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Florent GUINOT

Phd Student (CIFRE contract) with UEVE and Bioptimize
Laboratoire de Mathématiques et Modélisation d'Evry (LAMME)
Equipe Stat & Génome
23 boulevard de France 91000 Evry
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Statistical learning for omics association and interaction studies based on blockwise feature compression.

  • Improvement of the statistical power in GWAS by taking in account the genome structure through combination of hierarchical clustering and penalized regression.
  • Detection of large-scale interactions on structured datasets. Application for the research of interaction between genome and microbiome.

Direction: Christophe Ambroise, Marie Szafranski

Research topics

  • Statistical learning applied to high-dimensional data (omics data).
  • Hypothesis testing.
  • Genome-Wide association studies and Metagenomics.


  • R package SIComORe : From a set of input matrices and phenotype related to the same set of individual, sicomore is a two-step method which (1) finds and select groups of correlated variables in each input matrix which are good predictors for the common phenotype; (2) find the most predictive interaction effects between the set of data by testing for interaction between the selected groups of each input matrix.
  • Webserver tool LEOS: The LEOS web tool allows to perform Genome-Wide analysis with group of variables through hierarchical SNP aggregation. The program requires a matrix *X* of SNP coded as [0,1,2], a binary phenotype vector Y coded as [0,1] and optionally a genetic map indicating to which chromosome belongs the SNP. In order to work properly, the program requires that the columns *X* are ordered according to the SNP position on the genome. The program provides a manhattan plot indicating which clusters of SNPs are significantly associated with the phenotype, a table of results and a plot showing at which level in the hierarchy the SNPs have been aggregated.


[1] Guinot, F., Szafranski, M., Ambroise, C. & Samson, F. Learning the optimal scale for GWAS through hierarchical SNP aggregation. 2017. BMC bioinformatics.
[2] Guinot, F., Szafranski, M., Chiquet, J. & Ambroise, C. Fast Computation of Genome-Metagenome Interaction Effects, 2018. (Preprint).
[3] Guinot, F., Szafranski, M., Chiquet, J. & Ambroise, C. Une approche hiérarchique de la recherche d’interactions entre données omiques, 2018. (national conference paper)


  • TD Probabilités - Licence 1 Biologie (S2 2017/2018)
  • TP Probabilités / Introduction au logiciel R - Licence 1 Biologie (S2 2017/2018)
  • TD Probabilités - Licence 1 Biologie (S2 2016/2017)
members/fguinot/welcome.txt · Last modified: 2019/01/20 12:32 by fguinot

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