Dissection of Regulatory Networks that Are Altered in Disease via Differential Co-expression

Abstract

Comparing the gene-expression profiles of sick and healthy individuals can help in understanding disease. Such differential expression analysis is a well-established way to find gene sets whose expression is altered in the disease. Recent approaches to gene-expression analysis go a step further and seek differential co-expression patterns, wherein the level of co-expression of a set of genes differs markedly between disease and control samples. Such patterns can arise from a disease-related change in the regulatory mechanism governing that set of genes, and pinpoint dysfunctional regulatory networks.Here we present DICER, a new method for detecting differentially co-expressed gene sets using a novel probabilistic score for differential correlation. DICER goes beyond standard differential co-expression and detects pairs of modules showing differential co-expression. The expression profiles of genes within each module of the pair are correlated across all samples. The correlation between the two modules, however, differs markedly between the disease and normal samples.We show that DICER outperforms the state of the art in terms of significance and interpretability of the detected gene sets. Moreover, the gene sets discovered by DICER manifest regulation by disease-specific microRNA families. In a case study on Alzheimer's disease, DICER dissected biological processes and protein complexes into functional subunits that are differentially co-expressed, thereby revealing inner structures in disease regulatory networks.

DGCA: A comprehensive R package for Differential Gene Correlation Analysis

“…We sought to compare DGCA/MEGENA to two approaches to differential correlation module detection, DiffCoEx [44] and DICER [10]. For DiffCoEx, we downloaded the R script that the authors released in their Supplementary materials and used the same method and set of R commands they used therein.…”

“…Differential co-expression analysis can start with coexpressed gene modules or clusters based on the similarity of their gene expression in each condition using WGCNA [5] and MEGENA [6] and then computes module overlap statistics between conditions [7] or the average modular differential connectivity [8, 9]. Alternative approaches including DICER [10], DINGO [11], CoXpress [12], SDC [13], DiffCoEx [14], GSCA [15], and GSNCA [16] were developed to identify differential co-expression relationships between conditions and gene modules in each condition simultaneously.

Fig. 1An example demonstrating the theoretical difference between differential expression and differential correlation.

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Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis

“…Among the network analytical techniques that have recently been applied in biology, differential network (DN) analysis has shown robustness, which is evident in its ability to identify the DNA damage response genes in yeast (Bandyopadhyay et al, 2010;Califano, 2011), body weight-related genes in mice (Fuller et al, 2007;Gill et al, 2010), T cell differentiation-related genes in human (Elo et al, 2007), and human disease-relevant genes (Hudson et al, 2009;Amar et al, 2013). In contrast with DE analysis, which is a gene-centric analytic approach that assesses expression changes in individual genes, DN analysis is a networkcentric analytic approach that focuses on detecting the changes in a gene's associations with other genes via a comparison of two or more networks that were constructed under different experimental conditions (de la Fuente, 2010;Hudson et al, 2012;Ideker and Krogan, 2012).…”

Genetic variants in Alzheimer disease — molecular and brain network approaches

“…Traditional differential expression analysis might not detect cases of differential coexpression because gene–gene correlations can be altered without changes in the average expression level of the two genes 21 . Differential coexpression has been observed between several molecular systems when comparing control to AD gene expression patterns 92,95 and between PSEN1 and groups of genes highly expressed in oligodendrocyte and microglia, when comparing mouse and human coexpression patterns 96 . However, it is challenging to collect samples of ’true’ control-state or disease-state networks, as the phenotypic variability in AD is a continuum and AD pathology can be present long before the onset of clinical signs and symptoms 97 .…”