Motif-guided sparse decomposition of gene expression data for regulatory module identification

Background  

Genes work coordinately as gene modules or gene networks. Various computational approaches have been proposed to find gene modules based on gene expression data; for example, gene clustering is a popular method for grouping genes with similar gene expression patterns. However, traditional gene clustering often yields unsatisfactory results for regulatory module identification because the resulting gene clusters are co-expressed but not necessarily co-regulated.     

Modeling Genome-Wide Dynamic Regulatory Network in Mouse Lungs with Influenza Infection Using High-Dimensional Ordinary Differential Equations

The immune response to viral infection is regulated by an intricate network of many genes and their products. The reverse engineering of gene regulatory networks (GRNs) using mathematical models from time course gene expression data collected after influenza infection is key to our understanding of the mechanisms involved in controlling influenza infection within a host. A five-step pipeline: detection of temporally differentially expressed genes, clustering genes into co-expressed modules, identification of network structure, parameter estimate refinement, and functional enrichment analysis, is developed for reconstructing high-dimensional dynamic GRNs from genome-wide time course gene expression data. Applying the pipeline to the time course gene expression data from influenza-infected mouse lungs, we have identified 20 distinct temporal expression patterns in the differentially expressed genes and constructed a module-based dynamic network using a linear ODE model. Both intra-module and inter-module annotations and regulatory relationships of our inferred network show some interesting findings and are highly consistent with existing knowledge about the immune response in mice after influenza infection. The proposed method is a computationally efficient, data-driven pipeline bridging experimental data, mathematical modeling, and statistical analysis. The application to the influenza infection data elucidates the potentials of our pipeline in providing valuable insights into systematic modeling of complicated biological processes.     

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

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.     

Cis-regulatory properties of medaka synexpression groups

During embryogenesis, tissue specification is triggered by the expression of a unique combination of developmental genes and their expression in time and space is crucial for successful development. Synexpression groups are batteries of spatiotemporally co-expressed genes that act in shared biological processes through their coordinated expression. Although several synexpression groups have been described in numerous vertebrate species, the regulatory mechanisms that orchestrate their common complex expression pattern remain to be elucidated. Here we performed a pilot screen on 560 genes of the vertebrate model system medaka (Oryzias latipes) to systematically identify synexpression groups and investigate their regulatory properties by searching for common regulatory cues. We find that synexpression groups share DNA motifs that are arranged in various combinations into cis-regulatory modules that drive co-expression. In contrast to previous assumptions that these genes are located randomly in the genome, we discovered that genes belonging to the same synexpression group frequently occur in synexpression clusters in the genome. This work presents a first repertoire of synexpression group common signatures, a resource that will contribute to deciphering developmental gene regulatory networks.     

一种融合表达谱相关性信息的激活子网辨识算法

传统表达谱数据分析方法集中于寻找差异表达基因和共表达基因集合,没有考虑基因表达产物之间已知的相互作用.近年来在系统生物学的研究中发展了将基因表达谱与蛋白质相互作用网络进行整合分析的方法.现有方法未能综合考虑基因表达差异性和相关性信息,容易导致辨识结果中重要功能分子缺失且生物学功能相关度不高.提出一种融合表达谱差异性和相关性信息的激活子网辨识算法,能够在蛋白质相互作用网络中辨识高功能相关度的激活子网.应用到人免疫缺陷病毒HIV-1感染过程的研究,结果表明,该算法可以有效避免仅考虑基因表达差异性所引入的偏差,揭示了高相关性低表达差异基因在相关通路中的关键性作用.     

CoXpress: differential co-expression in gene expression data

Background  

Traditional methods of analysing gene expression data often include a statistical test to find differentially expressed genes, or use of a clustering algorithm to find groups of genes that behave similarly across a dataset. However, these methods may miss groups of genes which form differential co-expression patterns under different subsets of experimental conditions. Here we describe coXpress, an R package that allows researchers to identify groups of genes that are differentially co-expressed.