Lineage EM algorithm for inferring latent states from cellular lineage trees
Lineage EM algorithm for inferring latent states from cellular lineage trees
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DOI：
10.1093/bioinformatics/btaa040
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发表时间：
2020-05-01
期刊：
影响因子：
5.8
通讯作者：
Kobayashi, Tetsuya J.
中科院分区：
文献类型：
--
作者：
Nakashima, So;Sughiyama, Yuki;Kobayashi, Tetsuya J.
一个总结：细胞群体中的表型变异性可以作为细胞在不可预测变化的环境下的赌注对冲，其典型例子是细菌的持久性。为了理解控制这种现象的策略，识别每个细胞的表型及其遗传是必不可少的。虽然微流控技术的最新进展为我们提供了有用的谱系数据，但它们不足以直接识别细胞的表型。另一种方法是通过潜变量估计从谱系数据推断表型。然而，为了达到这个目的，我们必须解决从血统推断中的偏差问题，即生存偏差。在这项工作中，我们澄清了生存偏倚如何扭曲统计估计。然后，我们提出了一个潜在的变量估计算法没有生存偏差的世系树的基础上的期望最大化（EM）算法，我们称之为世系EM算法（LEM）。LEM提供了一种适用于各种谱系数据的识别细胞性状的统计方法。
A Summary: Phenotypic variability in a population of cells can work as the bet-hedging of the cells under an unpredictably changing environment, the typical example of which is the bacterial persistence. To understand the strategy to control such phenomena, it is indispensable to identify the phenotype of each cell and its inheritance. Although recent advancements in microfluidic technology offer us useful lineage data, they are insufficient to directly identify the phenotypes of the cells. An alternative approach is to infer the phenotype from the lineage data by latent-variable estimation. To this end, however, we must resolve the bias problem in the inference from lineage called survivorship bias. In this work, we clarify how the survivorship bias distorts statistical estimations. We then propose a latent-variable estimation algorithm without the survivorship bias from lineage trees based on an expectation-maximization (EM) algorithm, which we call lineage EM algorithm (LEM). LEM provides a statistical method to identify the traits of the cells applicable to various kinds of lineage data.