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The aim of this workshop is to provide an integrated view of data-driven hypothesis generation through machine learning, graph and network analysis as well as constraint-based modelling integration methods. A general description of different approaches for working with multiple layers of biological information, i.e. Omics data (e.g. transcriptomics and genomics) will be presented with some of the lectures discussing their advantages and pitfalls. The techniques will be discussed in terms of their rationale and applicability.

Covered topics

• Data pre-processing and cleaning prior to integration;
• Application of key machine learning methods for multi-omics analysis including deep learning;
• Multi-omics factor analysis,  dimension reduction and clustering;
• Biological network inference, community and topology analysis and visualization;
• Condition-specific and personalized modeling through Genome-scale Metabolic models for integration of transcriptomic, proteomic, metabolomic and fluxomic data;
• Identification of key biological functions and pathways;
• Identification of potential biomarkers and targetable genes through modeling and biological network analysis;
• Application of network approaches in meta-analyses;
• Similarity network fusion and matrix factorization techniques;
• Integrated data visualization techniques

Learning objectives

At the end of the course, students should:

• Identify key methods for analysis and integration of omics data based on a given dataset;
• Perform standard feature selection reduction techniques;
• Understand the differences and apply dimension reduction techniques;
• Understand strengths and pitfalls of key machine learning techniques in multi-omic analysis;
• Apply unsupervised and supervised data integration techniques;
• Build biological networks based on different omics data including integrated multi-omics networks;
• Perform centrality and community analyses in graphs;
• Apply network approaches in meta-analyses;
• Apply similarity network fusion of patient data;
• Compare different cell-types or conditions through the application of different biological network analysis techniques;
• Simulate biological functions using constraint-based models and flux balance analysis;
• Identify potential confounding factors and sources of bias.

Application

Course open for PhD students, postdocs, group leaders and core facility staff from Sweden and European institutions looking for an introduction to multi-omics integration and systems biology approaches. Please note that NBIS training events do not provide any formal university credits. If formal credits are crucial, the student needs to confer with the home department before submitting a course application in order to establish whether the course is valid for formal credits or not.

Applications are closed. Click here if you would like to be notified of the next course instance.

Fee This workshop has a fee of 3000kr and will be invoiced to the selected participants (Please note that NBIS cannot invoice individuals). Applications without complete invoice information will not be considered. Course fees cover all coffee breaks, all lunches and 1 course dinner.

Entry requirements

Practical exercises can be performed using R or Python, so we only accept students with previous experience in one of those programming languages. We will not discuss how to process specific omics, and the students are referred to other NBIS courses for this matter.

Required:

• Basic knowledge in R or Python;
• Basic understanding of frequentist statistics;
• A computer with web camera and Zoom.

Desirable:

• Experience with NGS and omics analysis
• Completing "Introduction to bioinformatics using NGS data" and "Introduction to biostatistics and machine learning" NBIS courses

Course staff

Nikolay Oskolkov (Lund University, course leader)
Rui Benfeitas (Stockholm University, course leader)
Ashfaq Ali (Lund University, course leader)
Sergiu Netotea (Chalmers University of Technology, course lecturer)
Paul Pyl (Lund University, TA)
Prasoon Agarwal (Lund University, TA)
Nima Rafati (Uppsala University, TA)
Payam Emami (Stockholm University, TA)

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