Summary
Last updated on 2026-10-13 | Edit this page
Estimated time: 10 minutes
Overview
Questions
- What did we learn about our study?
- How do I choose an integration method?
- Where can I learn more?
Objectives
- Summarize the integration workflow of this lesson.
- Choose a group of integration methods for a research question.
- Find resources for further learning.
What we did
We integrated three data types of 40 rats, step by step:
| Episode | What we learned about the study |
|---|---|
| MultiAssayExperiment | The three data types fit into one object. Fat in the diet has a large effect on the microbiota and the metabolites; XOS a small one. |
| Exploratory | The microbiota and the metabolites are related, but weakly. Glucose, butyrate and nicotinate are most strongly linked to the microbiota. Much of the association comes from the diet. |
| Unsupervised | The main pattern that all three data types share is the fat in the diet (joint RPCA, MOFA factor 1). No pattern matches XOS. |
| Supervised | Every data type predicts the fat in the diet almost perfectly, but XOS is hard to predict. DIABLO selects a few genera and metabolites that tell the diets apart. |
The same features came up again and again with different methods. Results that are consistent across methods are more convincing.
Which method should I use?
To choose a method for your own study, start from the research question:
- Are my data types related at all, and which features go together? Exploratory methods: a Mantel test, then MiRKAT and pairwise associations for the details.
- What are the main patterns in my data? Unsupervised methods, such as joint RPCA and MOFA.
- Can I predict an outcome, and which features predict it? Supervised methods, such as IntegratedLearner and DIABLO. Always evaluate them with cross-validation or an independent dataset.
In every case, prepare each data type carefully, look at each of them alone first, and remember that a factor shared by the data types, such as the diet, creates associations between them.
Where to go next
To continue learning, see the OMA book chapters on multi-omics, cross-association, multi-assay ordination and machine learning, the ECCB 2026 hologenomics tutorial, which applies the same methods to taxa and pathways of a colorectal cancer study, and the training slides of the OMA framework.
- Exploratory methods test whether data types are related, unsupervised methods find shared patterns, and supervised methods predict an outcome.
- Choose the group of methods by the research question, and look for results that are consistent across methods.
- Integrating data types does not create a signal that is not in the data.