Cell lineage analysis: Variance Components Models for Dependent Cell Populations
作者:
RichardM. Huggins,
RobertG. Staudte,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1994)
卷期:
Volume 89,
issue 425
页码: 19-29
ISSN:0162-1459
年代: 1994
DOI:10.1080/01621459.1994.10476442
出版商: Taylor & Francis Group
关键词: Bifurcating autoregression model;Maximum likelihood;Repeated measures;Robust estimation;Variance components model
数据来源: Taylor
摘要:
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.
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