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

Quantitative Methods