Section: New Results

High dimensional data

• Approaches Applied in Genomics Context [13]

  Motivation: The association between two blocks of ?omics? data brings challenging issues in computational biology due to their size and complexity. Here, we focus on a class of multivariate statistical methods called partial least square (PLS). Sparse version of PLS (sPLS) operates integration of two datasets while simultaneously selecting the contributing variables. However, these methods do not take into account the important structural or group effects due to the relationship between markers among biological pathways. Hence, considering the predefined groups of markers (e.g. genesets), this could improve the relevance and the efficacy of the PLS approach. Results: We propose two PLS extensions called group PLS (gPLS) and sparse gPLS (sgPLS). Our algorithm enables to study the relationship between two different types of omics data (e.g. SNP and gene expression) or between an omics dataset and multivariate phenotypes (e.g. cytokine secretion). We demonstrate the good performance of gPLS and sgPLS compared with the sPLS in the context of grouped data. Then, these methods are compared through an HIV therapeutic vaccine trial. Our approaches provide parsimonious models to reveal the relationship between gene abundance and the immunological response to the vaccine.

• Combining clustering of variables and feature selection using random forests: the CoV/VSURF procedure [26]

  High-dimensional data classification is a challenging problem. A standard approach to tackle this problem is to perform variables selection, e.g. using step-wise or LASSO procedures. Another standard way is to perform dimension reduction, e.g. by Principal Component Analysis or Partial Least Square procedures. The approach proposed in this paper combines both dimension reduction and variables selection. First, a procedure of clustering of variables is used to built groups of correlated variables in order to reduce the redundancy of information. This dimension reduction step relies on the R package ClustOfVar which can deal with both numerical and categorical variables. Secondly, the most relevant synthetic variables (which are numerical variables summarizing the groups obtained in the first step) are selected with a procedure of variable selection using random forests, implemented in the R package VSURF. Numerical performances of the proposed methodology called CoV/VSURF are compared with direct applications of VSURF or random forests on the original p variables. Improvements obtained with the CoV/VSURF procedure are illustrated on two simulated mixed datasets (cases n>p and n<p)

   

• Arbres CART et Forêts aléatoires, Importance et sélection de variables [27]

  Two algorithms proposed by Leo Breiman : CART trees (Classification And Regression Trees for) introduced in the first half of the 80s and random forests emerged, meanwhile, in the early 2000s, are the subject of this article. The goal is to provide each of the topics, a presentation, a theoretical guarantee, an example and some variants and extensions. After a preamble, introduction recalls objectives of classification and regression problems before retracing some predecessors of the Random Forests. Then, a section is devoted to CART trees then random forests are presented. Then, a variable selection procedure based on permutation variable importance is proposed. Finally the adaptation of random forests to the Big Data context is sketched.

• Comments on: " A Random Forest Guided Tour " [8]

  This paper is a comment on the survey paper by Biau and Scornet (2016) about random forests. We focus on the problem of quantifying the impact of each ingredient of random forests on their performance. We show that such a quantification is possible for a simple pure forest , leading to conclusions that could apply more generally. Then, we consider " hold-out " random forests, which are a good middle point between " toy " pure forests and Breiman's original random forests.

• Targeting HIV-1 Env gp140 to LOX-1 Elicits Immune Responses in Rhesus Macaques. [18]

  Improved antigenicity against HIV-1 envelope (Env) protein is needed to elicit vaccine-induced protective immunity in humans. Here we describe the first tests in non-human primates (NHPs) of Env gp140 protein fused to a humanized anti-LOX-1 recombinant antibody for delivering Env directly to LOX-1-bearing antigen presenting cells, especially dendritic cells (DC). These data, as well as the safety of this protein vaccine, justify further exploration of this DC-targeting vaccine approach for protective immunity against HIV-1.

• Significant changes in HIV-1 Capsid stability induced by common CTL-driven viral sequence mutations. [46]

  HIV-1-infected individuals with protective HLA class I alleles exhibit better control of viremia and slower disease progression. Virus control in these individuals has been associated with strong and potent HIV-1-specific cytotoxic-T-lymphocyte (CTL) responses restricted by protective HLA alleles, but control of viremia also occurs in the presence of selected CTL escape mutations. Taken together, these data demonstrate that CTL-driven escape mutations within p24 Gag restricted by protective HLA class I alleles have a significant impact on capsid stability that might contribute to the persistent control of viral replication observed despite viral escape from CTL responses.

• Optimization and evaluation of luminex performance with supernatants of Peripheral Blood Mononuclear Cell culture. [48]

  The Luminex bead-based multiplex assay is useful for quantifying immune mediators such as cytokines and chemokines. Cross-comparisons of reagents for this technique from different suppliers have already been performed using serum or plasma but rarely with supernatants collected from antigen-stimulated peripheral blood mononuclear cells (PBMC). Here, we first describe an optimization protocol for cell culture including quantity of cells and culture duration to obtain reproducible cytokine and chemokine quantifications. Then, we compared three different Luminex kit suppliers.