BayesKnockdown

BayesKnockdown applies a Bayesian framework to compute posterior probabilities that assess the effects of a knocked-down gene (single predictor) on multiple outcome genes to infer genetic interactions.

Key Features:

• Bayesian Framework: Calculates posterior probabilities using Bayesian inference to assess gene–gene interactions following a knockdown experiment.
• Incorporation of Prior Probabilities: Integrates prior probabilities about potential relationships between genes into the Bayesian model.
• Single-predictor, multiple-outcome modeling: Models a single predictor (the knocked-down gene) against multiple potential outcome genes.
• Applicability to differential expression and 2-class data: Supports analysis of differential expression and other two-class data scenarios.
• Computational Efficiency: Implemented to be simple and fast for analysis of genomic datasets.

Scientific Applications:

• Functional Genomics: Identify genes affected by targeted knockdown experiments to study gene function.
• Systems Biology and Regulatory Network Analysis: Infer probabilistic relationships among genes to support reconstruction and analysis of regulatory networks.
• Hypothesis Generation and Validation: Provide posterior probabilities to prioritize candidate target genes for experimental validation and hypothesis testing.

Methodology:

Performs Bayesian inference computing posterior probabilities and incorporating prior probabilities for a single predictor versus multiple outcomes; implemented within the Bioconductor project using the R programming language.