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


BACKGROUND
Our software is written in the R language and freely avail-able via the Bioconductor project at http://www.bioconductor.org.
Fröhlich
Holger
H
German Cancer Research Center, INF 580, 69120 Heidelberg, Germany.
Beissbarth
Tim
T
Tresch
Achim
A
Kostka
Dennis
D
Jacob
Juby
J
Spang
Rainer
R
Markowetz
F
F
D000465:Algorithms; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D012984:Software; D014584:User-Computer Interface