Large scale statistical inference of signaling pathways from RNAi and microarray data

Background: The advent of RNA interference techniques enables the selective silencing of biologically interesting genes in an efficient way. In combination with DNA microarray technology this enables researchers to gain insights into signaling pathways by observing downstream effects of individual knock-downs on gene expression. These secondary effects can be used to computationally reverse engineer features of the upstream signaling pathway. Results: In this paper we address this challenging problem by extending previous work by Markowetz et al., who proposed a statistical framework to score networks hypotheses in a Bayesian manner. Our extensions go in three directions: First, we introduce a way to omit the data discretization step needed in the original framework via a calculation based on p-values instead. Second, we show how prior assumptions on the network structure can be incorporated into the scoring scheme using regularization techniques. Third and most important, we propose ...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Holger Fröhlich, Mark Fellmann, Holger Sültmann, Annemarie Poustka, Tim Beißbarth
• Keywords: BMCBI 2007, Et Al, RNA Interference Techniques, Upstream Signaling Pathway