Detecting subnetwork-level dynamic correlations

Correlation-Centric Network (CCN) representation for microbial co-occurrence patterns: new insights for microbial ecology

Mainstream studies of microbial community focused on critical organisms and their physiology. Recent advances in large-scale metagenome analysis projects initiated new researches in the complex correlations between large microbial communities. Specifically, previous studies focused on the nodes (i.e. species) of the Species-Centric Networks (SCNs). However, little was understood about the change of correlation between network members (i.e. edges of the SCNs) when the network was disturbed. Here, we introduced a Correlation-Centric Network (CCN) to the microbial research based on the concept of edge networks. In CCN, each node represented a species-species correlation, and edge represented the species shared by two correlations. In this research, we investigated the CCNs and their corresponding SCNs on two large cohorts of microbiome. The results showed that CCNs not only retained the characteristics of SCNs, but also contained information that cannot be detected by SCNs. In addition, when the members of microbial communities were decreased (i.e. environmental disturbance), the CCNs fluctuated within a small range in terms of network connectivity. Therefore, by highlighting the important species correlations, CCNs could unveil new insights when studying not only the functions of target species, but also the stabilities of their residing microbial communities.

DNLC: differential network local consistency analysis

BACKGROUND

The biological network is highly dynamic. Functional relations between genes can be activated or deactivated depending on the biological conditions. On the genome-scale network, subnetworks that gain or lose local expression consistency may shed light on the regulatory mechanisms related to the changing biological conditions, such as disease status or tissue developmental stages.

RESULTS

In this study, we develop a new method to select genes and modules on the existing biological network, in which local expression consistency changes significantly between clinical conditions. The method is called DNLC: Differential Network Local Consistency. In simulations, our algorithm detected artificially created local consistency changes effectively. We applied the method on two publicly available datasets, and the method detected novel genes and network modules that were biologically plausible.

CONCLUSIONS

The new method is effective in finding modules in which the gene expression consistency change between clinical conditions. It is a useful tool that complements traditional differential expression analyses to make discoveries from gene expression data. The R package is available at https://cran.r-project.org/web/packages/DNLC.

A hypergraph-based method for large-scale dynamic correlation study at the transcriptomic scale

Background

The biological regulatory system is highly dynamic. Correlations between functionally related genes change over different biological conditions, which are often unobserved in the data. At the gene level, the dynamic correlations result in three-way gene interactions involving a pair of genes that change correlation, and a third gene that reflects the underlying cellular conditions. This type of ternary relation can be quantified by the Liquid Association statistic. Studying these three-way interactions at the gene triplet level have revealed important regulatory mechanisms in the biological system. Currently, due to the extremely large amount of possible combinations of triplets within a high-throughput gene expression dataset, no method is available to examine the ternary relationship at the biological system level and formally address the false discovery issue.

Results

Here we propose a new method, Hypergraph for Dynamic Correlation (HDC), to construct module-level three-way interaction networks. The method is able to present integrative uniform hypergraphs to reflect the global dynamic correlation pattern in the biological system, providing guidance to down-stream gene triplet-level analyses. To validate the method’s ability, we conducted two real data experiments using a melanoma RNA-seq dataset from The Cancer Genome Atlas (TCGA) and a yeast cell cycle dataset. The resulting hypergraphs are clearly biologically plausible, and suggest novel relations relevant to the biological conditions in the data.

Conclusions

We believe the new approach provides a valuable alternative method to analyze omics data that can extract higher order structures. The software is at https://github.com/yunchuankong/HypergraphDynamicCorrelation.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5787-x) contains supplementary material, which is available to authorized users.

A new dynamic correlation algorithm reveals novel functional aspects in single cell and bulk RNA-seq data

Dynamic correlations are pervasive in high-throughput data. Large numbers of gene pairs can change their correlation patterns in response to observed/unobserved changes in physiological states. Finding changes in correlation patterns can reveal important regulatory mechanisms. Currently there is no method that can effectively detect global dynamic correlation patterns in a dataset. Given the challenging nature of the problem, the currently available methods use genes as surrogate measurements of physiological states, which cannot faithfully represent true underlying biological signals. In this study we develop a new method that directly identifies strong latent dynamic correlation signals from the data matrix, named DCA: Dynamic Correlation Analysis. At the center of the method is a new metric for the identification of pairs of variables that are highly likely to be dynamically correlated, without knowing the underlying physiological states that govern the dynamic correlation. We validate the performance of the method with extensive simulations. We applied the method to three real datasets: a single cell RNA-seq dataset, a bulk RNA-seq dataset, and a microarray gene expression dataset. In all three datasets, the method reveals novel latent factors with clear biological meaning, bringing new insights into the data.

Dynamic correlation is an important area in expression data. However it hasn’t received much attention because of the lack of effective methods that can unravel the complex relationship. Here we describe a new method that represents a substantial improvement over existing approaches. It achieves the goal of efficiently finding patterns of dynamic correlation in RNA-seq data, as well as detecting biological functions associated with the dynamic correlation patterns. Unlike traditional methods that focus on first-order structures, linear or nonlinear, our method finds second-order patterns that bring insights into the regulations of the complex system. Some of the interesting discoveries by the new method, such as immunological functions of some intestinal epithelial cells, are validated by recent biological publications.

Genome-wide trait-trait dynamics correlation study dissects the gene regulation pattern in maize kernels

BACKGROUND

Dissecting the genetic basis and regulatory mechanisms for the biosynthesis and accumulation of nutrients in maize could lead to the improved nutritional quality of this crop. Gene expression is regulated at the genomic, transcriptional, and post-transcriptional levels, all of which can produce diversity among traits. However, the expression of most genes connected with a particular trait usually does not have a direct association with the variation of that trait. In addition, expression profiles of genes involved in a single pathway may vary as the intrinsic cellular state changes. To work around these issues, we utilized a statistical method, liquid association (LA) to investigate the complex pattern of gene regulation in maize kernels.

RESULTS

We applied LA to the expression profiles of 28,769 genes to dissect dynamic trait-trait correlation patterns in maize kernels. Among the 1000 LA pairs (LAPs) with the largest LA scores, 686 LAPs were identified conditional correlation. We also identified 830 and 215 LA-scouting leaders based on the positive and negative LA scores, which were significantly enriched for some biological processes and molecular functions. Our analysis of the dynamic co-expression patterns in the carotene biosynthetic pathway clearly indicated the important role of lcyE, CYP97A, ZEP1, and VDE in this pathway, which may change the direction of carotene biosynthesis by controlling the influx and efflux of the substrate. The dynamic trait-trait correlation patterns between gene expression and oil concentration in the fatty acid metabolic pathway and its complex regulatory network were also assessed. 23 of 26 oil-associated genes were correlated with oil concentration conditioning on 580 LA-scoutinggenes, and 5% of these LA-scouting genes were annotated as enzymes in the oil metabolic pathway.

CONCLUSIONS

By focusing on the carotenoid and oil biosynthetic pathways in maize, we showed that a genome-wide LA analysis provides a novel and effective way to detect transcriptional regulatory relationships. This method will help us understand the biological role of maize kernel genes and will benefit maize breeding programs.