VisHiC

VisHiC performs hierarchical clustering and visualization of microarray gene expression data to identify co-expressed gene clusters and annotate them with Gene Ontology (GO), pathway, and regulatory motif enrichment.

Key Features:

• Clustering of Gene Expression Data: Performs hierarchical clustering on gene expression matrices to group genes by similar expression profiles.
• Compact Visualization: Generates dendrograms and heatmaps representing the gene expression matrix to display cluster structure.
• Function Enrichment Analysis: Automates Gene Ontology (GO) enrichment and associates clusters with enriched pathways and regulatory motifs.
• Focused Representation: Contracts clusters with significant functional enrichments and hides less relevant parts to provide a dense overview of thousands of transcripts across multiple conditions.

Scientific Applications:

• Gene Function Discovery: Associate co-expressed genes with enriched GO terms and pathways to propose functions for previously uncharacterized genes.
• Regulatory Mechanism Inference: Identify co-expression modules and linked regulatory motifs to support inference of regulatory relationships.
• Comparative Condition Analysis: Summarize expression patterns across multiple conditions to aid hypothesis generation and experimental design.
• Global Dataset Overview: Provide compact, functionally annotated summaries of large microarray datasets spanning thousands of transcripts.

Methodology:

Hierarchical clustering of microarray-derived gene expression matrices, generation of dendrograms and heatmaps, automated Gene Ontology (GO) enrichment and pathway and regulatory motif analysis, and contraction of clusters with significant functional enrichments.