Structural influence of gene networks on their inference: analysis of C3NET

Abstract

BackgroundThe availability of large-scale high-throughput data possesses considerable challenges toward their functional analysis. For this reason gene network inference methods gained considerable interest. However, our current knowledge, especially about the influence of the structure of a gene network on its inference, is limited.ResultsIn this paper we present a comprehensive investigation of the structural influence of gene networks on the inferential characteristics of C3NET - a recently introduced gene network inference algorithm. We employ local as well as global performance metrics in combination with an ensemble approach. The results from our numerical study for various biological and synthetic network structures and simulation conditions, also comparing C3NET with other inference algorithms, lead a multitude of theoretical and practical insights into the working behavior of C3NET. In addition, in order to facilitate the practical usage of C3NET we provide an user-friendly R package, called c3net, and describe its functionality. It is available from https://r-forge.r-project.org/projects/c3net and from the CRAN package repository.ConclusionsThe availability of gene network inference algorithms with known inferential properties opens a new era of large-scale screening experiments that could be equally beneficial for basic biological and biomedical research with auspicious prospects. The availability of our easy to use software package c3net may contribute to the popularization of such methods.ReviewersThis article was reviewed by Lev Klebanov, Joel Bader and Yuriy Gusev.

Global assessment of network inference algorithms based on available literature of gene/protein interactions

“…We selected mutual information based on popular GNI methods, as they are able to predict networks from very high-throughput datasets, and their applications are available in R software. These methods are CLR (Context Likelihood of Relatedness; Faith et al, 2007), Relevance Network (RELNET) (Butte et al, 2000), ARACNE , MRNET (Meyer et al, 2007), and C3NET (Altay and Emmert-Streib, 2010a), which are widely cited in the literature and are available to use in the R platform (Meyer et al, 2008;Altay and Emmert-Streib, 2011). We refer interested readers to the work of Altay andEmmert-Streib (2010a, 2010b) for an overview of these methods.…”

Functional and genetic analysis of the colon cancer network

“…Briefly, BC3Net is a bagging version of C3Net [23,24] that generates from one dataset, D , an ensemble of B independent bootstrap datasets, ${\left\{D_k^b\right\}}_{kMathClass-rel=1}^B$, by sampling from D with replacement by using a non-parametric bootstrap with B = 100. Then, for each generated data set $D_k^b$ in the ensemble, a network $G_k^b$ is inferred by using C3Net [23,24].…”

“…Then, for each generated data set $D_k^b$ in the ensemble, a network $G_k^b$ is inferred by using C3Net [23,24]. From the ensemble of networks ${\left\{G_k^b\right\}}_{kMathClass-rel=1}^B$ we construct one aggregate network, $G_w^b$, which is used to determine the statistical significance of the connection between gene pairs.…”

Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data

“…As indicated by its name, SA-CLR is based on CLR but includes synergistic effects between genes (Anastassiou, 2007; Watkinson et al, 2009). Synergy,…”