Wigwams

Wigwams identifies modules of co-regulated genes by biclustering gene expression time series to detect condition-dependent regulatory relationships.

Key Features:

• Condition-Dependent Co-Expression Analysis: Detects gene modules that exhibit tight co-expression across subsets of time series datasets, reflecting condition-dependent regulation.
• Statistical Testing of Expression Patterns: Statistically evaluates expression patterns of each gene within subsets of conditions to identify shared regulatory signatures without requiring predefined time points.
• Incorporation of Differential Expression Data: Integrates differential expression relative to control conditions to distinguish co-regulatory signals from background noise.
• Non-Redundant Output for Network Reconstruction: Produces succinct, non-redundant module outputs that facilitate focused gene network reconstruction across different conditions.

Scientific Applications:

• Arabidopsis thaliana time series analysis: Applied to six Arabidopsis thaliana time series expression datasets to identify biologically significant modules spanning combinations of experimental conditions.
• Dynamic regulatory network discovery: Supports identification of condition-dependent gene co-regulation for studies of dynamic gene expression and regulatory networks.

Methodology:

Employs a biclustering approach tailored for time series data, analyzes similarities in gene expression patterns within each condition, statistically evaluates expression patterns of genes within subsets of conditions, tests dependence or independence of these patterns across different conditions, and can incorporate differential expression relative to control conditions.