Finite mixtures of matrix-variate Poisson-log normal distributions for three-way count data

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 to 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. A Markov chain Monte Carlo expectation-maximization algorithm is used for parameter estimation and information criteria are used for model selection. The models are applied to both real and simulated data, giving favourable clustering results

Global transcription profiling reveals differential responses to chronic nitrogen stress and putative nitrogen regulatory components in Arabidopsis

<p>Abstract</p> <p>Background</p> <p>A large quantity of nitrogen (N) fertilizer is used for crop production to achieve high yields at a significant economic and environmental cost. Efforts have been directed to understanding the molecular basis of plant responses to N and identifying N-responsive genes in order to manipulate their expression, thus enabling plants to use N more efficiently. No studies have yet delineated these responses at the transcriptional level when plants are grown under chronic N stress and the understanding of regulatory elements involved in N response is very limited.</p> <p>Results</p> <p>To further our understanding of the response of plants to varying N levels, a growth system was developed where N was the growth-limiting factor. An Arabidopsis whole genome microarray was used to evaluate global gene expression under different N conditions. Differentially expressed genes under mild or severe chronic N stress were identified. Mild N stress triggered only a small set of genes significantly different at the transcriptional level, which are largely involved in various stress responses. Plant responses were much more pronounced under severe N stress, involving a large number of genes in many different biological processes. Differentially expressed genes were also identified in response to short- and long-term N availability increases. Putative N regulatory elements were determined along with several previously known motifs involved in the responses to N and carbon availability as well as plant stress.</p> <p>Conclusion</p> <p>Differentially expressed genes identified provide additional insights into the coordination of the complex N responses of plants and the components of the N response mechanism. Putative N regulatory elements were identified to reveal possible new components of the regulatory network for plant N responses. A better understanding of the complex regulatory network for plant N responses will help lead to strategies to improve N use efficiency.</p

Finite Mixtures of Multivariate Poisson-Log Normal Factor Analyzers for Clustering Count Data

A mixture of multivariate Poisson-log normal factor analyzers is introduced by imposing constraints on the covariance matrix, which resulted in flexible models for clustering purposes. In particular, a class of eight parsimonious mixture models based on the mixtures of factor analyzers model are introduced. Variational Gaussian approximation is used for parameter estimation, and information criteria are used for model selection. The proposed models are explored in the context of clustering discrete data arising from RNA sequencing studies. Using real and simulated data, the models are shown to give favourable clustering performance. The GitHub R package for this work is available at https://github.com/anjalisilva/mixMPLNFA and is released under the open-source MIT license.Comment: 29 pages, 2 figure

CMB Signals of Neutrino Mass Generation

We propose signals in the cosmic microwave background to probe the type and spectrum of neutrino masses. In theories that have spontaneous breaking of approximate lepton flavor symmetries at or below the weak scale, light pseudo-Goldstone bosons recouple to the cosmic neutrinos after nucleosynthesis and affect the acoustic oscillations of the electron-photon fluid during the eV era. Deviations from the Standard Model are predicted for both the total energy density in radiation during this epoch, \Delta N_nu, and for the multipole of the n'th CMB peak at large n, \Delta l_n. The latter signal is difficult to reproduce other than by scattering of the known neutrinos, and is therefore an ideal test of our class of theories. In many models, the large shift, \Delta l_n \approx 8 n_S, depends on the number of neutrino species that scatter via the pseudo-Goldstone boson interaction. This interaction is proportional to the neutrino masses, so that the signal reflects the neutrino spectrum. The prediction for \Delta N_nu is highly model dependent, but can be accurately computed within any given model. It is very sensitive to the number of pseudo-Goldstone bosons, and therefore to the underlying symmetries of the leptons, and is typically in the region of 0.03 < \Delta N_nu < 1. This signal is significantly larger for Majorana neutrinos than for Dirac neutrinos, and, like the scattering signal, varies as the spectrum of neutrinos is changed from hierarchical to inverse hierarchical to degenerate.Comment: 40 pages, 4 figure

Exploring the Molecular and Metabolic Factors Contributing to the Adaptation of Maize Seedlings to Nitrate Limitation

Crop production on soils containing sub-optimal levels of nitrogen (N) severely compromises yield potential. The development of plant varieties displaying high N use efficiency (NUE) will optimize N fertilizer use and reduce the environmental damage caused by excess N application. Maize is one of the most important crops cultivated worldwide. Identification of the genotypes with an enhanced NUE in the field is both time and resource consuming and sometime is difficult due to the regulation in the biotechnology programs. Identification of traits associated with adaptation to N limitation at an early vegetative stage which could reflect NUE at maturity is in need. We developed a hydroponic growth system and used it to test two genotypes that were different in their NUE at maturity under N limitation. One genotype SRG-200 showed a higher NUE than the other genotype SRG-100 and we used its hybrid SRG-150 as a reference for NUE. A number of phenotypic, molecular, and metabolic factors were tested using these three genetic lines at an early vegetative stage to determine which of these could be more indicative of predicting improved NUE at an early seedling stage. These include a transcriptional analysis which showed that the higher NUE in SRG-200 genotype is associated with higher transcript levels for the genes involved in nitrate transport, N assimilation, and GS and that the SRG-200 genotype maintained higher sugar content in leaves. Those identified in this study could be useful indicators for selecting promising maize lines at early stages to help develop elite varieties showing an enhanced NUE

β-III spectrin is critical for development of purkinje cell dendritic tree and spine morphogenesis

Mutations in the gene encoding β-III spectrin give rise to spinocerebellar ataxia type 5 (SCA5), a neurodegenerative disease characterized by progressive thinning of the molecular layer, loss of Purkinje cells and increasing motor deficits. A mouse lacking full-length β-III spectrin (β-III(−/−)) displays a similar phenotype. In vitro and in vivo analyses of Purkinje cells lacking β-III spectrin, reveal a critical role for β-III spectrin in Purkinje cell morphological development. Disruption of the normally well-ordered dendritic arborization occurs in Purkinje cells from β-III(−/−) mice, specifically showing a loss of monoplanar organization, smaller average dendritic diameter and reduced densities of Purkinje cell spines and synapses. Early morphological defects appear to affect distribution of dendritic, but not axonal, proteins. This study confirms that thinning of the molecular layer associated with disease pathogenesis is a consequence of Purkinje cell dendritic degeneration, as Purkinje cells from 8-month old β-III(−/−) mice have drastically reduced dendritic volumes, surface areas and total dendritic lengths compared to 5–6 week old β-III(−/−) mice. These findings highlight a critical role of β-III spectrin in dendritic biology and are consistent with an early developmental defect in β-III(−/−) mice, with abnormal Purkinje cell dendritic morphology potentially underlying disease pathogenesis