FunCluster

FunCluster performs functional profiling of gene expression data from cDNA microarray experiments by integrating biological annotations with expression patterns to identify co-regulated biological processes.

Key Features:

• R implementation: Implemented in the R language for analysis of cDNA microarray gene expression data.
• Functional annotation automation: Automates annotation to categorize genes by functional roles using biological annotations.
• Co-clustering procedure for co-regulation detection: Employs a specialized co-clustering procedure to identify groups of co-expressed, putatively co-regulated genes.
• Biological/genomic themes identification: Detects genomic themes representing sets of genes associated with specific biological functions or pathways.
• Integration of annotations and expression profiles: Integrates biological annotations with gene expression profiles to detect significant co-expression patterns.

Scientific Applications:

• Adipose tissue analysis: Applied to human white adipose tissue to highlight the role of nonadipose cells in synthesizing inflammatory and immunity molecules related to human adiposity.
• Skeletal muscle and insulin regulation: Applied to human skeletal muscle datasets focused on insulin regulation, identifying novel functional classes associated with protein metabolism and regulation of muscular contraction.

Methodology:

Implemented in R and based on automated functional annotation, integration of biological annotations with gene expression profiles, and a specialized co-clustering procedure to detect significant co-expression patterns among genes.