TRANSWESD: inferring cellular networks with transitive reduction

Abstract

Motivation: Distinguishing direct from indirect influences is a central issue in reverse engineering of biological networks because it facilitates detection and removal of false positive edges. Transitive reduction is one approach for eliminating edges reflecting indirect effects but its use in reconstructing cyclic interaction graphs with true redundant structures is problematic.Results: We present TRANSWESD, an elaborated variant of TRANSitive reduction for WEighted Signed Digraphs that overcomes conceptual problems of existing versions. Major changes and improvements concern: (i) new statistical approaches for generating high-quality perturbation graphs from systematic perturbation experiments; (ii) the use of edge weights (association strengths) for recognizing true redundant structures; (iii) causal interpretation of cycles; (iv) relaxed definition of transitive reduction; and (v) approximation algorithms for large networks. Using standardized benchmark tests, we demonstrate that our method outperforms existing variants of transitive reduction and is, despite its conceptual simplicity, highly competitive with other reverse engineering methods.Contact: klamt@mpi-magdeburg.mpg.deSupplementary information: Supplementary data are available at Bioinformatics online.

Efficient reconstruction of biological networks via transitive reduction on general purpose graphics processors

“…However, in the network inference algorithms interaction strengths between genes usually play an important role. Motivated by this fact, the concept of TR of weighted graphs was introduced [4,8], where the weights correspond to interaction strengths. Both these papers take interaction signs (promoting or inhibiting) into account.…”

“…Unlike the previous work in [4,8], we adopt an approach that disregards the interaction signs. A benefit of this decision is that the TR algorithms are of polynomial complexity and amenable to parallelisation.…”

“…A benefit of this decision is that the TR algorithms are of polynomial complexity and amenable to parallelisation. In contrast, the algorithm in [8] is in the worst case NP-complete, which means that its runtime grows exponentially with the size of the networks. Tests with the Dream challenges [10] show that the omission of signs does not incur any degradation in overall performance compared to the signed weighted TR of [8].…”

“…For a similar reason, our algorithm for cyclic weighted graphs cannot be adapted to cyclic signed weighted graphs. The problems with cycles in signed graphs are discussed in [8]. …”

Algorithmic Perspectives of Network Transitive Reduction Problems and their Applications to Synthesis and Analysis of Biological Networks

“…The idea of transitive reductions, in a more simplistic setting or in a different form, has also been used to identify structure of gene regulatory networks [48,49,50,51,52]. Of particular interest is a network “deconvolution” problem, considered by Feizi et al .…”