A statistical approach to virtual cellular experiments: improved causal discovery using accumulation IDA (aIDA)

Taruttis, Franziska and Spang, Rainer and Engelmann, Julia C. (2015) A statistical approach to virtual cellular experiments: improved causal discovery using accumulation IDA (aIDA). BIOINFORMATICS, 31 (23). pp. 3807-3814. ISSN 1367-4803, 1460-2059

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Abstract

Motivation: We address the following question: Does inhibition of the expression of a gene X in a cellular assay affect the expression of another gene Y? Rather than inhibiting gene X experimentally, we aim at answering this question computationally using as the only input observational gene expression data. Recently, a new statistical algorithm called Intervention calculus when the Directed acyclic graph is Absent (IDA), has been proposed for this problem. For several biological systems, IDA has been shown to outcompete regression-based methods with respect to the number of true positives versus the number of false positives for the top 5000 predicted effects. Further improvements in the performance of IDA have been realized by stability selection, a resampling method wrapped around IDA that enhances the discovery of true causal effects. Nevertheless, the rate of false positive and false negative predictions is still unsatisfactorily high. Results: We introduce a new resampling approach for causal discovery called accumulation IDA (aIDA). We show that aIDA improves the performance of causal discoveries compared to existing variants of IDA on both simulated and real yeast data. The higher reliability of top causal effect predictions achieved by aIDA promises to increase the rate of success of wet lab intervention experiments for functional studies.

Item Type: Article
Uncontrolled Keywords: SACCHAROMYCES-CEREVISIAE; EQUIVALENCE CLASSES; OBSERVATIONAL DATA; INTERFERENCE; EXPRESSION; INFERENCE; ALGORITHM; SELECTION; NETWORK;
Subjects: 600 Technology > 610 Medical sciences Medicine
Divisions: Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang)
Depositing User: Dr. Gernot Deinzer
Date Deposited: 06 May 2019 07:05
Last Modified: 06 May 2019 07:05
URI: https://pred.uni-regensburg.de/id/eprint/4388

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