High-dimensional data of discrete and skewed nature is commonly encountered in high-throughput sequencing studies. Analyzing the network itself or the interplay between genes in this type of data continues to present many challenges. As data visualization techniques become cumbersome for higher dimensions and unconvincing when there is no clear separation between homogeneous subgroups within the data, cluster analysis provides an intuitive alternative. The aim of applying mixture model-based clustering in this context is to discover groups of co-expressed genes, which can shed light on biological functions and pathways of gene products. A mixture of multivariate Poisson-log normal (MPLN) model is developed for clustering of high-throughput transcriptome sequencing data. Parameter estimation is carried out using a Markov chain Monte Carlo expectation-maximization (MCMC-EM) algorithm, and information criteria are used for model selection. The mixture of MPLN model is able to fit a wide range of correlation and overdispersion situations, and is suited for modeling multivariate count data from RNA sequencing studies. All scripts used for implementing the method can be found at https://github.com/anjalisilva/MPLNClust .

Edible dry beans (Phaseolus vulgaris L.) that darken during postharvest storage are graded lower and are less marketable than their non-darkened counterparts. Seed coat darkening in susceptible genotypes is dependent upon the availability of proanthocyanidins, and their subsequent oxidation to reactive quinones. Mature cranberry beans lacking this postharvest darkening trait tend to be proanthocyanidin-deficient, although the underlying molecular and biochemical determinants for this metabolic phenomenon are unknown. Seed coat proanthocyanidin levels increased with plant maturation in a darkening-susceptible cranberry bean recombinant inbred line (RIL), whereas these metabolites were absent in seeds of the non-darkening RIL plants. RNA sequencing (RNA-seq) analysis was used to monitor changes in the seed coat transcriptome as a function of bean development, where transcript levels were measured as fragments per kilobase of exon per million fragments mapped. A total of 1336 genes were differentially expressed between darkening and non-darkening cranberry bean RILs. Structural and regulatory genes of the proanthocyanidin biosynthesis pathway were upregulated in seed coats of the darkening RIL. A principal component analysis determined that changes in transcript levels for two genes of unknown function and three proanthocyanidin biosynthesis genes, FLAVANONE 3-HYDROXYLASE 1, DIHYDROFLAVONOL 4-REDUCTASE 1 and ANTHOCYANIDIN REDUCTASE 1 (PvANR1) were highly correlated with proanthocyanidin accumulation in seed coats of the darkening-susceptible cranberry bean RIL. HPLC-DAD analysis revealed that in vitro activity of a recombinant PvANR1 was NADPH-dependent and assays containing cyanidin yielded epicatechin and catechin; high cyanidin substrate levels inhibited the formation of both of these products. Proanthocyanidin oxidation is a pre-requisite for postharvest-related seed coat darkening in dicotyledonous seeds. In model plant species, the accumulation of proanthocyanidins is dependent upon upregulation of biosynthetic genes. In this study, proanthocyanidin production in cranberry bean seed coats was strongly associated with an increase in PvANR1 transcripts during seed maturation. In the presence of NADPH, PvANR1 converted the physiologically relevant substrate cyanidin to epicatechin and catechin.

Nutrigenetics research is anticipated to lay the foundation for personalized dietary recommendations; however, it remains unclear if providing individuals with their personal genetic information changes dietary behaviors. Our objective was to evaluate if providing information for a common variant in the fatty acid desaturase 1 (FADS1) gene changed omega-3 fatty acid (FA) intake and blood levels in young female adults (18–25 years). Participants were randomized into Genetic (intervention) and Non-Genetic (control) groups, with measurements taken at Baseline and Final (12 weeks). Dietary intake of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) was assessed using an omega-3 food frequency questionnaire. Red blood cell (RBC) FA content was quantified by gas chromatography. Implications of participation in a nutrigenetics study and awareness of omega-3 FAs were assessed with online questionnaires. Upon completion of the study, EPA and DHA intake increased significantly (p = 1.0 × 10⁻⁴) in all participants. This change was reflected by small increases in RBC %EPA. Participants in the Genetic group showed increased awareness of omega-3 terminology by the end of the study, reported that the dietary recommendations were more useful, and rated cost as a barrier to omega-3 consumption less often than those in the Non-Genetic group. Providing participants FADS1 genetic information did not appear to influence omega-3 intake during the 12 weeks, but did change perceptions and behaviors related to omega-3 FAs in this timeframe.

Discrete data such as counts of microbiome taxa resulting from next-generation sequencing are routinely encountered in bioinformatics. Taxa count data in microbiome studies are typically high-dimensional, over-dispersed, and can only reveal relative abundance therefore being treated as compositional. Analyzing compositional data presents many challenges because they are restricted to a simplex. In a logistic normal multinomial model, the relative abundance is mapped from a simplex to a latent variable that exists on the real Euclidean space using the additive log-ratio transformation. While a logistic normal multinomial approach brings flexibility for modeling the data, it comes with a heavy computational cost as the parameter estimation typically relies on Bayesian techniques. In this paper, we develop a novel mixture of logistic normal multinomial models for clustering microbiome data. Additionally, we utilize an efficient framework for parameter estimation using variational Gaussian approximations (VGA). Adopting a variational Gaussian approximation for the posterior of the latent variable reduces the computational overhead substantially. The proposed method is illustrated on simulated and real datasets.

Three-way data structures, characterized by three entities, the units, the variables and the occasions, are frequent in biological studies. In RNA sequencing, three-way data structures are obtained when high-throughput transcriptome sequencing data are collected for n genes across p conditions at r occasions. Matrix variate distributions offer a natural way to model three-way data and mixtures of matrix variate distributions can be used to cluster three-way data. Clustering of gene expression data is carried out as means of discovering gene co-expression networks. In this work, a mixture of matrix variate Poisson-log normal distributions is proposed for clustering read counts from RNA sequencing. By considering the matrix variate structure, full information on the conditions and occasions of the RNA sequencing dataset is simultaneously considered, and the number of covariance parameters to be estimated is reduced. We propose three different frameworks for parameter estimation: a Markov chain Monte Carlo-based approach, a variational Gaussian approximation-based approach, and a hybrid approach. Various information criteria are used for model selection. The models are applied to both real and simulated data, and we demonstrate that the proposed approaches can recover the underlying cluster structure in both cases. In simulation studies where the true model parameters are known, our proposed approach shows good parameter recovery. The GitHub R package for this work is available at https://github.com/anjalisilva/mixMVPLN and is released under the open source MIT license.