Illustration thèse confinancée
Thesis by Ekaterina Tomilina (2022 - 2025)
DIGIT-BIO metaprogramme
Themes
Understanding
Thesis by Ekaterina Tomilina (2022 - 2025)

Copula-based network inference for multi-omics data

Thesis by Ekaterina Tomilina (MaIAGE, 2022-2025). Systems biology is built on the analysis of complex, large and highly diverse data networks. Understanding the interdependencies between, and regulation of, the different types of omics data it measures is a genuine challenge. This thesis proposes to use copula theory to construct, test and apply a coupled statistical model of this heterogeneity.

  • Starting date : october 2022
  • Research laboratory : MaIAGE
  • Thesis director :  Gildas MAZO (INRAE, MaIAGE), Florence JAFFREZIC (INRAE, GABI), Andrea Rau (INRAE, GABI)
  • Metaprogramme axis : Axis 1 (Deciphering the functions of living matter at multiple scales: regulation and integration of biological processes)

Summary

To better understand the relationships between the different objects that comprise a biological network (genes, proteins, etc), biologists observe variables of various types: categorical, ordinal, continuous. The 'discovery'' of the inter-dependencies in these heterogeneous data (also known as "multi-omics'' data in biology) is a genuine challenge both in biology and statistics. The goal of this PhD thesis is to build a statistical model to infer these inter-dependencies by modeling the heterogeneity in the data with copulas, which are functions that can couple variables of varying types. The estimation method will be examined both theoretically and numerically, and will be applied to a multi-omics dataset produced by INRAE.

Ekaterina Tomilina

Contact

 

Publications

 

Journal articles

Ekaterina Tomilina, Gildas Mazo, Florence Jaffrézic. A semi-parametric Gaussian copula model for heterogeneous network inference: an application to multi-omics data. 2024. ⟨hal-04847648⟩

Conferences paper

  • Ekaterina Tomilina, Gildas Mazo, Florence Jaffrézic. Méthodes à copules pour l'inférence de réseaux de régulation multi-omiques. Colloque Jeunes Probabilistes et Statisticiens, groupe Modélisation Aléatoire et Statistique de la Société de Mathématiques Appliquées et Industrielles, Oct 2023, Saint Pierre d'Oléron, France. ⟨hal-04308489⟩
  • Ekaterina Tomilina, Gildas Mazo, Florence Jaffrezic. Gaussian copula estimation for heterogeneous data. European Meeting of Statisticians, Jul 2023, Warsaw (POLAND), Poland. . ⟨hal-04308470⟩
  • Ekaterina Tomilina, Gildas Mazo, Florence Jaffrezic. Copula-based models for multi-omic network inference. Compstat 2024, Aug 2024, Giessen, Germany. ⟨hal-04683480⟩
  • Ekaterina Tomilina, Gildas Mazo, Florence Jaffrezic. Copula-based models for multi-omics network inference. Journée des Statistiques 2024, Société Française de Statistique, May 2024, Bordeaux, France. ⟨hal-04598167⟩

Software

See also

Modification date: 03 June 2025 | Publication date: 09 January 2023 | By: Marjorie Domergue