CRCView

CRCView clusters and visualizes microarray gene expression data using a Dirichlet process mixture model (Chinese Restaurant Clustering) to identify expression patterns and enable Gene Ontology-based functional interpretation.

Key Features:

• Clustering algorithm: Uses a Dirichlet process mixture model implementing Chinese Restaurant Clustering (CRC) to cluster genes by their expression profiles.
• Gene clustering: Groups genes based on similarity of microarray expression profiles to reveal patterns and relationships.
• Input formats: Accepts a flexible range of microarray data input formats and supports diverse experimental designs.
• Visualization: Produces graphical representations of clustering results to depict cluster structure and expression patterns.
• Gene Ontology annotation: Associates clusters with Gene Ontology (GO) terms for functional interpretation.

Scientific Applications:

• Microarray expression analysis: Identification of expression patterns and relationships in microarray gene expression datasets.
• Functional annotation: Linking gene clusters to Gene Ontology terms to interpret biological functions.
• Regulatory and interaction studies: Exploration of genetic interactions and regulatory mechanisms through cluster-derived patterns.

Methodology:

Clustering is performed using a Dirichlet process mixture model implementing Chinese Restaurant Clustering; outputs include graphical cluster visualizations and Gene Ontology term-based annotation.