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pySuStaIn: a Python implementation of the Subtype and Stage Inference algorithm

View ORCID ProfileLeon M. Aksman, View ORCID ProfilePeter A. Wijeratne, View ORCID ProfileNeil P. Oxtoby, View ORCID ProfileArman Eshaghi, View ORCID ProfileCameron Shand, View ORCID ProfileAndre Altmann, View ORCID ProfileDaniel C. Alexander, Alexandra L. Young
doi: https://doi.org/10.1101/2021.06.09.447713
Leon M. Aksman
1Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California
2Centre for Medical Image Computing, Departments of Computer Science and Medical Physics, University College London
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  • For correspondence: leon.aksman@loni.usc.edu
Peter A. Wijeratne
2Centre for Medical Image Computing, Departments of Computer Science and Medical Physics, University College London
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Neil P. Oxtoby
2Centre for Medical Image Computing, Departments of Computer Science and Medical Physics, University College London
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Arman Eshaghi
2Centre for Medical Image Computing, Departments of Computer Science and Medical Physics, University College London
3Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London
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Cameron Shand
2Centre for Medical Image Computing, Departments of Computer Science and Medical Physics, University College London
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Andre Altmann
2Centre for Medical Image Computing, Departments of Computer Science and Medical Physics, University College London
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Daniel C. Alexander
2Centre for Medical Image Computing, Departments of Computer Science and Medical Physics, University College London
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Alexandra L. Young
4Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London
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Abstract

Progressive disorders are highly heterogeneous. Symptom-based clinical classification of these disorders may not reflect the underlying pathobiology. Data-driven subtyping and staging of patients has the potential to disentangle the complex spatiotemporal patterns of disease progression. Tools that enable this are in high demand from clinical and treatment-development communities. Here we describe the pySuStaIn software package, a Python-based implementation of the Subtype and Stage Inference (SuStaIn) algorithm. SuStaIn unravels the complexity of heterogeneous diseases by inferring multiple disease progression patterns (subtypes) and individual severity (stages) from cross-sectional data. The primary aims of pySuStaIn are to enable widespread application and translation of SuStaIn via an accessible Python package that supports simple extension and generalization to novel modelling situations within a single, consistent architecture.

Code metadata Current code version v1.0

Permanent link to code/repository used of this code version https://github.com/ucl-pond/pySuStaIn

Legal Code License MIT

Code versioning system used git

Software code languages, tools, and services used Python

Compilation requirements, operating environments & dependencies Linux, Mac, Windows

Support email for questions leon.aksman{at}loni.usc.edu, p.wijeratne{at}ucl.ac.uk, alexandra.young{at}kcl.ac.uk

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* Joint first authors

  • https://github.com/ucl-pond/pySuStaIn

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted June 10, 2021.
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pySuStaIn: a Python implementation of the Subtype and Stage Inference algorithm
Leon M. Aksman, Peter A. Wijeratne, Neil P. Oxtoby, Arman Eshaghi, Cameron Shand, Andre Altmann, Daniel C. Alexander, Alexandra L. Young
bioRxiv 2021.06.09.447713; doi: https://doi.org/10.1101/2021.06.09.447713
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pySuStaIn: a Python implementation of the Subtype and Stage Inference algorithm
Leon M. Aksman, Peter A. Wijeratne, Neil P. Oxtoby, Arman Eshaghi, Cameron Shand, Andre Altmann, Daniel C. Alexander, Alexandra L. Young
bioRxiv 2021.06.09.447713; doi: https://doi.org/10.1101/2021.06.09.447713

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