Variance Components Models for Dependent Cell Populations
该文为培养细胞的家系数据建立方差分量模型，将双叉自回归模型嵌入允许测量误差和树间变异的框架，并给出稳健方法处理异常值。对41棵EMT6细胞树的分析显示，去除测量误差后姐妹细胞寿命几乎相同。
Cells grown in culture can be tracked for several generations and measurements taken on size or age at division and other cell characteristics. The observations for the offspring of each cell form a family tree of dependent data. Such cell lineage data are here modeled as repeated measurements on different family trees arising from individual ancestor cells selected at random from a population of cultured cells. The bifurcating autoregression model is embedded in a process that allows for measurement error and variation from tree to tree. Robust methods are presented that accommodate outliers in this time-dependent and branching environment while allowing the statistician to interactively build a variance components model for the process. The methodology is illustrated on a substantial data set of 41 trees of EMT6 cells, with the surprising conclusion that after removing measurement error, sister-cell lifetimes are nearly identical.