Abstract not found

Abstract not found

Motivation: RNA-Seq is increasingly being used for differential gene expression analysis, which was dominated by the microarray technol-ogy in the past decade. However, inferring differential gene expression based on the observed difference of RNA-Seq read counts has unique challenges that were not present in microarray-based analysis. The differential expression estimation may be biased against low read count values such that the differential expression of genes with high read counts is more easily detected. The estimation bias may further propagate in downstream analyses at the systems biology level if it is not corrected. Results: To obtain a better inference of differential gene expression, we propose a new efficient algorithm based on a Markov random field (MRF) model, called MRFSeq, that uses additional gene coexpression data to enhance the prediction power. Our main technical contribution is the careful selection of the clique potential functions in the MRF so its maximum a posteriori estimation can be reduced to the well-known maximum flow problem and thus solved in polynomial time. Our ex-tensive experiments on simulated and real RNA-Seq datasets demon-strate that MRFSeq is more accurate and less biased against genes with low read counts than the existing methods based on RNA-Seq data alone. For example, on the well-studied MAQC dataset, MRFSeq improved the sensitivity from 11.6 to 38.8 % for genes with low read counts. Availability:MRFSeq is implemented in C and available at

Abstract not found

high-dimensional two-sample test for the mean

Spatial ecological networks are widely used to model interactions between georeferenced bi-ological entities (e.g., populations or communities). The analysis of such data often leads to a two-step approach where groups containing similar biological entities are firstly identified and the spatial information is used afterwards to improve the ecological interpretation. We develop an integrative approach to retrieve groups of nodes that are geographically close and ecologically similar. Our model-based spatially-constrained method embeds the geo-graphical information within a regularization framework by adding some constraints to the maximum likelihood estimation of parameters. A simulation study and the analysis of real data demonstrate that our approach is able to detect complex spatial patterns that are eco-logically meaningful. The model-based framework allows us to consider external information (e.g., geographic proximities, covariates) in the analysis of ecological networks and appears to be an appealing alternative to consider such data. Key-words: Graph Laplacian; Model-based clustering; Stochastic block model;

the functional effect of gene expression

Motivation: RNA-Seq is increasingly being used for differential gene expression analysis, which was dominated by the microarray technol-ogy in the past decade. However, inferring differential gene expression based on the observed difference of RNA-Seq read counts has unique challenges that were not present in microarray-based analysis. The differential expression estimation may be biased against low read count values such that the differential expression of genes with high read counts is more easily detected. The estimation bias may further propagate in downstream analyses at the systems biology level if it is not corrected. Results: To obtain a better inference of differential gene expression, we propose a new efficient algorithm based on a Markov random field (MRF) model, called MRFSeq, that uses additional gene coexpression data to enhance the prediction power. Our main technical contribution is the careful selection of the clique potential functions in the MRF so its maximum a posteriori estimation can be reduced to the well-known maximum flow problem and thus solved in polynomial time. Our ex-tensive experiments on simulated and real RNA-Seq datasets demon-strate that MRFSeq is more accurate and less biased against genes with low read counts than the existing methods based on RNA-Seq data alone. For example, on the well-studied MAQC dataset, MRFSeq improved the sensitivity from 11.6 to 38.8 % for genes with low read counts. Availability:MRFSeq is implemented in C and available at