Department of Molecular Pathology, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
Department of medicine, Institute of Medical Statistics and Computational Biology, University Hospital Cologne, Cologne, Germany.
Bioinformatics. 2019 Jul 1;35(13):2291-2299. doi: 10.1093/bioinformatics/bty939.
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.
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.
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 data are available at Bioinformatics online.
最近的成像技术允许对细胞的迁移、分裂、表达荧光标记物和改变形态进行高通量跟踪。这些数据的解释需要无偏、有效的统计方法，这些方法可以对细胞表型的动力学进行建模。
我们引入了 treeHFM，这是一种概率模型，它将隐马尔可夫模型的理论推广到树状结构的数据中。treeHFM 考虑了细胞的整个谱系，根据其主要表型特征对细胞进行分类。它对细胞生命中的所有相关事件进行建模，包括细胞分裂，从而能够分析事件顺序和细胞命运异质性。模拟结果表明，在对标准 HMM 建模更复杂的树状样本依赖性进行建模时，预测正确状态标签的准确性更高。将 treeHFM 应用于造血祖细胞分化的延时图像，我们证明祖细胞经历了一个有序的分化事件序列。
treeHFM 是用 C++实现的。我们为编程语言 R（CRAN 包，https://CRAN.R-project.org/package=treeHFM）和 Matlab（可在 Mathworks Central 获得，http://se.mathworks.com/matlabcentral/fileexchange/57575-treehfml）提供了封装函数。
补充数据可在生物信息学在线获得。
