Clustering of samples with a tree-shaped dependence structure, with an application to microscopic time lapse imaging
| Autor: | Don Poron, Anne Krug, Sebastian Dümcke, Henrik Failmezger, Timm Schroeder, Max Endele, Achim Tresch, Ezgi Dursun |
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| Rok vydání: | 2017 |
| Předmět: |
Statistics and Probability
Dependency (UML) Cell division Computer science Cell Cell fate determination Biochemistry Hematopoietic progenitor cell differentiation Time-Lapse Imaging 03 medical and health sciences medicine Cluster Analysis Progenitor cell Cluster analysis Hidden Markov model Molecular Biology 030304 developmental biology 0303 health sciences Models Statistical business.industry Event (computing) 030302 biochemistry & molecular biology Statistical model Pattern recognition Computer Science Applications Computational Mathematics medicine.anatomical_structure Computational Theory and Mathematics Programming Languages Artificial intelligence business Software |
| Zdroj: | Bioinformatics (Oxford, England). 35(13) |
| ISSN: | 1367-4811 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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