MultiDCoX

MultiDCoX performs multi-factor differential co-expression analysis to identify gene sets whose co-expression patterns vary across multiple factors and to quantify each factor's influence on those patterns.

Key Features:

• Multi-Factor Analysis Capability: Handles multiple factors simultaneously, including genetic markers, clinical variables, and treatment conditions.
• Greedy Search Algorithm: Employs a greedy search algorithm to navigate the search space for differentially co-expressed gene sets.
• Space and Time Efficiency: Implements strategies aimed at reducing computational time and memory requirements for large genomic datasets.
• Identification of Differentially Co-Expressed Gene Sets: Detects gene sets whose co-expression relationships change across biological conditions.
• Quantification of Factor Influence: Estimates the contribution of each factor to observed differential co-expression patterns.

Scientific Applications:

• Breast Cancer Analysis: Applied to breast cancer datasets to identify biologically meaningful differentially co-expressed gene sets and to elucidate roles of genetic and clinical factors in co-expression changes.
• Complex Disease Studies: Supports investigation of how multiple biological variables interact to shape gene expression networks in diseases such as cancer.

Methodology:

Uses a greedy search algorithm to identify differentially co-expressed gene sets across multiple factors and to quantify each factor's influence.