolomics.

Scientific Applications:

• Metabolomics Data Preprocessing: Prepares quantitative metabolomics data for downstream statistical analysis through systematic processing steps.
• Differential Metabolite Detection: Identifies significantly altered metabolic features between experimental conditions using empirical Bayes statistics.
• Multi-Omics Integration: Applies preprocessing and analytical functions to other omics data types for integrated studies.

Methodology:

The package implements empirical Bayes statistics for differential analysis of metabolic features between two sample sets, along with standard data processing functions for imputation, normalization, filtering, clustering, and dimension reduction.