Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso.

In clinical trials, identification of prognostic and predictive biomarkers has became essential to precision medicine. Prognostic biomarkers can be useful for the prevention of the occurrence of the disease, and predictive biomarkers can be used to identify patients with potential benefit from the treatment. Previous researches were mainly focused on clinical characteristics, and the use of genomic data in such an area is hardly studied.

A variable selection approach for highly correlated predictors in high-dimensional genomic data.

Motivation: In genomic studies, identifying biomarkers associated with a variable of interest is a major concern in biomedical research. Regularized approaches are classically used to perform variable selection in high-dimensional linear models. However, these methods can fail in highly correlated settings.

A Quantitative Multivariate Model of Human Dendritic Cell-T Helper Cell Communication.

Cell-cell communication involves a large number of molecular signals that function as words of a complex language whose grammar remains mostly unknown. Here, we describe an integrative approach involving (1) protein-level measurement of multiple communication signals coupled to output responses in receiving cells and (2) mathematical modeling to uncover input-output relationships and interactions between signals. Using human dendritic cell (DC)-T helper (Th) cell communication as a model, we measured 36 DC-derived signals and 17 Th cytokines broadly covering Th diversity in 428 observations.

A variable selection approach in the multivariate linear model: an application to LC-MS metabolomics data.

Omic data are characterized by the presence of strong dependence structures that result either from data acquisition or from some underlying biological processes. Applying statistical procedures that do not adjust the variable selection step to the dependence pattern may result in a loss of power and the selection of spurious variables. The goal of this paper is to propose a variable selection procedure within the multivariate linear model framework that accounts for the dependence between the multiple responses.

Two-dimensional segmentation for analyzing Hi-C data.

Motivation: The spatial conformation of the chromosome has a deep influence on gene regulation and expression. Hi-C technology allows the evaluation of the spatial proximity between any pair of loci along the genome. It results in a data matrix where blocks corresponding to (self-)interacting regions appear.