Probabilistic inference of bifurcations in single-cell data using a hierarchical mixture of factor analysers
Probabilistic inference of bifurcations in single-cell data using a hierarchical mixture of factor analysers
复制标题
使用层次混合因子分析器对单细胞数据中的分岔进行概率推断
DOI：
10.1101/076547
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Campbell K
中科院分区：
文献类型：
--
作者：
Campbell K
对单细胞转录组数据中的分叉进行建模已成为一个日益流行的研究领域。已经提出了几种从这些数据中推断分叉结构的方法，但都依赖于启发式的非概率推理。在这里，我们提出了第一个生成性的，完全概率的模型，用于这种推断，基于贝叶斯因素分析器的分层混合。我们的模型在大数据集上表现出了具有竞争力的性能，尽管我们实现了完全的MCMC采样，并且其独特的分层先验结构使得能够自动确定驱动分支过程的基因。
Modelling bifurcations in single-cell transcriptomics data has become an increasingly popular field of research. Several methods have been proposed to infer bifurcation structure from such data but all rely on heuristic non-probabilistic inference. Here we propose the first generative, fully probabilistic model for such inference based on a Bayesian hierarchical mixture of factor analysers. Our model exhibits competitive performance on large datasets despite implementing full MCMC sampling and its unique hierarchical prior structure enables automatic determination of genes driving the bifurcation process.