Oulu Summer School

Multi-omic data analysis with R/Bioconductor

June 19-21, 2023

The course is organized by the Health and Biosciences Doctoral Programme (HBS-DP), University of Oulu Graduate School, Research Unit of Translational Medicine, University of Oulu. The Finnish IT Center for Science (CSC) supports the course by providing cloud computing services.

The course is organized in a live format. Preparatory material and video clips, and online support are available before the course. All teaching material will be shared openly.

Venue: University of Oulu. June 19-21, 2023 (Mon-Wed). The course is organized in a live format.

Costs: There is no registration fee for the course. Participants are expected to cover their own travel and accommodation.

Accommodation: Housing tips can be found at

Schedule: Contact teaching daily between 9am – 4pm, including lectures, demonstrations, hands-on sessions, and breaks.

  • Day 1 Reproducible workflows with R/Bioconductor and Quarto
  • Day 2 Tabular data analysis (working with single ’omics)
  • Day 3 Multi-assay data integration (multi-omics methods)

For a detailed schedule, see Section 2. The course can be extended by an independent assignment (details will be agreed with the main teacher).

For program & schedule, travel tips, registration, and other details, see the course homepage:

We are delighted to announce the programme of the EMBnet & GOBLET Annual General Meeting 2021, including the ML4Microbiome Symposium “Grand Challenges of Data-Intensive Science in microbiome & metagenome data analysis and training” and the “Workshop on Statistical and Machine Learning Techniques for Microbiome Data Analysis(respectively October 14th-15th, 2021).

Rooms for registration are still available up to October 8th.

Registration form

Full Programme


The ML4Microbiome Training School 2021 will be held virtually on 27-28 September & 4-7-8 October 2021!

Visit the Training school webpage, look at the programme and register. Registration is free and open to everybody who is interested in learning about Machine Learning applied to the analysis of microbiome data.


Microbiome biology

Microbiome sampling & wet-lab basics, study design

Metagenomic data analysis for human gut microbiota: statistical specificity of microbiome data

Statistical analysis of microbiome data

Compositional data analysis

Machine learning: basic concepts

Avoiding compositionality and absolute abundance profiling

Discussion on rarefying, clustering and data transformation (alr/clr/ilr)

Unsupervised learning: Basic approaches

Supervised machine learning: Methods and Model Quality Assessment

Hands-on sessions on Unsupervised & Supervised Machine Learning