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

Bioinformatics. 2008 Nov 1;24(21):2549-50. doi: 10.1093/bioinformatics/btn446. Epub 2008 Aug 21.

Abstract

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Gene Expression
  • Gene Expression Profiling / methods*
  • Models, Statistical
  • Oligonucleotide Array Sequence Analysis
  • Software*
  • User-Computer Interface