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Patient subtypes: real or not?
(Clustering, you’re doing it wrong)
Paul Agapow
Data Science Institute
<p.agapow@ic.ac.uk>
@agapow
March 2018
Precision medicine
Patient assignment is a clustering
problem
Precision medicine
(assign patients to clusters)
Translational medicine
(infer clusters from patients)
Clustering requires many decisions
• Similarity
• Group boundaries
• Spectra
• Ground truth
• Noise / non-clustered
• Resolution / cut-offs
• Uninteresting clusters
• Stable & robust
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)
Example: Diabetes
• 6 clinical vars
produce 5
clusters
• Validated in
other
datasets
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
Example: Asthma
• Complex & heterogeneous
• Many attempts to stratify: 3,
4, 6+ clusters
Are asthma clusters real?
• Use multiple
methods &
multiple
datasets to
validate
• Compare nested
clusters with
homogeneity &
completeness
Are all asthma clusters
equally real?
• Requires many more genes to id
TAC3a & b than 1 or 2
• Because different clusters cluster
differently
Biclustering
• Better
approach?
• Simultaneously
group samples
& features
• Relieves
assumption of
clustering
everything
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
Or ...
Clustering methods are hypothesis
generators, cluster partitions are
hypotheses and need to be validated or
proven to be useful.
Thanks
• Nazanin Kermani
• Mansoor Saqi
• Axel Oehmichen

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Patient subtypes: real or not?

  • 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
  • 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