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

@article{citeulike:1772950, abstract = {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 genetic 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 importantly, we propose methods to scale up the original approach, which is limited to around 5 genes, to large scale networks. CONCLUSIONS:Comparisons of these methods on artificial data are conducted. Our proposed module network is employed to infer the signaling network between 13 genes in the ER-alpha pathway in human MCF-7 breast cancer cells. Using a bootstrapping approach this reconstruction can be found with good statistical stability. The code for the module network inference method is available in the latest version of the R-package - nem, which can be obtained from the Bioconductor homepage.}, author = {Froehlich, Holger and Fellmann, Mark and Sueltmann, Holger and Poustka, Annemarie and Beissbarth, Tim }, citeulike-article-id = {1772950}, doi = {http://dx.doi.org/10.1186/1471-2105-8-386}, journal = {BMC Bioinformatics}, keywords = {genetic\_regulatory\_network, microarray, r}, number = {1}, posted-at = {2007-10-16 08:48:04}, priority = {4}, title = {Large scale statistical inference of signaling pathways from RNAi and microarray data}, url = {http://dx.doi.org/10.1186/1471-2105-8-386}, volume = {8}, year = {2007} }

TY - JOUR ID - citeulike:1772950 L3 - citeulike-article-id:1772950 TI - Large scale statistical inference of signaling pathways from RNAi and microarray data JF - BMC Bioinformatics VL - 8 IS - 1 N2 - 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 genetic 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 importantly, we propose methods to scale up the original approach, which is limited to around 5 genes, to large scale networks. CONCLUSIONS:Comparisons of these methods on artificial data are conducted. Our proposed module network is employed to infer the signaling network between 13 genes in the ER-alpha pathway in human MCF-7 breast cancer cells. Using a bootstrapping approach this reconstruction can be found with good statistical stability. The code for the module network inference method is available in the latest version of the R-package - nem, which can be obtained from the Bioconductor homepage. KW - genetic_regulatory_network KW - microarray KW - r AU - Froehlich, Holger AU - Fellmann, Mark AU - Sueltmann, Holger AU - Poustka, Annemarie AU - Beissbarth, Tim PY - 2007/// UR - http://dx.doi.org/10.1186/1471-2105-8-386 ER -