rqubic

rqubic implements the QUBIC algorithm to perform qualitative biclustering of gene expression data, identifying gene subsets with coherent expression patterns across specific subsets of experimental conditions to reveal gene regulation and interaction networks.

Key Features:

• Generalized biclustering solution: Uses qualitative and semi-quantitative measures to solve a generalized form of the biclustering problem on gene expression matrices.
• Identification of statistically significant biclusters: Enumerates all statistically significant biclusters, including those exhibiting scaling patterns.
• Combinatorial optimization for efficiency: Employs combinatorial optimization to process datasets with tens of thousands of genes under thousands of conditions within minutes on standard hardware.
• Validated performance: Demonstrates improved biclustering performance validated across benchmark sets and additional datasets.
• Implementation: Implemented in ANSI C and tested with GCC 4.1.2 on Linux systems.

Scientific Applications:

• Gene expression pattern discovery: Detects condition-specific co-expression modules in genomics studies.
• Regulatory network inference: Supports discovery of gene regulatory interactions and modular regulation.
• Disease mechanism analysis: Identifies biclusters relevant to understanding disease-associated expression changes.
• Biomarker and therapeutic target identification: Reveals candidate biomarkers and condition-specific therapeutic targets through detected biclusters.

Methodology:

The QUBIC algorithm applies qualitative/semi-quantitative discretization combined with combinatorial optimization to enumerate statistically significant biclusters and detect scaling patterns in gene expression data.