metaX

metaX performs end-to-end analysis of untargeted metabolomics data generated by mass spectrometry to support metabolite profiling, biomarker selection, and pathway-level interpretation.

Key Features:

• Peak Picking and Annotation: Automates identification and annotation of peaks in mass spectrometry data for metabolite profiling.
• Data Quality Assessment: Provides methods for evaluating raw mass spectrometry data quality to ensure reliability of downstream analyses.
• Missing Value Imputation: Implements imputation approaches to handle missing values common in large-scale metabolomics datasets.
• Data Normalization: Applies normalization techniques to correct systematic variations across samples.
• Statistical Analysis: Supports univariate and multivariate statistical analyses for detecting differential metabolites and multivariate patterns.
• Power Analysis and Sample Size Estimation: Calculates statistical power and estimates required sample size for experimental design.
• Receiver Operating Characteristic (ROC) Analysis: Performs ROC analysis to evaluate diagnostic performance of candidate biomarkers.
• Biomarker Selection: Includes methods for selecting significant biomarkers from metabolomics datasets.
• Pathway Annotation and Correlation Network Analysis: Enables pathway annotation and construction of correlation networks to explore metabolic interactions.
• Metabolite Identification: Supports comprehensive identification of metabolites to map spectral features to known compounds.

Scientific Applications:

• Systems Biology: Facilitates comprehensive metabolomic profiling to integrate metabolic data into systems-level analyses.
• Clinical Diagnostics: Supports biomarker discovery and evaluation for diagnostic and prognostic studies.
• Pharmacology: Enables detection of metabolic changes relevant to drug response and toxicology studies.
• Biomarker Discovery: Provides workflows for identifying and validating candidate metabolic biomarkers from untargeted datasets.

Methodology:

Implemented as an R package and performing peak picking, annotation, data quality assessment, missing value imputation, normalization, univariate and multivariate statistical analyses, power and sample size estimation, ROC analysis, biomarker selection, pathway annotation, correlation network analysis, and metabolite identification.