Intro & practicals

Multi-omic data science with R/Bioconductor

Oulu summer school, June 19-21, 2023

Welcome!

Organizers

  • Health and Biosciences Doctoral Programme University of Oulu Graduate School
  • Cancer & Translational Medicine Research Unit, University of Oulu
  • Department of Computing, University of Turku, Finland

Finnish IT Center for Science (CSC) provides cloud computing services

Local organizers

  • Justus Reunanen, docent, University of Oulu

  • Anna Kaisanlahti, doctoral researcher, University of Oulu

Teachers / Facilitators

  • Leo Lahti, associate prof. in data science

  • Pande Putu Erawijantari, postdoc

  • Tuomas Borman, doctoral researcher

  • Giulio Benedetti, scientific programmer

Department of Computing, Uni. Turku, Finland datascience.utu.fi  

Code of Conduct

Bioconductor community values an open approach to science that promotes

  • sharing of ideas, code, software and expertise

  • collaboration

  • diversity and inclusivity

  • a kind and welcoming environment

  • community contributions

By participating in this community, you agree not to engage in behavior contrary to these values at any Bioconductor-sponsored event or electronic communication channel.

For the full CoC, see: https://bioconductor.github.io/bioc_coc_multilingual

Target audience

  • Focus on multi-omic data integration, with emphasis on microbiome research

  • Advanced MSc, PhD & postdoctoral researchers who wish to learn new skills in scientific programming and multi-omic data analysis

  • Earlier experience with R is expected

  • Participants & wishlists

Learning goals

Introduction to multi-omic data integration and analysis with R/Bioconductor, a popular open-source environment for life science informatics.

After the course, you will know how to:

  • organize multiple data sources into a coherent data science framework

  • implement open & reproducible data science workflows

  • approach common data analysis tasks by utilizing available documentation and R tools

Moreno-Indias et al. (2021) Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions. Frontiers in Microbiology.

Primary focus: microbiome research; methods are generally applicable to transcriptomics, metabolomics, single cell sequencing and other omics integration.

Schedule

  • Day 1: open data science framework

  • Day 2: tabular data analysis (single omics)

  • Day 3: multi-table data analysis (multi-omics)

Schedule

Session types

  • Lectures, demonstrations don’t hesitate to ask questions!

  • Practicals Solve tasks by taking advantage of the online examples and resources that are pointed out in the material. There is often more than one way to solve a given task.

  • Additional exercises & example data sets, supporting online material, many ways to solve a given task

  • Presentations, present your solutions, highlight questions and challenges, engage the audience

Teaching material

  • Teaching follows the open online book (beta version) created by the course teachers, Orchestrating Microbiome Analysis.

  • The openly licensed teaching material, exercises and slides will be available online during and after the course.

Support

Other points

  • Basic vs. advanced groups
  • Working with your own data?

If you need a small break, take it!

Acknowledgments

Development work has received support from several sources.

Learning goals for today

Broader context and practical skills on:

  • Open data science workflow: setting up reproducible data science workflows with Quarto

  • Data containers: understanding SummarizedExperiment

  • Basic data wrangling (e.g. subsetting, aggregation)

Questions?

Moreno-Indias et al. (2021) Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions. Frontiers in Microbiology.