Comparative analysis of module-based versus direct methods for reverse-engineering transcriptional regulatory networks

TL;DR

This study compares module-based and direct mutual information methods for reverse-engineering transcriptional networks, revealing their distinct strengths and limitations through biological validation and network topology analysis.

Contribution

It provides a detailed comparison of LeMoNe and CLR algorithms, highlighting their different topological and biological inference capabilities.

Findings

CLR is regulator-centric, predicting more regulators.

LeMoNe is target-centric, recovering more known targets.

Both methods complement each other in network inference.

Abstract

We have compared a recently developed module-based algorithm LeMoNe for reverse-engineering transcriptional regulatory networks to a mutual information based direct algorithm CLR, using benchmark expression data and databases of known transcriptional regulatory interactions for Escherichia coli and Saccharomyces cerevisiae. A global comparison using recall versus precision curves hides the topologically distinct nature of the inferred networks and is not informative about the specific subtasks for which each method is most suited. Analysis of the degree distributions and a regulator specific comparison show that CLR is 'regulator-centric', making true predictions for a higher number of regulators, while LeMoNe is 'target-centric', recovering a higher number of known targets for fewer regulators, with limited overlap in the predicted interactions between both methods. Detailed biological examples in E. coli and S. cerevisiae are used to illustrate these differences and to prove that each method is able to infer parts of the network where the other fails. Biological validation of the inferred networks cautions against over-interpreting recall and precision values computed using incomplete reference networks.