1 Overview

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1.1 Contents and learning goals

Contents and learning goals: This course provides an introduction to multi-omic data integration and analysis with R/Bioconductor, a popular open source environment for scientific data analysis. After the course you will know how to organize multiple data sources into a coherent framework, implement reproducible data science workflows, and approach common data analysis tasks by utilizing available documentation and R tools. The primary focus is on microbiome research but the covered data science methods are generally applicable and we will discuss links with other application domains such as transcriptomics, metabolomics, and single cell sequencing.

Target audience: MSc students, PhD, postdoctoral, and other researchers who wish to learn new skills in statistical programming and data analysis. Academic students and researchers from Finland and abroad are welcome and encouraged to apply.

Teaching material: We will follow open online documentation created by the course teachers, primarily the Orchestrating Microbiome Analysis (OMA) book. The training material walks you through the standard steps of omics data analysis covering data access, exploration, analysis, visualization, and reproducible workflows. Preparatory material and video clips, and online support are available before the course. All teaching material will be shared openly.

Figure source: Moreno-Indias et al. (2021) Frontiers in Microbiology 12:11.
Figure source: Moreno-Indias et al. (2021) Frontiers in Microbiology 12:11.

1.2 Schedule

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 https://visitoulu.fi/en/arrival-overnight/.

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).

1.3 How to apply

  • Send a brief motivation letter to Anna Kaisanlahti
  • Applications from local students, and applications sent before May 15 will be given priority
  • The course has maximum capacity of 20 participants.

1.4 Teachers and organizers

Teachers: Leo Lahti is the main teacher and Associate Professor in Data Science at the University of Turku. Dr. Pande Erawijantari is a co-teacher. Course assistants are Tuomas Borman (Turku), Giulio Benedetti (Turku), and Anna Kaisanlahti (Oulu). Docent Justus Reunanen is the course coordinator. The course is organized by 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.

1.5 Code of Conduct

The Bioconductor community values an open approach to science that promotes the

  • sharing of ideas, code, software and expertise
  • collaboration
  • diversity and inclusivity
  • a kind and welcoming environment
  • community contributions

More details on its enforcement are available here.

1.6 Acknowledgments

Citation: We thank all developers and contributors who have contributed open resources that supported the development of the training material. Kindly cite the course material as Tuomas Borman and Leo Lahti (2023).

Contact: Refer to https://microbiome.github.io.

License and source code:

All material is released under the open CC BY-NC-SA 3.0 License and available online during and after the course, following the recommendations on open teaching materials of the national open science coordination in Finland.

The source code of this repository is reproducible and contains the Rmd files with executable code. All files can be rendered at one go by running the file main.R. You can check the file for details on how to clone the repository and convert it into a gitbook, although this is not necessary for the training.


Tuomas Borman, and Leo Lahti. 2023. Multi-Omic Data Science with r/Bioconductor. microbiome.github.io.