Gene regulatory network discovery using pairwise Granger causality

Abstract

Discovery of gene regulatory network from gene expression data can yield a useful insight to drug development. Among the methods applied to time-series data, Granger causality (GC) has emerged as a powerful tool with several merits. Since gene expression data usually have a much larger number of genes than time points therefore a full model cannot be applied in a straightforward manner, GC is often applied to genes pair wisely. In this study, the authors first investigate with synthetic data how spurious causalities (false discoveries) may arise because of the use of pairwise rather than full-model GC detection. Furthermore, spurious causalities may also arise if the order of the vector autoregressive model is not high enough. As a remedy, the authors demonstrate that model validation techniques can effectively reduce the number of false discoveries. Then, they apply pairwise GC with model validation to the real human HeLa cell-cycle dataset. They find that Akaike information criterion is generally most suitable for determining model order, but precaution should be taken for extremely short time series. With the authors proposed implementation, degree distributions and network hubs are obtained and compared with existing results, giving a new observation that the hubs tend to act as sources rather than receivers of interactions.

A roadmap for research on crassulacean acid metabolism (CAM) to enhance sustainable food and bioenergy production in a hotter, drier world

“…Dissecting the complexity of CAM regulation will be facilitated by the construction of protein–protein interaction and gene regulatory networks (GRNs), which are the collection of interactions between transcription factors and their target genes. The integration of complete genome sequences with RNA‐seq and chromatin immunoprecipitation sequencing (ChIP‐seq) experiments offers an exciting platform to dissect and model GRNs using computational approaches such as Bayesian inference, Boolean modeling, linear and nonlinear regression methods, Granger causality‐based inference, and cross‐correlation analysis (Wallach et al ., ; Marbach et al ., ; Middleton et al ., ; Krouk et al ., ; Moghaddam & Van den Ende, ; Tam et al ., ).…”

Elastic-Net Copula Granger Causality for Inference of Biological Networks

“…In the past, several methods were used to handle such high-dimensional data, such as pairwise analysis [18], kernel-based algorithms [11, 19], and other regularization-based methods [20, 21]. Another viable alternative, proposed by Nelsen [22], uses the copula to discover dependencies between random variables.…”

“…The second set of simulated data was first used by Schelter et al [39] and later in [18, 40, 41] and [42]. It simulates the scenario of five variables and uses the following set of equations: …”

“…We generated the desired quantities of data from these sets of equations, as is done by other authors [18, 37, 38, 40, 41] and [42], by first initializing the equations with white Gaussian noise having zero mean and unit variance; we then used the iterative process to generate the remainder of the data.…”

Meta-analysis on gene regulatory networks discovered by pairwise Granger causality

“…Gene regulatory network (GRN) discovery detected genegene interactions from gene expression data [1]- [11]. Genes identified to play roles in disease development are potential targets of future drugs [2]- [4].…”