MetaOmics

MetaOmics performs integrative transcriptomic meta-analysis to combine multiple transcriptomic studies and increase statistical power, accuracy, and reproducibility for comparative and functional genomic investigations.

Key Features:

• Quality control: Implements quality control measures for transcriptomic datasets.
• Differential expression analysis: Supports differential expression analysis across studies.
• Pathway enrichment analysis: Performs pathway enrichment analysis for functional interpretation.
• Differential co-expression network analysis: Conducts differential co-expression network analysis to compare gene-gene relationships between conditions.
• Prediction: Provides prediction capabilities for downstream classification or biomarker evaluation.
• Clustering: Offers clustering techniques for grouping samples or genes.
• Dimension reduction: Includes dimension reduction strategies for data visualization and feature reduction.
• Transcriptomic meta-analytic methods: Incorporates over ten in-house transcriptomic meta-analytic methods alongside numerous public methods.
• Study integration strategies: Integrates multiple studies related to a common hypothesis to enhance statistical power, accuracy, and reproducibility.
• Analytical paradigms: Employs both data-driven and biological-aim-driven strategies for analysis.

Scientific Applications:

• Transcriptomic meta-analysis: Meta-analysis of transcriptomic datasets across independent studies.
• Cross-study reproducibility: Increasing statistical power and reproducibility through study integration.
• Functional interpretation: Identifying enriched pathways and functional signatures from combined datasets.
• Network biology: Comparing differential co-expression networks to reveal condition-specific gene interactions.
• Prediction and biomarker discovery: Deriving predictive models and candidate biomarkers from aggregated transcriptomic data.
• Clustering and dimensionality reduction: Structuring and visualizing high-dimensional transcriptomic data for interpretation.

Methodology:

Implements quality control, differential expression analysis, pathway enrichment analysis, differential co-expression network analysis, prediction, clustering, and dimension reduction; integrates multiple studies using over ten in-house transcriptomic meta-analytic methods alongside public meta-analysis methods; and applies both data-driven and biological-aim-driven analytical strategies.