Comparing Statistical Methods for Constructing Large Scale Gene Networks

By Jeffrey D. Allen, Yang Xie, Min Chen, Luc Girard, Guanghua Xiao. 2012

203 citations recorded by the source corpus.

The gene regulatory network (GRN) reveals the regulatory relationships among genes and can provide a systematic understanding of molecular mechanisms underlying biological processes. The importance of computer simulations in understanding cellular processes is now widely accepted; a variety of algorithms have been developed to study these biological networks. The goal of this study is to provide a comprehensive evaluation and a practical guide to aid in choosing statistical methods for constructing large scale GRNs. Using both simulation studies and a real application in E. coli data, we compare different methods in terms of sensitivity and specificity in identifying the true connections and the hub genes, the ease of use, and computational speed. Our results show that these algorithms performed reasonably well, and each method has its own advantages: (1) GeneNet, WGCNA (Weighted Correlation Network Analysis), and ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) performed well in constructing the global network structure; (2) GeneNet and SPACE (Sparse PArtial Correlation Estimation) performed well in identifying a few connections with high specificity.

Subfields: Molecular Biology

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Citations in the corpus

Aging effects on DNA methylation modules in human brain and blood tissue (2012, methodology): “Studies of our group [42,45-48] and of independent groups [49] provide empirical evidence that the topological overlap measure is a biologically meaningful network similarity measure.”
From big data analysis to personalized medicine for all: challenges and opportunities (2015, background): “It is however, a computationally demanding approach that requires advanced computing methods such as parallel computing and acceleration via GPUs [106].”
Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015, background): “At currently available sample sizes, networks are most reproducible at a module level 35,38,39 , then at the hub gene level 41,223 and, last, at the level of precise gene connectivity rankings or precise module memberships of genes 40,49 .”
Comparison of co-expression measures: mutual information, correlation, and model based indices (2012, methodology): “While association networks such as WGCNA or ARACNE have been been successfully used for gene regulatory networks (GRNs) [13], a host of alternatives are available.”