CLIC

CLIC clusters large microarray gene expression datasets to identify homogeneous gene groups and expression patterns for biological interpretation.

Key Features:

• Dimensional clustering approach: Genes are initially clustered within individual dimensions and assigned ordinal labels that underpin subsequent comprehensive clustering across dimensions.
• Iterative sub-clustering: The method performs iterative sub-clustering to generate more homogeneous gene groups and resolve expression pattern subtleties.
• Parallelized computation: Clustering computations are parallelized to reduce processing time for large datasets.
• Automatic cluster detection: The algorithm automatically determines the optimal number of clusters for the dataset.
• Functional enrichment analysis: Functional enrichment is computed for each identified cluster and expression pattern to assess biological significance.

Scientific Applications:

• Expression pattern discovery: Identification of both absolute expression differences and nuanced expression profile patterns across large microarray datasets.
• Gene function and biomarker analysis: Grouping genes by expression patterns to support gene function studies and disease biomarker identification.

Methodology:

CLIC clusters genes within individual dimensions with ordinal labels, performs iterative sub-clustering, parallelizes computations, automatically detects the optimal number of clusters, and computes functional enrichment for identified clusters.