Molecular signatures identified from high-throughput transcriptomic studies often have poor reliability and fail to reproduce across studies. One solution is to combine independent studies into a single integrative analysis, additionally increasing sample size. However, the different protocols and technological platforms across transcriptomic studies produce unwanted systematic variation that strongly confounds the integrative analysis results. When studies aim to discriminate an outcome of interest, the common approach is a sequential two-step procedure; unwanted systematic variation removal techniques are applied prior to classification methods. To limit the risk of overfitting and over-optimistic results of a two-step procedure, we developed a novel multivariate integration method, MINT, that simultaneously accounts for unwanted systematic variation and identifies predictive gene signatures with greater reproducibility and accuracy. In two biological examples on the classification of three human cell types and four subtypes of breast cancer, we combined high-dimensional microarray and RNA-seq data sets and MINT identified highly reproducible and relevant gene signatures predictive of a given phenotype. MINT led to superior classification and prediction accuracy compared to the existing sequential two-step procedures. MINT is a powerful approach and the first of its kind to solve the integrative classification framework in a single step by combining multiple independent studies. MINT is computationally fast as part of the mixOmics R CRAN package, available at http://www.mixOmics.org/mixMINT/ and http://cran.r-project.org/web/packages/mixOmics/ .

The aim of this study was to detect the associations between bovine herpesvirus 1 (BHV-1) status of a herd and respiratory disease (BRD) occurrence and reproductive performance in pregnant heifers and cows. The association between management-related factors and higher BRD occurrence was also estimated.

For the purpose of exploring and modelling the relationships between a dataset and several datasets, multiblock Partial Least Squares is a widely-used regression technique. It is designed as an extension of PLS which aims at linking two datasets. In the same vein, we propose an extension of Redundancy Analysis to the multiblock setting. We show that PLS and multiblock Redundancy Analysis aim at maximizing the same criterion but the constraints are different. From the solutions of both these approaches, it turns out that they are the two end points of a continuum approach that we propose to investigate.

Streptococcus suis is a leading cause of infection in pigs, causing extensive economic losses. In addition, it can also infect wild fauna, and can be responsible for severe infections in humans. Increasing antimicrobial resistance (AMR) has been described in S. suis worldwide and most of the AMR genes are carried by mobile genetic elements (MGEs). This contributes to their dissemination by horizontal gene transfer. A collection of 102 strains isolated from humans, pigs and wild boars in France was subjected to whole genome sequencing in order to: (i) study their genetic diversity, (ii) evaluate their content in virulence-associated genes, (iii) decipher the mechanisms responsible for their AMR and their association with MGEs, and (iv) study their ability to acquire extracellular DNA by natural transformation. Analysis by hierarchical clustering on principal components identified a few virulence-associated factors that distinguish invasive CC1 strains from the other strains. A plethora of AMR genes (n=217) was found in the genomes. Apart from the frequently reported erm(B) and tet(O) genes, more recently described AMR genes were identified [vga(F)/sprA, vat(D)]. Modifications in PBPs/MraY and GyrA/ParC were detected in the penicillin- and fluoroquinolone-resistant isolates respectively. New AMR gene–MGE associations were detected. The majority of the strains have the full set of genes required for competence, i.e for the acquisition of extracellular DNA (that could carry AMR genes) by natural transformation. Hence the risk of dissemination of these AMR genes should not be neglected.