Dataset Information

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

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.

Availability and implementation

R code for aIDA is available in the Supplementary material.

Contact

franziska.taruttis@ur.de, julia.engelmann@ur.de.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Taruttis F

PROVIDER: S-EPMC4653390 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Publications

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

Taruttis Franziska F Spang Rainer R Engelmann Julia C JC

Bioinformatics (Oxford, England) 20150806 23

<h4>Motivation</h4>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, ...[more]

PMID: 26249813

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Dataset Information

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

Motivation

Results

Availability and implementation

Contact

Supplementary information

Publications

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

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