Orchestrating Microbiome Analysis with Bioconductor

Leiden, October 2026

Leo Lahti, Tuomas Borman

2026-10-04

Orchestrating Microbiome Analysis with Bioconductor

TNO, Leiden, Oct 5–7, 2026 (Mon–Wed)
Daily: ~9:00–16:00 (lectures + demos + hands-on)

mia logo Bioconductor logo

Code of Conduct

Bioconductor community values:

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

Full CoC: https://bioconductor.github.io/bioc_coc_multilingual/

Course overview

The course provides a gentle introduction to microbiome and multi-omic data science with R/Bioconductor, focusing on:

  • reproducible data analysis workflows
  • standardized data containers for biomedical data integration
  • how to approach new analysis tasks using documentation + existing tools

Learning goals

After the course, you will be able to:

  • use Bioconductor data infrastructure for omics data analysis
  • work with standardized containers for single-omics and multi-omics
  • build reproducible workflows with Quarto
  • continue learning independently using the OMA documentation

Practicalities

  • Each topic: short lecture + demonstration
  • Majority of time: hands-on exercises
  • Help available during exercises
  • Materials remain available after the course

Schedule overview

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)

Day 1 — Open data science

Day 1 — Open data science

Time Activity
9:00–10:00 Coffee, welcome & practicalities
10:00–11:00 Introduction to Quarto notebook
11:00–12:00 Lecture: open & reproducible workflows
12:00–13:00 Lunch break
13:00–16:00 Working with data containers and workflows

Learning goals

  • Understand principles of open & reproducible microbiome/omics analysis
  • Become familiar with the Bioconductor ecosystem
  • Learn the role of standardized data containers for omics data

Slides

Exercises

Day 1 — Wrap-up

  • Reproducible & transparent reporting: makes analyses verifiable and easier to reuse, review, and extend (for you and others)
  • TreeSummarizedExperiment: standard container for microbiome data
  • Bioconductor provides interoperable packages built on shared data infrastructure

Feedback

https://forms.gle/iEkoNdMVp9BUnyqNA

Day 2 — Tabular data analysis

Day 2 — Tabular data analysis

Time Activity
9:00–10:00 Lecture: analysis & visualization of tabular data (single omics)
10:00–12:00 Subsetting, transformations, and data summaries
12:00–13:00 Lunch break
13:00–14:00 Univariate data analysis and visualization
14:00–16:00 Multivariate data analysis and visualization

Learning goals

Calculate, visualize, and interpret

  • alpha diversity
  • beta diversity
  • differential abundance/prevalence

Slides

Exercises

Day 2 — Wrap-up

  • Alpha diversity: mia::addAlpha() + miaViz::plotBoxplot()
  • Beta diversity ordination: mia::addMDS()/mia::addRDA() + miaViz::plotOrdination()/miaViz::plotRDA()
  • Differential abundance analysis: maaslin3::maaslin3()

Feedback

https://forms.gle/iEkoNdMVp9BUnyqNA

Day 3 — Multi-assay data integration

Day 3 — Multi-assay data integration

Time Activity
9:00–10:00 Lecture: analysis & visualization of multi-assay data (multi-omics)
10:00–12:00 Multi-assay data analysis and visualization
12:00–13:00 Lunch break
13:00–15:00 Q&A and advanced techniques (time series, machine learning, simulation)
15:00–16:00 Summary and wrap-up

Learning goals

  • Import multiomics data into MultiAssayExperiment
  • Analyse and visualize multiomics data

Feedback

https://forms.gle/rHTTNqvr9jsRQkRg6

Channels

  • Bioconductor community chat Zulip
  • GitHub issues
  • Email?

Acknowledgements

UTU logo mia logo Bioconductor logo

References