ExpressVis

ExpressVis enables interactive analysis and visualization of multi-omics expression data to support differential expression, clustering, survival analysis, and integration with protein-protein interaction networks and pathway maps for systems-biology and clinical-cohort studies.

Key Features:

• Modular analysis: Organizes functionalities into six distinct analytical modules, including differential expression analysis, clustering analysis, survival analysis, protein-protein interaction integration, and pathway mapping.
• Differential Expression Analysis: Performs differential expression analysis to identify genes or proteins with significant changes between conditions.
• Clustering Analysis: Performs clustering to group similar samples for disease subtype discovery and patient stratification.
• Survival Analysis: Performs survival analysis to evaluate the prognostic significance of specific biomarkers.
• Protein-Protein Interaction Integration: Integrates expression data with protein-protein interaction networks to contextualize molecular interactions.
• Pathway Mapping: Maps expression changes onto pathway maps to interpret affected biological pathways.
• Interactive Visualization: Produces interactive figures and tables that enable detailed exploration within and across modules.
• Clinical validation: Has been applied to re-analyze a published hepatocellular carcinoma multi-omics cohort and reproduced key findings from the original study.

Scientific Applications:

• Differential expression studies: Identification of genes or proteins with significant expression changes across experimental conditions.
• Sample stratification and subtype discovery: Clustering analyses to define disease subtypes or patient groups for downstream analysis.
• Prognostic biomarker evaluation: Use of survival analysis to assess associations between expression features and clinical outcomes.
• Systems biology and pathway analysis: Integration of multi-omics data with protein-protein interaction networks and pathway maps to generate and validate hypotheses about regulatory mechanisms.
• Clinical-cohort multi-omics analysis: Analysis and validation of multi-omics clinical cohorts, including hepatocellular carcinoma datasets, in genomics and proteomics contexts.

Methodology:

Modular analysis employing state-of-the-art algorithms for data processing and visualization.