Henrik Failmezger1,2, Ezgi Dursun3, Sebastian Dümcke2, Max Endele4, Don Poron2, Timm Schroeder4, Anne Krug3, Achim Tresch2,5. 1. Department of Molecular Pathology, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK. 2. Department of medicine, Institute of Medical Statistics and Computational Biology, University Hospital Cologne, Cologne, Germany. 3. Department of Medicine, Institute for Immunology, Biomedical Center, Ludwig-Maximilians-University Munich, Martinsried, Germany. 4. Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland. 5. Department of Medicine, Center for Data and Simulation Science, University of Cologne, Cologne, Germany.
Abstract
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.
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.
Authors: Konstantinos Zormpas-Petridis; Henrik Failmezger; Shan E Ahmed Raza; Ioannis Roxanis; Yann Jamin; Yinyin Yuan Journal: Front Oncol Date: 2019-10-11 Impact factor: 6.244