A HIERARCHICAL BAYESIAN APPROACH FOR DETECTING DIFFERENTIAL GENE EXPRESSION IN UNREPLICATED RNA-SEQUENCING DATA

Next-generation sequencing technologies have emerged as a promising technology in a variety of fields, including genomics, epigenomics, and transcriptomics. These technologies play an important role in understanding cell organization and functionality. Unlike data from earlier technologies (e.g., microarrays), data from next-generation sequencing technologies are highly replicable with little technical variation. One application of next-generation sequencing technologies is RNA-Sequencing (RNA-Seq). It is used for detecting differential gene expression between different biological conditions. While statistical methods for detecting differential expression in RNA-Seq data exist, one serious limitation to these methods is the absence of biological replication. At present, the high cost of next-generation sequencing technologies imposes a serious restriction on the number of biological replicates. We present a simple parametric hierarchical Bayesian model for detecting differential expression in data from unreplicated RNA-Seq experiments. The model extends naturally to multiple treatment groups and any number of biological replicates. We illustrate the application of this model through simulation studies and compare our approach to existing methods for detecting differential expression such as, Fisher\u27s Exact Test

A simple model-based approach to variable selection in classification and clustering

Clustering and classification of replicated data is often performed using classical techniques that inappropriately treat the data as unreplicated, or by complex modern ones that are computationally demanding. In this paper, we introduce a simple approach based on a spike-and-slab mixture model that is fast, automatic, allows classification, clustering and variable selection in a single framework, and can handle replicated or unreplicated data. Simulation shows that our approach compares well with other recently proposed methods. The ideas are illustrated by application to microarray and metabolomic data. The Canadian Journal of Statistics 43: 157-175; 2015 (c) 2015 Statistical Society of Canad

Transcriptomic responses of the olive fruit fly Bactrocera oleae and its symbiont Candidatus Erwinia dacicola to olive feeding

The olive fruit fly, Bactrocera oleae, is the most destructive pest of olive orchards worldwide. The monophagous larva has the unique capability of feeding on olive mesocarp, coping with high levels of phenolic compounds and utilizing non-hydrolyzed proteins present, particularly in the unripe, green olives. On the molecular level, the interaction between B. oleae and olives has not been investigated as yet. Nevertheless, it has been associated with the gut obligate symbiotic bacterium Candidatus Erwinia dacicola. Here, we used a B. oleae microarray to analyze the gene expression of larvae during their development in artificial diet, unripe (green) and ripe (black) olives. The expression profiles of Ca. E. dacicola were analyzed in parallel, using the Illumina platform. Several genes were found overexpressed in the olive fly larvae when feeding in green olives. Among these, a number of genes encoding detoxification and digestive enzymes, indicating a potential association with the ability of B. oleae to cope with green olives. In addition, a number of biological processes seem to be activated in Ca. E. dacicola during the development of larvae in olives, with the most notable being the activation of amino-acid metabolism

High-Dimensional Bayesian Clustering with Variable Selection: The R Package bclust

The R package bclust is useful for clustering high-dimensional continuous data. The package uses a parametric spike-and-slab Bayesian model to downweight the effect of noise variables and to quantify the importance of each variable in agglomerative clustering. We take advantage of the existence of closed-form marginal distributions to estimate the model hyper-parameters using empirical Bayes, thereby yielding a fully automatic method. We discuss computational problems arising in implementation of the procedure and illustrate the usefulness of the package through examples

Normalization of two-channel microarrays accounting for experimental design and intensity-dependent relationships

eCADS is a new method for multiple array normalization of two-channel microarrays that takes into account general experimental designs and intensity-dependent relationships and allows for a more efficient dye-swap design that requires only one array per sample pair

Zim17/Tim15 links mitochondrial iron–sulfur cluster biosynthesis to nuclear genome stability

Genomic instability is related to a wide-range of human diseases. Here, we show that mitochondrial iron–sulfur cluster biosynthesis is important for the maintenance of nuclear genome stability in Saccharomyces cerevisiae. Cells lacking the mitochondrial chaperone Zim17 (Tim15/Hep1), a component of the iron–sulfur biosynthesis machinery, have limited respiration activity, mimic the metabolic response to iron starvation and suffer a dramatic increase in nuclear genome recombination. Increased oxidative damage or deficient DNA repair do not account for the observed genomic hyperrecombination. Impaired cell-cycle progression and genetic interactions of ZIM17 with components of the RFC-like complex involved in mitotic checkpoints indicate that replicative stress causes hyperrecombination in zim17Δ mutants. Furthermore, nuclear accumulation of pre-ribosomal particles in zim17Δ mutants reinforces the importance of iron–sulfur clusters in normal ribosome biosynthesis. We propose that compromised ribosome biosynthesis and cell-cycle progression are interconnected, together contributing to replicative stress and nuclear genome instability in zim17Δ mutants