Dataset Information

Correcting for link loss in causal network inference caused by regulator interference.

ABSTRACT:

Motivation

There are a number of algorithms to infer causal regulatory networks from time series (gene expression) data. Here we analyse the phenomena of regulator interference, where regulators with similar dynamics mutually suppress both the probability of regulating a target and the associated link strength; for instance, interference between two identical strong regulators reduces link probabilities by ?50%.

Results

We construct a robust method to define an interference-corrected causal network based on an analysis of the conditional link probabilities that recovers links lost through interference. On a large real network (Streptomyces coelicolor, phosphate depletion), we demonstrate that significant interference can occur between regulators with a correlation as low as 0.865, losing an estimated 34% of links by interference. However, levels of interference cannot be predicted from the correlation between regulators alone and are data specific. Validating against known networks, we show that high numbers of functional links are lost by regulator interference. Performance against other methods on DREAM4 data is excellent.

Availability and implementation

The method is implemented in R and is publicly available as the NIACS package at http://www2.warwick.ac.uk/fac/sci/systemsbiology/research/software.

SUBMITTER: Wang Y

PROVIDER: S-EPMC4173021 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Publications

Correcting for link loss in causal network inference caused by regulator interference.

Wang Ying Y Penfold Christopher A CA Hodgson David A DA Gifford Miriam L ML Burroughs Nigel J NJ

Bioinformatics (Oxford, England) 20140619 19

<h4>Motivation</h4>There are a number of algorithms to infer causal regulatory networks from time series (gene expression) data. Here we analyse the phenomena of regulator interference, where regulators with similar dynamics mutually suppress both the probability of regulating a target and the associated link strength; for instance, interference between two identical strong regulators reduces link probabilities by ∼50%.<h4>Results</h4>We construct a robust method to define an interference-correc ...[more]

PMID: 24947751

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Dataset Information

Correcting for link loss in causal network inference caused by regulator interference.

Motivation

Results

Availability and implementation

Publications

Correcting for link loss in causal network inference caused by regulator interference.

Similar Datasets

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