This workshop will comprise hands-on sessions, lectures and seminars. All times indicated in CET (Stockholm time), check your time here.

- lecture; - hands-on workshop; - open seminar; - break; - extra materials

Questions? Check the FAQs or use HackMD

Before the course

Follow the pre-course materials and data pre-processing introduction notebook.

Monday 06/09/2021

09.00 - 09.20 - Introduction and contextualization (Rui Benfeitas)

09.20 - 09.55 - Machine Learning view of Omics integration (Nikolay Oskolkov)

09.55 - 10.00 - Break

10.00 - 10.55 - Unsupervised Omics integration (Nikolay Oskolkov)

10.55 - 11.00 - Break

11.00 - 12.00 - Lab: Unsupervised Omics integration through MOFA2

12.00 - 13.00 - Lunch

13.00 - 13.20 - Lab recap (Nikolay Oskolkov)

13.20 - 14.00 - Multi-Omics Factor Analysis - MOFA (Ricard Argelaget)

Bonus Materials: mofa

14.00 - 14.05 - Break

14.05 - 15.20 - Lab: MOFA for personalized medicine (Ricard Argelaget)

15.20 - 15.30 - Break

15.30 - 17.00 - Assisted exercises

Notebook 1: Lab: Unsupervised Omics integration through MOFA2
Notebook 2: Lab: MOFA for personalized medicine

Tuesday 07/09/2021

09.00 - 10.00 - Data mining and Discriminant analysis with PLS methods (Kim-Ahn Le Cao)

10.00 - 10.05 - Break

10.05 - 10.30 - Lab demo: application of PLS to a microarray data set (Kim-Ahn Le Cao)

10.30 - 11.35 - Lab: Application to a microarray data set (Kim-Ahn Le Cao)

Bonus materials

11.35 - 11.40 - Break

11.40 - 12.15 - Deep Learning for Omics integration (Nikolay Oskolkov)

12.15 - 13.00 - Lunch

13.00 - 13.55 - Lab: Deep Learning for Omics integration (Nikolay Oskolkov)

Bonus materials

13.55 - 14.05 - Break

14.05 - 15.00 - UMAP for data integration (Nikolay Oskolkov)

15.00 - 15.30 - Lab: UMAP for data integration (Nikolay Oskolkov)

15.30 - 17.00 - Assisted exercises

Notebook 1: PLSDA application to a microarray data set
Notebook 2: Deep Learning for Omics integration
Notebook 3: UMAP for data integration
Bonus: dimensionality reduction
Bonus: data integration at single-cell level

Wednesday 08/09/2021

09.00 - 09.30 - Introduction to biological network analysis (Rui Benfeitas)

09.30 - 10.00 - Network inference and analysis (Rui Benfeitas)

10.00 - 10.05 - Break

10.05 - 10.55 - Community analysis (Rui Benfeitas)

10.55 - 11.00 - Break

11.20 - 12.00 - Lab: Network analysis (Rui Benfeitas)

12.00 - 13.00 - Lunch

13.00 - 14.55 - Computational approaches to study kinase signalling networks (Pedro Beltrao)

14.55 - 15.00 - Break

15.00 - 16.00 - Open seminar: Network based analysis of 1002 GWAS study defines a pleiotropy map of human cell biology (Pedro Beltrao)

16.00 - 17.00 - Lab recap (Rui Benfeitas)

Notebook 1: Network construction and analysis of a transcriptomic and metabolomic dataset
Bonus: Feature selection
Bonus: Data pre-processing in python

Thursday 09/09/2021

09.00 - 09.40 - Non-negative Matrix Factorization (Sergiu Netotea)

09.40 - 09.45 - Break

09.45 - 10.40 - Similarity Network Fusion (Sergiu Netotea)

10.40 - 10.45 - Break

10.45 - 11.50 - Hands-on: SNF and NMF (Sergiu Netotea)

Lab NMF

Lab SNF

11.50 - 12.10 - Lab recap (Sergiu Netotea)

12.10 - 13.00 - Lunch

13.00 - 14.00 - Constraint-based modeling of metabolism (Nikolaus Sonnenschein)

14.00 - 14.05 - Break

14.05 - 15.00 - Lab: constraint-based modeling of metabolism (in Python with cobrapy) (Nikolaus Sonnenschein)

15.00 - 17.00 - Assisted exercises

Notebook 1: Non-negative matrix factorizattion
Notebook 2: Similarity network fusion
Notebook 3: Constraint-based modeling of metabolism (in Python with cobrapy)

Friday 10/09/2021

09.00 - 09.30 - Gene set analysis and reporter features (Ashfaq Ali)

09.30 - 09.35 - Break

09.35 - 10.05 - Network Meta-analysis (Ashfaq Ali)

10.05 - 10.15 - Break

10.15 - 11.40 - GSA, reporter features and network meta-analysis (Ashfaq Ali)

11.40 - 12.15 - Lab Recap (Ashfaq Ali)

12.15 - 13.00 - Lunch

13.00 - 13.55 - Open seminar: Metabolic Atlas: genome-scale metabolic models for easy browsing and analysis (Mihail Anton)

13.55 - 14.00 - Break

14.00 - 15.00 - Open seminar: Network-based integration and visualization of large-scale data (Lars Juhl Jensen)

15.00 - 15.30 - Discussions and course end

Additional materials

The following content is presented as stand-alone resources and not presented during the course.

Multi-omics data management in R

Visualization with Cytoscape in R

Circos & Hive visualization in R

Additional reading materials