The Fate of a Polygenic Phenotype Within the Genomic Landscapes of Introgression in the European Seabass Hybrid Zone.

Unraveling the evolutionary mechanisms and consequences of hybridization is a major concern in biology. Many studies have documented the interplay between recombination and selection in modulating the genomic landscape of introgression, but few have considered how associations with phenotype may affect this landscape. Here, we use the European seabass (Dicentrarchus labrax), a key species in marine aquaculture that undergoes natural hybridization, to determine how selection on phenotype modulates the introgression landscape between Atlantic and Mediterranean lineages.

Divergent clonal differentiation trajectories establish CD8 memory T cell heterogeneity during acute viral infections in humans.

The CD8 T cell response to an antigen is composed of many T cell clones with unique T cell receptors, together forming a heterogeneous repertoire of effector and memory cells. How individual T cell clones contribute to this heterogeneity throughout immune responses remains largely unknown. In this study, we longitudinally track human CD8 T cell clones expanding in response to yellow fever virus (YFV) vaccination at the single-cell level.

Extending approximate Bayesian computation with supervised machine learning to infer demographic history from genetic polymorphisms using DIYABC Random Forest.

Simulation-based methods such as approximate Bayesian computation (ABC) are well-adapted to the analysis of complex scenarios of populations and species genetic history. In this context, supervised machine learning (SML) methods provide attractive statistical solutions to conduct efficient inferences about scenario choice and parameter estimation. The Random Forest methodology (RF) is a powerful ensemble of SML algorithms used for classification or regression problems.

Probabilistic count matrix factorization for single cell expression data analysis.

Motivation: The development of high-throughput single-cell sequencing technologies now allows the investigation of the population diversity of cellular transcriptomes. The expression dynamics (gene-to-gene variability) can be quantified more accurately, thanks to the measurement of lowly expressed genes. In addition, the cell-to-cell variability is high, with a low proportion of cells expressing the same genes at the same time/level.

High dimensional classification with combined adaptive sparse PLS and logistic regression.

Motivation: The high dimensionality of genomic data calls for the development of specific classification methodologies, especially to prevent over-optimistic predictions. This challenge can be tackled by compression and variable selection, which combined constitute a powerful framework for classification, as well as data visualization and interpretation. However, current proposed combinations lead to unstable and non convergent methods due to inappropriate computational frameworks.