Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK.
Bioinformatics. 2009 Nov 1;25(21):2824-30. doi: 10.1093/bioinformatics/btp456. Epub 2009 Jul 23.
Human pluripotent stem cell lines persist in culture as a heterogeneous population of SSEA3 positive and SSEA3 negative cells. Tracking individual stem cells in real time can elucidate the kinetics of cells switching between the SSEA3 positive and negative substates. However, identifying a cell's substate at all time points within a cell lineage tree is technically difficult.
A variational Bayesian Expectation Maximization (EM) with smoothed probabilities (VBEMS) algorithm for hidden Markov trees (HMT) is proposed for incomplete tree structured data. The full posterior of the HMT parameters is determined and the underflow problems associated with previous algorithms are eliminated. Example results for the prediction of the types of cells in synthetic and real stem cell lineage trees are presented.
The Matlab code for the VBEMS algorithm is freely available at http://www.acse.dept.shef.ac.uk/repository/vbems_lineage_tree/VBEMS.ZIP CONTACT: visakan@sheffield.ac.uk
Supplementary data are available at Bioinformatics online.
人类多能干细胞系在培养中作为 SSEA3 阳性和 SSEA3 阴性细胞的异质群体持续存在。实时跟踪单个干细胞可以阐明细胞在 SSEA3 阳性和阴性亚状态之间转换的动力学。然而，在细胞谱系树中的所有时间点识别细胞的亚状态在技术上是困难的。
提出了一种用于不完整树状结构数据的隐马尔可夫树（HMT）的变分贝叶斯期望最大化（EM）与平滑概率（VBEMS）算法。确定了 HMT 参数的完整后验概率，并消除了与先前算法相关的下溢问题。呈现了用于预测合成和真实干细胞谱系树中细胞类型的示例结果。
VBEMS 算法的 Matlab 代码可在 http://www.acse.dept.shef.ac.uk/repository/vbems_lineage_tree/VBEMS.ZIP 上免费获得。
补充数据可在生物信息学在线获得。
