ExpHunterSuite

ExpHunterSuite performs comprehensive transcriptomic data analysis to detect co-expression modules, integrate results across multiple analytical methods for data fusion, and classify gene expression patterns using threshold-based approaches and a Naïve Bayes classifier.

Key Features:

• Co-expression Module Detection: Detects co-expression modules and identifies gene clusters with similar expression patterns across conditions or samples.
• Data Fusion Techniques: Integrates and fuses results from multiple analytical methods to support consensus-based inference and enhance robustness.
• Threshold-Based Methods and Naïve Bayes Classifier: Implements threshold-based techniques and a Naïve Bayes classifier trained on simulated data for classification and interpretation of expression patterns.
• Modular and Scalable Architecture: Provides a modular, scalable architecture enabling independent or combined execution of analysis modules for large transcriptomic datasets.
• R/Bioconductor Implementation: Implemented as an R/Bioconductor package for integration within R-based analysis workflows.

Scientific Applications:

• Rare-Disease Research: Applied to rare-disease model datasets to corroborate immune cell infiltration signals and to propose hypotheses such as involvement of calcium channels.
• Simulated and Spike-In Experiments: Validated on simulated and spike-in experiments, demonstrating that consensus-based combinations of methods and classifiers improve analytical accuracy.
• Model and Non-Model Organisms: Applicable to transcriptomic studies in both model and non-model organisms.

Methodology:

Integrates co-expression module detection with data-fusion techniques, threshold-based methods, consensus approaches, and a Naïve Bayes classifier trained on simulated data.