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We propose a novel two-stage Gene Set Gibbs Sampling (GSGS) framework, toreverse engineer signaling pathways from gene sets inferred from molecularprofiling data. We hypothesize that signaling pathways are structurally anensemble of overlapping linear signal transduction events which we encode asInformation Flow Gene Sets (IFGS's). We infer pathways from gene setscorresponding to these events subjected to a random permutation of genes withineach set. In Stage I, we use a source separation algorithm to derive unorderedand overlapping IFGS's from molecular profiling data, allowing cross talk amongIFGS's. In Stage II, we develop a Gibbs sampling like algorithm, Gene Set GibbsSampler, to reconstruct signaling pathways from the latent IFGS's derived inStage I. The novelty of this framework lies in the seamless integration of thetwo stages and the hypothesis of IFGS's as the basic building blocks for signalpathways. In the proof-of-concept studies, our approach is shown to outperformthe existing Bayesian network approaches using both continuous and discretedata generated from benchmark networks in the DREAM initiative. We perform acomprehensive sensitivity analysis to assess the robustness of the approach.Finally, we implement the GSGS framework to reconstruct signaling pathways inbreast cancer cells.

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