Document Detail


Causal Stability Ranking.
MedLine Citation:
PMID:  22945788     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Genotypic causes of a phenotypic trait are typically determined via randomized controlled intervention experiments. Such experiments are often prohibitive with respect to durations and costs, and informative prioritization of experiments is desirable. We therefore consider predicting stable rankings of genes (covariates), according to their total causal effects on a phenotype (response), from observational data. Since causal effects are generally non-identifiable from observational data only, we use a method which can infer lower bounds for the total causal effect under some assumptions. We validated our method, which we call Causal Stability Ranking (CStaR), in two situations. First, we performed knock-out experiments with Arabidopsis thaliana according to a predicted ranking based on observational gene expression data, using flowering time as phenotype of interest. Besides several known regulators of flowering time we found almost half of the tested top ranking mutants to have a significantly changed flowering time. Secondly, we compared CStaR to established regression-based methods on a gene expression data set of Saccharomyces cerevisiae. We found that CStaR outperforms these established methods. Our method allows for efficient design and prioritization of future intervention experiments, and due to its generality it can be used for a broad spectrum of applications.
Authors:
Daniel J Stekhoven; Izabel Moraes; Gardar Sveinbjörnsson; Lars Hennig; Marloes H Maathuis; Peter Bühlmann
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-9-3
Journal Detail:
Title:  Bioinformatics (Oxford, England)     Volume:  -     ISSN:  1367-4811     ISO Abbreviation:  Bioinformatics     Publication Date:  2012 Sep 
Date Detail:
Created Date:  2012-9-4     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9808944     Medline TA:  Bioinformatics     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Seminar for Statistics, Department of Mathematics, ETH Zurich, 8092 Zurich, Switzerland.
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