Journal article
Clustering of samples with a tree-shaped dependence structure, with an application to microscopic time lapse imaging.
- Failmezger H Department of Molecular Pathology, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
- Dursun E Department of Medicine, Institute for Immunology, Biomedical Center, Ludwig-Maximilians-University Munich, Martinsried, Germany.
- Dümcke S Department of medicine, Institute of Medical Statistics and Computational Biology, University Hospital Cologne, Cologne, Germany.
- Endele M Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
- Poron D Department of medicine, Institute of Medical Statistics and Computational Biology, University Hospital Cologne, Cologne, Germany.
- Schroeder T Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
- Krug A Department of Medicine, Institute for Immunology, Biomedical Center, Ludwig-Maximilians-University Munich, Martinsried, Germany.
- Tresch A Department of medicine, Institute of Medical Statistics and Computational Biology, University Hospital Cologne, Cologne, Germany.
- 2018-11-20
Published in:
- Bioinformatics (Oxford, England). - 2019
English
MOTIVATION
Recent imaging technologies allow for high-throughput tracking of cells as they migrate, divide, express fluorescent markers and change their morphology. The interpretation of these data requires unbiased, efficient statistical methods that model the dynamics of cell phenotypes.
RESULTS
We introduce treeHFM, a probabilistic model which generalizes the theory of hidden Markov models to tree structured data. While accounting for the entire genealogy of a cell, treeHFM categorizes cells according to their primary phenotypic features. It models all relevant events in a cell's life, including cell division, and thereby enables the analysis of event order and cell fate heterogeneity. Simulations show higher accuracy in predicting correct state labels when modeling the more complex, tree-shaped dependency of samples over standard HMM modeling. Applying treeHFM to time lapse images of hematopoietic progenitor cell differentiation, we demonstrate that progenitor cells undergo a well-ordered sequence of differentiation events.
AVAILABILITY AND IMPLEMENTATION
The treeHFM is implemented in C++. We provide wrapper functions for the programming languages R (CRAN package, https://CRAN.R-project.org/package=treeHFM) and Matlab (available at Mathworks Central, http://se.mathworks.com/matlabcentral/fileexchange/57575-treehfml).
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Recent imaging technologies allow for high-throughput tracking of cells as they migrate, divide, express fluorescent markers and change their morphology. The interpretation of these data requires unbiased, efficient statistical methods that model the dynamics of cell phenotypes.
RESULTS
We introduce treeHFM, a probabilistic model which generalizes the theory of hidden Markov models to tree structured data. While accounting for the entire genealogy of a cell, treeHFM categorizes cells according to their primary phenotypic features. It models all relevant events in a cell's life, including cell division, and thereby enables the analysis of event order and cell fate heterogeneity. Simulations show higher accuracy in predicting correct state labels when modeling the more complex, tree-shaped dependency of samples over standard HMM modeling. Applying treeHFM to time lapse images of hematopoietic progenitor cell differentiation, we demonstrate that progenitor cells undergo a well-ordered sequence of differentiation events.
AVAILABILITY AND IMPLEMENTATION
The treeHFM is implemented in C++. We provide wrapper functions for the programming languages R (CRAN package, https://CRAN.R-project.org/package=treeHFM) and Matlab (available at Mathworks Central, http://se.mathworks.com/matlabcentral/fileexchange/57575-treehfml).
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
- Language
-
- English
- Open access status
- closed
- Identifiers
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- DOI 10.1093/bioinformatics/bty939
- PMID 30452534
- Persistent URL
- https://sonar.ch/global/documents/93882
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