DU-Bii module 6: Integrative Bioinformatics

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Access to training material

• Github (sources): https://github.com/DU-Bii/module-6-Integrative-Bioinformatics/tree/master/current

Teaching material

Topics	Trainers	Teaching material
Analyse multi-omique par factorisation multi-niveaux de matrices (MOFA)	Laura Cantini (helpers: Anaïs Baudot, Claire Lansonneur, Olivier Sand)	Session 1
Analyse multi-omique par factorisation multi-niveaux de matrices (mixOmics/mixKernel)	Sébastien Déjean and Jérôme Mariette (à distance) (helpers: Etienne Thévenot, Claire Lansonneur, Olivier Sand)	Session 2
Web semantique, représentation des connaissances	Alban Gaignard (helper: Olivier Sand)	Session 3
ProMetIS: : un exemple d’intégration de données protéomiques et métabolomiques	Etienne Thévenot (helpers: Camilo Broc, Olivier Sand)	Session 4
Network Analysis & Cytoscape	Anaïs Baudot (helpers: Costas Bouyioukos, Claire Lansonneur, Olivier Sand)	Session 5
Network Inference & WGCNA	Costas Bouyioukos (helpers: Anaïs Baudot, Claire Lansonneur, Olivier Sand)	Session 6

Description

This course takes place in the 1-month training “Diplôme Universitaire en Bioinformatique Intégrative” (DU-Bii) organised by Université Paris-Diderot and the Institut Français de Bioinformatique (IFB).

Table of contents

Session 1: Integrating multi-omics data with multi-level matrix factorisation

Teacher: Laura Cantini

Concepts:

• Integrative bioinformatics approaches and their application to cancer - Motivation
  - Which approach to answer which question (subsetting, modules, pathways) ?
  - Main methodologies: networks, matrix factorisation

Practical:

• MOFA

Datasets:

• Chronic Lymphoblastic Leukemia (CLL)

Session 2: Integrating multi-omics data with multi-level matrix factorisation

Teachers: Sébastien Déjean, Jérôme Mariette

Concepts:

• Principles of multi-level matrix factorisation (Sébastien Déjean)
• Kernel-based approaches (Jérôme Mariette)

Practical:

• mixOmics
• JM tools (please specify)

Datasets:

• Chronic Lymphoblastic Leukemia (CLL)
• metagenomics data (Jérôme Mariette)

Session 3: Web sémantique et représentation des connaissances

Teacher: Alban Gaignard

Session 4: ProMetIS : un exemple d’intégration de données protéomiques et métabolomiques

Teacher: Etienne Thévenot

Session 5: Network Analysis & Cytoscape

Teacher: Anaïs Baudot

• Introduction to network sciences in biology

• Practical with Cytoscape
  • Basics on human interactome
  • Keywords: interactome, regulome, network visualisation and topological analyses
  • Practicals:

Session 6: WGCNA, network inference

Teacher: Costas Bouyioukos

• Contents:
  • Introduction to gene co-expression networks.
  • Introduction to WGCNA and the concept of eigengenes.
  • Introduction: inferring networks from *omics data, clustering for Gene Regulatory Networks.
    • Slides: SLidesM6S5.

Before the TP: Must read this document to familiarise with the terminology of correlation networks and WGCNA.

• Particulars please read here.

• Practical with R

  • Inferrence of co-expression network modules with the WGCNA package.
  • Document containing all the R code for the TP Network Inference with WGCNA R and a notebook with explanations and output graphs Network Inference with WGCNA notebook.

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Credits

Course coordinators

1. Anaïs Baudot, CNRS, Marseille
2. Olivier Sand, CNRS, Lille
3. Jacques van Helden, Institut Français de Bioinformatique, Aix-Marseille Université

Other teachers / helpers

1. Laura Cantini
2. Sébastien Déjean
3. Jérôme Mariette
4. Alban Gaignard
5. Etienne Thévenot
6. Costas Bouyioukos
7. Claire Lansonneur

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Installation

Contributors (members of the teaching team)

git clone git@github.com:DU-Bii/module-6-Integrative-Bioinformatics.git

Other people

git clone https://github.com/DU-Bii/module-6-Integrative-Bioinformatics.git

License

This content is released under the Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0) license. See the bundled LICENSE file for details.

Ce contenu est mis à disposition selon les termes de la licence Creative Commons Attribution - Partage dans les Mêmes Conditions 4.0 International (CC BY-SA 4.0). Consultez le fichier LICENSE pour plus de détails.