Section: Application Domains

Analysis of genomic data

For many years now, select collaborates with Marie-Laure Martin-Magniette (URGV) for the analysis of genomic data. An important theme of this collaboration is using statistically sound model-based clustering methods to discover groups of co-expressed genes from microarray and high-throughput sequencing data. In particular, identifying biological entities that share similar profiles across several treatment conditions, such as co-expressed genes, may help identify groups of genes that are involved in the same biological processes.

Yann Vasseur is completing a thesis co-supervised by Gilles Celeux and Marie-Laure Martin-Magniette on this topic, which is also an interesting investigation domain for the latent block model developed by select . For this work, Yann Vasseur is dealing with high-dimensional ill-posed problems where the number of variable is almost equal to the number of observations. He has designed heuristic tools using regularized regression methods to circumvent this difficulty.

select collaborates with Anavaj Sakuntabhai and Benno Schwikowski (Pasteur Institute) on prediction of dengue fever severity from high-dimensional gene expression data. One project involves using/finding new and computationally efficient methods (e.g., 2d isotonic regression, lasso regression) for predicting dengue severity. Due to the high-dimensional nature of the data and low-dimensional nature of the number of individuals, false discovery rate (FDR) methods are used to provide statistical justification of results. A second project aims to predict dengue severity using only low-dimensional clinical data obtained at hospital arrival. A third project involves statistical meta-analysis of newly collected dengue gene expression data along with recently published data sets from other groups.

select is involved in the ANR “jeunes chercheurs” MixStatSeq directed by Cathy Maugis (INSA Toulouse), which is concerned with statistical analysis and clustering of RNASeq genomics data.