Audrey Hulot

Multi-omic data: clustering and network inference (2017-2020)

Audrey’s PhD was co-supervised with Florence Jaffrezic and Henri-Jean Garchon (33%/33%/33%), from 2017 to 2020.

Summary

This thesis develops statistical methods for the analysis of high-dimensional omics data, focusing on two complementary tasks: consensus clustering and network inference. It introduces a fast tree aggregation method that combines several hierarchical clustering trees, obtained for instance from different data types or resampled datasets, into a single consensus tree, with an efficient algorithm implemented in the mergeTrees R package. The thesis also contributes methods for inferring biological association networks from omics data, and illustrates both lines of work on real transcriptomic and proteomic datasets.

PhD manucript

2020

  1. PhD thesis
    Analyses de données omiques : clustering et inférence de réseaux
    Audrey Hulot
    Université Paris-Saclay, 2020

Journal papers

2020

  1. BMC Bioinfo
    Fast tree aggregation for consensus hierarchical clustering
    Audrey Hulot, Julien Chiquet, Florence Jaffrezic, and 1 more author
    BMC Bioinformatics, 2020

Conferences

2018

  1. Fused-ANOVA: une méthode de clustering en grande dimension
    A. Hulot, J. Chiquet, F. Jaffrezic, and 1 more author
    In actes des 50\ieme journées françaises de statistique, Saclay, 2018