1 research outputs found
Consensus Network Inference of Microarray Gene Expression Data
Genetic and protein interactions are essential to regulate cellular machinery. Their
identification has become an important aim of systems biology research. In recent years, a
variety of computational network inference algorithms have been employed to reconstruct
gene regulatory networks from post-genomic data. However, precisely predicting these
regulatory networks remains a challenge.
We began our study by assessing the ability of various network inference algorithms
to accurately predict gene regulatory interactions using benchmark simulated datasets. It was
observed from our analysis that different algorithms have strengths and weaknesses when
identifying regulatory networks, with a gene-pair interaction (edge) predicted by one
algorithm not always necessarily consistent with the other. An edge not predicted by most
inference algorithms may be an important one, and should not be missed. The naïve
consensus (intersection) method is perhaps the most conservative approach and can be used
to address this concern by extracting the edges consistently predicted across all inference
algorithms; however, it lacks credibility as it does not provide a quantifiable measure for
edge weights. Existing quantitative consensus approaches, such as the inverse-variance
weighted method (IVWM) and the Borda count election method (BCEM), have been
previously implemented to derive consensus networks from diverse datasets. However, the
former method was biased towards finding local solutions in the whole network, and the
latter considered species diversity to build the consensus network.
In this thesis we proposed a novel consensus approach, in which we used Fishers
Combined Probability Test (FCPT) to combine the statistical significance values assigned to
each network edge by a number of different networking algorithms to produce a consensus
network. We tested our method by applying it to a variety of in silico benchmark expression datasets of different dimensions and evaluated its performance against individual inference
methods, Bayesian models and also existing qualitative and quantitative consensus
techniques. We also applied our approach to real experimental data from the yeast (S.
cerevisiae) network as this network has been comprehensively elucidated previously. Our
results demonstrated that the FCPT-based consensus method outperforms single algorithms in
terms of robustness and accuracy. In developing the consensus approach, we also proposed a
scoring technique that quantifies biologically meaningful hierarchical modular networks.University of Exeter studentshi