Presented at AI & Machine Learning for Computational Medicine (Imperial College London, March 2018)

1. Patient subtypes: real or not?
(Clustering, you’re doing it wrong)
Paul Agapow
Data Science Institute
<p.agapow@ic.ac.uk>
@agapow
March 2018

2. Precision medicine

3. Patient assignment is a clustering
problem
Precision medicine
(assign patients to clusters)
Translational medicine
(infer clusters from patients)

4. Clustering requires many decisions
• Similarity
• Group boundaries
• Spectra
• Ground truth
• Noise / non-clustered
• Resolution / cut-offs
• Uninteresting clusters
• Stable & robust

5. So are clusters real?
Every dataset contains clusters, with
different set of clusters being revealed by
different methods, but not all of these
clusters are real or interesting or
meaningful.
(Paraphrased from Christian Hennig)

6. Example: Diabetes
• 6 clinical vars
produce 5
clusters
• Validated in
other
datasets

7. But!
After van Smeden,
Harrel, Dahly:
• Dimension reduce 6
to 5 only?
• 2 related random
vars can generate 6
“clusters”
• Not validated in
other data types

8. Example: Asthma
• Complex & heterogeneous
• Many attempts to stratify: 3,
4, 6+ clusters

9. Are asthma clusters real?
• Use multiple
methods &
multiple
datasets to
validate
• Compare nested
clusters with
homogeneity &
completeness

10. Are all asthma clusters
equally real?
• Requires many more genes to id
TAC3a & b than 1 or 2
• Because different clusters cluster
differently

11. Biclustering
• Better
approach?
• Simultaneously
group samples
& features
• Relieves
assumption of
clustering
everything

12. Take home
• There are many ways to cluster &
thus many clusters
• Lots of different ways to be a cluster,
even in same dataset
• Too easy to fish
• Validate with other data types

13. Or ...
Clustering methods are hypothesis
generators, cluster partitions are
hypotheses and need to be validated or
proven to be useful.

14. Thanks
• Nazanin Kermani
• Mansoor Saqi
• Axel Oehmichen