NEMix: single-cell nested effects models for probabilistic pathway stimulation

Siebourg-Polster, Juliane and Mudrak, Daria and Emmenlauer, Mario and Rämö, Pauli and Dehio, Christoph and Greber, Urs and Fröhlich, Holger and Beerenwinkel, Niko. (2015) NEMix: single-cell nested effects models for probabilistic pathway stimulation. PLoS Computational Biology, 11 (4). e1004078.

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Nested effects models have been used successfully for learning subcellular networks from high-dimensional perturbation effects that result from RNA interference (RNAi) experiments. Here, we further develop the basic nested effects model using high-content single-cell imaging data from RNAi screens of cultured cells infected with human rhinovirus. RNAi screens with single-cell readouts are becoming increasingly common, and they often reveal high cell-to-cell variation. As a consequence of this cellular heterogeneity, knock-downs result in variable effects among cells and lead to weak average phenotypes on the cell population level. To address this confounding factor in network inference, we explicitly model the stimulation status of a signaling pathway in individual cells. We extend the framework of nested effects models to probabilistic combinatorial knock-downs and propose NEMix, a nested effects mixture model that accounts for unobserved pathway activation. We analyzed the identifiability of NEMix and developed a parameter inference scheme based on the Expectation Maximization algorithm. In an extensive simulation study, we show that NEMix improves learning of pathway structures over classical NEMs significantly in the presence of hidden pathway stimulation. We applied our model to single-cell imaging data from RNAi screens monitoring human rhinovirus infection, where limited infection efficiency of the assay results in uncertain pathway stimulation. Using a subset of genes with known interactions, we show that the inferred NEMix network has high accuracy and outperforms the classical nested effects model without hidden pathway activity. NEMix is implemented as part of the R/Bioconductor package 'nem' and available at www.cbg.ethz.ch/software/NEMix.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Infection Biology > Molecular Microbiology (Dehio)
UniBasel Contributors:Dehio, Christoph
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Library of Science
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:15 Nov 2017 09:47
Deposited On:04 May 2016 08:22

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