MCbiclust

MCbiclust identifies large correlated biclusters in gene expression and transcriptomics datasets to uncover co-regulated gene networks within biologically heterogeneous data.

Key Features:

• Novel Biclustering Algorithm: Selects both genes and samples from large datasets to detect large biclusters by maximizing correlation in gene expression.
• Handling Biological Heterogeneity: Manages biologically heterogeneous transcriptomics collections to reveal co-regulated gene modules across diverse samples.
• Biological Relevance: Validated on synthetic datasets and applied to large bacterial and cancer cell datasets, yielding biclusters with statistical significance and biological relevance.
• Applications in Disease Diagnosis and Network Analysis: Enables identification of transcriptomics-based diagnostic indicators and downstream network analyses to investigate genotype-phenotype correlations.

Scientific Applications:

• Co-regulated Gene Network Discovery: Detects large co-regulated gene networks and biclusters in gene expression datasets.
• Multi-gene Controlled Processes: Identifies processes such as energy metabolism, organelle biogenesis, and stress responses that are controlled by multiple genes.
• Disease-associated Transcriptomics: Facilitates discovery of transcriptomics features relevant to disease diagnosis and comparison across conditions.
• Benchmarking and Validation: Supports benchmarking using synthetic datasets and application to large bacterial and cancer cell datasets.

Methodology:

Selects a supplied gene set of size n and identifies the maximum-strength correlation matrix containing m samples by selecting genes and samples that maximize correlation in gene expression.