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

SUBMITTER: Wang Y 

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

REPOSITORIES: biostudies-literature