Nested effects models for high-dimensional phenotyping screens
1Lewis-Sigler Institute for Integrative Genomics and Department of Computer Science, Princeton University, Princeton, NJ, 08544, USA,2Max Planck Institute for Molecular Genetics, Ihnestraße 63-73, 14195 Berlin and3Institute for Functional Genomics, Computational Diagnostics Group, University of Regensburg, Josef Engertstr. 9, 93503 Regensburg, Germany
*To whom correspondence should be addressed.
 |
Abstract |
|---|
Motivation: In high-dimensional phenotyping screens, a large number of cellular features is observed after perturbing genes by knockouts or RNA interference. Comprehensive analysis of perturbation effects is one of the most powerful techniques for attributing functions to genes, but not much work has been done so far to adapt statistical and computational methodology to the specific needs of large-scale and high-dimensional phenotyping screens.
Results: We introduce and compare probabilistic methods to efficiently infer a genetic hierarchy from the nested structure of observed perturbation effects. These hierarchies elucidate the structures of signaling pathways and regulatory networks. Our methods achieve two goals: (1) they reveal clusters of genes with highly similar phenotypic profiles, and (2) they order (clusters of) genes according to subset relationships between phenotypes. We evaluate our algorithms in the controlled setting of simulation studies and show their practical use in two experimental scenarios: (1) a data set investigating the response to microbial challenge in Drosophila melanogaster, and (2) a compendium of expression profiles of Saccharomyces cerevisiae knockout strains. We show that our methods identify biologically justified genetic hierarchies of perturbation effects.
Availability: The software used in our analysis is freely available in the R package ‘nem’ from www.bioconductor.org
Contact: ogt{at}cs.princeton.edu
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  L. Kaderali, E. Dazert, U. Zeuge, M. Frese, and R. Bartenschlager Reconstructing signaling pathways from RNAi data using probabilistic Boolean threshold networks Bioinformatics, September 1, 2009; 25(17): 2229 - 2235. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
H. Frohlich, M. Fellmann, H. Sultmann, A. Poustka, and T. Beissbarth Estimating large-scale signaling networks through nested effect models with intervention effects from microarray data Bioinformatics, November 15, 2008; 24(22): 2650 - 2656. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
H. Frohlich, T. Beissbarth, A. Tresch, D. Kostka, J. Jacob, R. Spang, and F. Markowetz Analyzing gene perturbation screens with nested effects models in R and bioconductor Bioinformatics, November 1, 2008; 24(21): 2549 - 2550. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Jacob, M. Jentsch, D. Kostka, S. Bentink, and R. Spang Detecting hierarchical structure in molecular characteristics of disease using transitive approximations of directed graphs Bioinformatics, April 1, 2008; 24(7): 995 - 1001. [Abstract] [Full Text] [PDF]
|
|