Inferring Co-expression Networks Research Articles

Higher-order correction of persistent batch effects in correlation networks.

Systems biology analyses often use correlations in gene expression profiles to infer co-expression networks that are then used as input for gene regulatory network inference or to identify functional modules of co-expressed or putatively co-regulated genes. While systematic biases, including batch effects, are known to induce spurious associations and confound differential gene expression analyses (DE), the impact of batch effects on gene co-expression has not been fully explored. Methods have been developed to adjust expression values, ensuring conditional independence of mean and variance from batch or other covariates for each gene, resulting in improved fidelity of DE analysis. However, such adjustments do not address the potential for spurious differential co-expression (DC) between groups. Consequently, uncorrected, artifactual DC can skew the correlation structure, leading to the identification of false, non-biological associations, even when the input data are corrected using standard batch correction. In this work, we demonstrate the persistence of confounders in covariance after standard batch correction using synthetic and real-world gene expression data examples. We then introduce Co-expression Batch Reduction Adjustment (COBRA), a method for computing a batch-corrected gene co-expression matrix based on estimating a conditional covariance matrix. COBRA estimates a reduced set of parameters expressing the co-expression matrix as a function of the sample covariates, allowing control for continuous and categorical covariates. COBRA is computationally efficient, leveraging the inherently modular structure of genomic data to estimate accurate gene regulatory associations and facilitate functional analysis for high-dimensional genomic data. COBRA is available under the GLP3 open source license in R and Python in netZoo (https://netzoo.github.io).

Bayesian inference of sample-specific coexpression networks.

Gene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring coexpression networks is a critical element of GRN inference, as the correlation between expression patterns may indicate that genes are coregulated by common factors. However, methods that estimate coexpression networks generally derive an aggregate network representing the mean regulatory properties of the population and so fail to fully capture population heterogeneity. Bayesian optimized networks obtained by assimilating omic data (BONOBO) is a scalable Bayesian model for deriving individual sample-specific coexpression matrices that recognizes variations in molecular interactions across individuals. For each sample, BONOBO assumes a Gaussian distribution on the log-transformed centered gene expression and a conjugate prior distribution on the sample-specific coexpression matrix constructed from all other samples in the data. Combining the sample-specific gene coexpression with the prior distribution, BONOBO yields a closed-form solution for the posterior distribution of the sample-specific coexpression matrices, thus allowing the analysis of large data sets. We demonstrate BONOBO's utility in several contexts, including analyzing gene regulation in yeast transcription factor knockout studies, the prognostic significance of miRNA-mRNA interaction in human breast cancer subtypes, and sex differences in gene regulation within human thyroid tissue. We find that BONOBO outperforms other methods that have been used for sample-specific coexpression network inference and provides insight into individual differences in the drivers of biological processes.

Inferring co-expression networks of Arabidopsis thaliana genes during their interaction with Trichoderma spp.

Fungi of the Trichoderma genus are called "biostimulants" because they promote plant growth and development and induce disease resistance. We used conventional transcriptome and gene co-expression analyses to understand the molecular response of the plant Arabidopsis thaliana to inoculation with Trichoderma atroviride or Trichoderma virens. The transcriptional landscape of the plant during the interaction with these fungi showed a reduction in functions such as reactive oxygen species production, defense mechanisms against pathogens, and hormone signaling. T. virens, as opposed to T. atroviride, was more effective at downregulating genes related to terpenoid metabolism, root development, and chemical homeostasis. Through gene co-expression analysis, we found functional gene modules that closely link plant defense with hypoxia. Notably, we found a transcription factor (locus AT2G47520) with two functional domains of interest: a DNA-binding domain and an N-terminal cysteine needed for protein stability under hypoxia. We hypothesize that the transcription factor can bind to the promoter sequence of the GCC-box that is connected to pathogenesis by positioned weight matrix analysis.

Improving gene regulatory network inference and assessment: The importance of using network structure.

Gene regulatory networks are graph models representing cellular transcription events. Networks are far from complete due to time and resource consumption for experimental validation and curation of the interactions. Previous assessments have shown the modest performance of the available network inference methods based on gene expression data. Here, we study several caveats on the inference of regulatory networks and methods assessment through the quality of the input data and gold standard, and the assessment approach with a focus on the global structure of the network. We used synthetic and biological data for the predictions and experimentally-validated biological networks as the gold standard (ground truth). Standard performance metrics and graph structural properties suggest that methods inferring co-expression networks should no longer be assessed equally with those inferring regulatory interactions. While methods inferring regulatory interactions perform better in global regulatory network inference than co-expression-based methods, the latter is better suited to infer function-specific regulons and co-regulation networks. When merging expression data, the size increase should outweigh the noise inclusion and graph structure should be considered when integrating the inferences. We conclude with guidelines to take advantage of inference methods and their assessment based on the applications and available expression datasets.

SwCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution.

Complex biological tissues are often a heterogeneous mixture of several molecularly distinct cell subtypes. Both subtype compositions and subtype-specific (STS) expressions can vary across biological conditions. Computational deconvolution aims to dissect patterns of bulk tissue data into subtype compositions and STS expressions. Existing deconvolution methods can only estimate averaged STS expressions in a population, while many downstream analyses such as inferring co-expression networks in particular subtypes require subtype expression estimates in individual samples. However, individual-level deconvolution is a mathematically underdetermined problem because there are more variables than observations. We report a sample-wise Convex Analysis of Mixtures (swCAM) method that can estimate subtype proportions and STS expressions in individual samples from bulk tissue transcriptomes. We extend our previous CAM framework to include a new term accounting for between-sample variations and formulate swCAM as a nuclear-norm and ℓ2,1-norm regularized matrix factorization problem. We determine hyperparameter values using cross-validation with random entry exclusion and obtain a swCAM solution using an efficient alternating direction method of multipliers. Experimental results on realistic simulation data show that swCAM can accurately estimate STS expressions in individual samples and successfully extract co-expression networks in particular subtypes that are otherwise unobtainable using bulk data. In two real-world applications, swCAM analysis of bulk RNASeq data from brain tissue of cases and controls with bipolar disorder or Alzheimer's disease identified significant changes in cell proportion, expression pattern and co-expression module in patient neurons. Comparative evaluation of swCAM versus peer methods is also provided. The R Scripts of swCAM are freely available at https://github.com/Lululuella/swCAM. A user's guide and a vignette are provided. Supplementary data are available at Bioinformatics online.

A network-based computational framework to predict and differentiate functions for gene isoforms using exon-level expression data.

Alternative splicing makes significant contributions to functional diversity of transcripts and proteins. Many alternatively spliced gene isoforms have been shown to perform specific biological functions under different contexts. In addition to gene-level expression, the advances of high-throughput sequencing offer a chance to estimate isoform-specific exon expression with a high resolution, which is informative for studying splice variants with network analysis. In this study, we propose a novel network-based analysis framework to predict isoform-specific functions from exon-level RNA-Seq data. In particular, based on exon-level expression data, we firstly propose a unified framework, referred to as Iso-Net, to integrate two new mathematical methods (named MINet and RVNet) that infer co-expression networks at different data scenarios. We demonstrate the superior prediction accuracy of Iso-Net over the existing methods for most simulation data, especially in two extreme cases: sample size is very small and exon numbers of two isoforms are quite different. Furthermore, by defining relevant quantitative measures (e.g., Jaccard correlation coefficient) and combining differential co-expression network analysis and GO functional enrichment analysis, a co-expression network analysis framework is developed to predict functions of isoforms and further, to discover their distinct functions within the same gene. We apply Iso-Net to study gene isoforms for several important transcription factors in human myeloid differentiation with the exon-level RNA-Seq data from three different cell lines. Iso-Net is open source and freely available from https://github.com/Dingjie-Wang/Iso-Net.

Maize transposable elements contribute to long non-coding RNAs that are regulatory hubs for abiotic stress response

BackgroundSeveral studies have mined short-read RNA sequencing datasets to identify long non-coding RNAs (lncRNAs), and others have focused on the function of individual lncRNAs in abiotic stress response. However, our understanding of the complement, function and origin of lncRNAs – and especially transposon derived lncRNAs (TE-lncRNAs) - in response to abiotic stress is still in its infancy.ResultsWe utilized a dataset of 127 RNA sequencing samples that included total RNA datasets and PacBio fl-cDNA data to discover lncRNAs in maize. Overall, we identified 23,309 candidate lncRNAs from polyA+ and total RNA samples, with a strong discovery bias within total RNA. The majority (65%) of the 23,309 lncRNAs had sequence similarity to transposable elements (TEs). Most had similarity to long-terminal-repeat retrotransposons from the Copia and Gypsy superfamilies, reflecting a high proportion of these elements in the genome. However, DNA transposons were enriched for lncRNAs relative to their genomic representation by ~ 2-fold. By assessing the fraction of lncRNAs that respond to abiotic stresses like heat, cold, salt and drought, we identified 1077 differentially expressed lncRNA transcripts, including 509 TE-lncRNAs. In general, the expression of these lncRNAs was significantly correlated with their nearest gene. By inferring co-expression networks across our large dataset, we found that 39 lncRNAs are as major hubs in co-expression networks that respond to abiotic stress, and 18 appear to be derived from TEs.ConclusionsOur results show that lncRNAs are enriched in total RNA samples, that most (65%) are derived from TEs, that at least 1077 are differentially expressed during abiotic stress, and that 39 are hubs in co-expression networks, including a small number that are evolutionary conserved. These results suggest that lncRNAs, including TE-lncRNAs, may play key regulatory roles in moderating abiotic responses.

Contribution of Gene Regulatory Networks to Heritability of Coronary Artery Disease

BackgroundGenetic variants currently known to affect coronary artery disease (CAD) risk explain less than one-quarter of disease heritability. The heritability contribution of gene regulatory networks (GRNs) in CAD, which are modulated by both genetic and environmental factors, is unknown. ObjectivesThis study sought to determine the heritability contributions of single nucleotide polymorphisms affecting gene expression (eSNPs) in GRNs causally linked to CAD. MethodsSeven vascular and metabolic tissues collected in 2 independent genetics-of-gene-expression studies of patients with CAD were used to identify eSNPs and to infer coexpression networks. To construct GRNs with causal relations to CAD, the prior information of eSNPs in the coexpression networks was used in a Bayesian algorithm. Narrow-sense CAD heritability conferred by the GRNs was calculated from individual-level genotype data from 9 European genome-wide association studies (GWAS) (13,612 cases, 13,758 control cases). ResultsThe authors identified and replicated 28 independent GRNs active in CAD. The genetic variation in these networks contributed to 10.0% of CAD heritability beyond the 22% attributable to risk loci identified by GWAS. GRNs in the atherosclerotic arterial wall (n = 7) and subcutaneous or visceral abdominal fat (n = 9) were most strongly implicated, jointly explaining 8.2% of CAD heritability. In all, these 28 GRNs (each contributing to >0.2% of CAD heritability) comprised 24 to 841 genes, whereof 1 to 28 genes had strong regulatory effects (key disease drivers) and harbored many relevant functions previously associated with CAD. The gene activity in these 28 GRNs also displayed strong associations with genetic and phenotypic cardiometabolic disease variations both in humans and mice, indicative of their pivotal roles as mediators of gene–environmental interactions in CAD. ConclusionsGRNs capture a major portion of genetic variance and contribute to heritability beyond that of genetic loci currently known to affect CAD risk. These networks provide a framework to identify novel risk genes/pathways and study molecular interactions within and across disease-relevant tissues leading to CAD.

A new method for constructing tumor specific gene co-expression networks based on samples with tumor purity heterogeneity.

MotivationTumor tissue samples often contain an unknown fraction of stromal cells. This problem is widely known as tumor purity heterogeneity (TPH) was recently recognized as a severe issue in omics studies. Specifically, if TPH is ignored when inferring co-expression networks, edges are likely to be estimated among genes with mean shift between non-tumor- and tumor cells rather than among gene pairs interacting with each other in tumor cells. To address this issue, we propose Tumor Specific Net (TSNet), a new method which constructs tumor-cell specific gene/protein co-expression networks based on gene/protein expression profiles of tumor tissues. TSNet treats the observed expression profile as a mixture of expressions from different cell types and explicitly models tumor purity percentage in each tumor sample.ResultsUsing extensive synthetic data experiments, we demonstrate that TSNet outperforms a standard graphical model which does not account for TPH. We then apply TSNet to estimate tumor specific gene co-expression networks based on TCGA ovarian cancer RNAseq data. We identify novel co-expression modules and hub structure specific to tumor cells.Availability and implementationR codes can be found at https://github.com/petraf01/TSNet.Supplementary information Supplementary data are available at Bioinformatics online.

Characterizing co-expression networks underpinning maize stalk rot virulence in Fusarium verticillioides through computational subnetwork module analyses

Fusarium verticillioides is recognized as an important stalk rot pathogen of maize worldwide, but our knowledge of genetic mechanisms underpinning this pathosystem is limited. Previously, we identified a striatin-like protein Fsr1 that plays an important role in stalk rot. To further characterize transcriptome networks downstream of Fsr1, we performed next-generation sequencing (NGS) to investigate relative read abundance and also to infer co-expression networks utilizing the preprocessed expression data through partial correlation. We used a probabilistic pathway activity inference strategy to identify functional subnetwork modules likely involved in virulence. Each subnetwork modules consisted of multiple correlated genes with coordinated expression patterns, but the collective activation levels were significantly different in F. verticillioides wild type versus fsr1 mutant. We also identified putative hub genes from predicted subnetworks for functional validation and network robustness studies through mutagenesis, virulence and qPCR assays. Our results suggest that these genes are important virulence genes that regulate the expression of closely correlated genes, demonstrating that these are important hubs of their respective subnetworks. Lastly, we used key F. verticillioides virulence genes to computationally predict a subnetwork of maize genes that potentially respond to fungal genes by applying cointegration-correlation-expression strategy.