EnsemblQDB

EnsemblQDB performs query-based biclustering of gene expression compendia to identify genes with expression profiles matching specified gene lists or individual query genes and integrates results across query-genes and parameter settings to generate consensus biclusters.

Key Features:

• Query-Based Biclustering: Identifies genes whose expression profiles closely match the average profile of a specified gene list or an individual query gene within an expression compendium.
• Individual-Gene Query Strategy: Supports using each gene separately as a query to address non-coexpression within large query lists and to detect biclusters targeted to single-gene profiles.
• Ensemble Consensus Matrix: Builds a consensus matrix that integrates biclustering outcomes across multiple query-genes and parameter settings to reduce redundancy.
• Clustering of Consensus Matrix: Applies clustering to the consensus matrix to extract distinct, non-redundant consensus biclusters.
• Statistical Robustness: Merges results in a statistically robust manner to maximize information retention from the original query-based biclustering outputs.
• Parameter Sweep and Post-processing: Accommodates multiple parameter settings and aggregates their outputs to address redundancy and optimize bicluster size detection.
• Biological Case Study: Has been applied to a gene expression case study involving Escherichia coli.

Scientific Applications:

• Coexpression discovery: Identify coexpressed genes and functionally related gene clusters from large expression compendia.
• Regulatory mechanism exploration: Reveal shared expression patterns to support inference of gene regulatory relationships.
• Hypothesis generation: Generate candidate gene sets for experimental validation.
• Robustness assessment: Compare and aggregate biclustering outcomes across parameter settings to assess result stability.

Methodology:

Performs query-based biclustering by matching compendium gene profiles to the average profile of a query list or individual genes, iterating queries across genes and parameter settings; constructs a consensus matrix integrating these biclustering outcomes and clusters that matrix to produce consensus biclusters.