Estimating large scale signaling networks through nested effects models from intervention effects in microarray data
Targeted interventions in combination with the measurement of secondary effects can be used to computationally reverse engineer features of upstream nontranscriptional signaling cascades based on the nested effect structure. We extend previous work by Markowetz et al., who proposed a statistical framework to score hypothetical networks for this purpose. Our extensions go in several directions: We show how prior assumptions on the network structure can be incorporated into the scoring scheme by defining appropriate prior distributions on the network structure as well as on hyperparameters. A new approach called module networks is introduced to scale up the original approach, which is limited to around 5 genes, to infer large scale networks (up to more than 50 genes). Instead of the data discretization step needed in the original framework, we propose the usage of a beta-uniform mixture distribution on the p-value profile, resulting from differential gene expression calculation, to quantify effects. Extensive simulations on artificial data and application of our module network approach to infer the signaling network between 10 genes in the ER-$α$pathway in human MCF-7 breast cancer cells show that our approach gives sensible results. Using a bootstrapping approach this reconstruction is found to be statistically stable.
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