Position

Researcher (DR) at Institut National de la Recherche pour l'Agriculture, l'alimentation et l'Environnement

• in the joint Research Unit MIA-Paris (Solstis Team)
• in the joint Research Unit IPS2 (Leader of the group Genomic networks).

Phone: +33 1 44 08 16 73 (Paris) 
Phone: +33 1 69 15 77 64 (Gif-sur-Yvette) 
Fax: + 33 1 44 08 16 66
Mail : marie_laure.martin@agroparistech.fr

Research interests

• Statistical Methodology
  • High-dimensional regression for network inference
  • Variable selection in Gaussian Mixture models
  • Mixture models
  • Model selection

• Statistical Applications in Molecular Biology
  • Omic data (transcriptomic, proteomic and metabolomic data)
  • Transcription Factors
  • Global analysis of microarray data
  • Gene networks

Students

Co-supervisor with Gilles Celeux of the thesis of Cathy Maugis Variable selection for model-based clustering. Application for transcriptome data analysis.

Co-supervisor with Stéphane Robin of the thesis of Caroline Bérard Statistical and bioinformatic analysis of ChIP-chip data and transcriptome data from tiling array.

Co-supervisor with Stéphane Robin of the thesis of Stevenn Volant Statistical methods for transcriptome tiling-array data.

Co-supervisor with Etienne Delannoy of the thesis of Rim Zaag 

Co-supervisor with Gilles Celeux of the thesis of Yann Vasseur

Co-supervisor with Julien Chiquet and Guillem Rigaill of the thesis of Trung Ha

Current PhD students:

• Perrine Lacroix co-supervised with Pascal Massart (LMO) 
• Julien Rozière co-supervised with Sylvie Coursol (IJPB)
• Yacine Djabali co-supervised with Mélisande Blein-Nicolas (GQE- Le Moulon)

Scientific animations

NETBIO is a French group of more than 200 scientists, mainly statisticians, biologists and bioinformaticians, interested in gene networks and network inference and analysis

DIGIT-Bio "IA pour les sciences du vivant" is a  cycle of webinars on IA for the life sciences

Training

Since 2015 and twice per year, I have been co-leading a one-week training on RNAseq analysis entitled "From gene expression to networks" with Etienne Delannoy

Softwares and R Packages

DiCoExpress : is a workspace developed in R to perform RNAseq data analysis.

Development of R package before 2015

Anapuce: normalisation and differential analysis of transcriptome data

MixThres: mixture model of truncated gaussians to detect a hybridization threshold for microarray data

ChIPmix: mixture model of regression for chIP-chip data

Contribution to R package 

kerfdr: semi-parametric kernel-based approach to local fdr estimations

Softwares

SelvarClust is a software implemented in C++ with object-oriented programming. It is devoted to the variable selection in model-based clustering. It is a greedy algorithm associated to the SR modeling proposed by Maugis et al. (2009) in Biometrics. This software allows us to study data where individuals are described by quantitative block variables. It returns a data clustering and the selected model, composed of the number of clusters, the mixture form and the variable partition.

SelvarClustMV is a software implemented in C++ with object-oriented programming. It is an extension of SelvarClust, it is devoted to the variable selection in model-based clustering allowing for missing value. Currently, this software is proposed for Gaussian mixtures whose variance matrices are assumed to be identical and free (m=[pkLC]).

SelvarClustIndep is a software implemented in C++ with object-oriented programming. It is devoted to the variable selection in model-based clustering. It is a greedy algorithm associated to the SRUW modeling proposed by Maugis et al. (2009) in CSDA. The SRUW modeling takes into account three possible roles for each variable: relevant, redundant and independent. This software allows to study datasets where observations are described by quantitative variables. It returns a data clustering and the selected model composed of the number of clusters, the mixture form, the variance matrix form for the linear regression and the independent Gaussian density, and the variable partition.NETBIO