INMEX

INMEX performs integrative meta-analysis of multiple gene-expression and metabolomics datasets to identify significant genes and pathways across studies.

Key Features:

• Data integration and meta-analysis: Combines multiple gene-expression and metabolomics datasets to reduce study bias and increase statistical power across studies.
• Data Preparation Module: Provides annotation and visualization of individual datasets for downstream analysis.
• Statistical Analysis Module: Combines datasets using P-values, effect sizes, rank orders, and other metrics to identify significant genes across studies.
• Functional Analysis Module: Performs enriched Gene Ontology (GO) term analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway exploration, and expression profile visualization.
• Identifier and platform support: Includes built-in support for common gene and metabolite identifiers and supports 45 microarray platforms for human, mouse, and rat datasets.

Scientific Applications:

• Meta-analysis of gene expression: Identifies robust differentially expressed genes by integrating diverse gene-expression studies.
• Systems biology and multi-omics integration: Integrates gene-expression and metabolomics data to explore the interplay between genes and metabolites and to investigate metabolic pathways.
• Comparative analysis across conditions: Supports analysis of datasets from different physiological states or disease conditions to uncover consistent biological signals.

Methodology:

Computational steps explicitly include dataset annotation and visualization, statistical combination of datasets using P-values, effect sizes, rank orders and other metrics, and functional analyses via GO enrichment, KEGG pathway exploration, and expression profile visualization, with mapping of common gene/metabolite identifiers and support for 45 microarray platforms.