GSGS: A Computational Framework to Reconstruct Signaling Pathways from Gene Sets

摘要: We propose a novel two-stage Gene Set Gibbs Sampling (GSGS) framework, to reverse engineer signaling pathways from gene sets inferred molecular profiling data. hypothesize that are structurally an ensemble of overlapping linear signal transduction events which we encode as Information Flow Sets (IFGS's). infer corresponding these subjected random permutation genes within each set. In Stage I, use source separation algorithm derive unordered and IFGS's data, allowing cross talk among IFGS's. II, develop sampling like algorithm, Sampler, reconstruct the latent derived in I. The novelty this framework lies seamless integration two stages hypothesis basic building blocks for pathways. proof-of-concept studies, our approach is shown outperform existing Bayesian network approaches using both continuous discrete data generated benchmark networks DREAM initiative. perform comprehensive sensitivity analysis assess robustness approach. Finally, implement GSGS breast cancer cells.