Tutorial 4: A practical introduction to multi-omics integration and network analysis

DOI Github repository

This tutorial is a shorter version of the workshop in Omics Integration and Systems Biology of the National Bioinformatics Infrastructure Sweden. Please refer to that workshop for more information.


Please check the preparation instructions. If you are unable to complete all installations you will nevertheless be able to follow all exercises through the respective html files. The schedule contains links to all lectures and notebooks.

16/07/2021: Note the updated installation instructions based on Docker instead of Conda.


Please write them on the HackMD


Thursday, July 22, 11:00 - 15:00 UTC
Friday, July 23, 11:00 - 15:00 UTC


Rui Benfeitas, Science for Life Laboratory, Stockholm University
Nikolay Oskolkov, Science for Life Laboratory, Lund University
Ashfaq Ali, Science for Life Laboratory, Lund University


Advances in next generation sequencing (NGS) and mass spectrometry have recently allowed us to probe deeper and systematically into different layers of biological information flow. We can now capture snapshots of cellular states at single-cell or tissue levels on genomic, transcriptomic, metabolomic, and proteomic levels, to examine relationships between thousands of features in each of these omics and a given phenotype or disease. However, characterization beyond individual omic levels to understand how multi-omic relationships jointly relate with a given phenotype remains a challenge. How may identify the features with the largest phenotypic impact, and how can we identify patterns among the different layers?

In this tutorial we will introduce several different approaches for integration of multi-omics data including supervised and unsupervised learning and network analyses. We will highlight some of the key issues in dealing with the high multidimensionality that characterizes multi-omic data and techniques to address them. We will also discuss some of the most successful methods for multi-omic data abstraction, and how machine learning approaches can be used in unraveling biological relationships. We will show how biological network analyses can be used to identify patterns within and between omics, and how communities of features may be related with phenotypic data and biologic functions. Finally, we will discuss how meta-analyses and network meta-analyses can be used in analyzing studies from independent experiments.

Learning Objectives
  1. Identify common issues in integration of highly multidimensional omics data.
  2. Identify key methods for data integration through supervised and unsupervised machine learning approaches.
  3. Understand how biological network analysis may assist in identifying coordinated patterns between features and associating feature communities with phenomic and biological functions.
  4. Hands-on experience in supervised/unsupervised integration and biological network analysis.
Audience and level

Aimed at bioinformaticians and computational biologists with experience in analysis of high throughput data and basic statistics knowledge, with R or Python coding experience. Knowledge of machine learning techniques is advantageous. Hands-on sessions will comprise both R and Python coding.