Journal Article

Analyzing gene perturbation screens with nested effects models in R and bioconductor

Holger Fröhlich, Tim Beißbarth, Achim Tresch, Dennis Kostka, Juby Jacob, Rainer Spang and F. Markowetz

in Bioinformatics

Volume 24, issue 21, pages 2549-2550
Published in print November 2008 | ISSN: 1367-4803
Published online August 2008 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btn446
Analyzing gene perturbation screens with nested effects models in R and bioconductor

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Summary: Nested effects models (NEMs) are a class of probabilistic models introduced to analyze the effects of gene perturbation screens visible in high-dimensional phenotypes like microarrays or cell morphology. NEMs reverse engineer upstream/downstream relations of cellular signaling cascades. NEMs take as input a set of candidate pathway genes and phenotypic profiles of perturbing these genes. NEMs return a pathway structure explaining the observed perturbation effects. Here, we describe the package nem, an open-source software to efficiently infer NEMs from data. Our software implements several search algorithms for model fitting and is applicable to a wide range of different data types and representations. The methods we present summarize the current state-of-the-art in NEMs.

Availability: Our software is written in the R language and freely avail-able via the Bioconductor project at http://www.bioconductor.org.

Contact: rainer.spang@klinik.uni-regensburg.de

Journal Article.  1145 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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