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December 2019 Statistical inference for partially observed branching processes with application to cell lineage tracking of in vivo hematopoiesis
Jason Xu, Samson Koelle, Peter Guttorp, Chuanfeng Wu, Cynthia Dunbar, Janis L. Abkowitz, Vladimir N. Minin
Ann. Appl. Stat. 13(4): 2091-2119 (December 2019). DOI: 10.1214/19-AOAS1272


Single-cell lineage tracking strategies enabled by recent experimental technologies have produced significant insights into cell fate decisions, but lack the quantitative framework necessary for rigorous statistical analysis of mechanistic models describing cell division and differentiation. In this paper, we develop such a framework with corresponding moment-based parameter estimation techniques for continuous-time, multi-type branching processes. Such processes provide a probabilistic model of how cells divide and differentiate, and we apply our method to study hematopoiesis, the mechanism of blood cell production. We derive closed-form expressions for higher moments in a general class of such models. These analytical results allow us to efficiently estimate parameters of much richer statistical models of hematopoiesis than those used in previous statistical studies. To our knowledge, the method provides the first rate inference procedure for fitting such models to time series data generated from cellular barcoding experiments. After validating the methodology in simulation studies, we apply our estimator to hematopoietic lineage tracking data from rhesus macaques. Our analysis provides a more complete understanding of cell fate decisions during hematopoiesis in nonhuman primates, which may be more relevant to human biology and clinical strategies than previous findings from murine studies. For example, in addition to previously estimated hematopoietic stem cell self-renewal rate, we are able to estimate fate decision probabilities and to compare structurally distinct models of hematopoiesis using cross validation. These estimates of fate decision probabilities and our model selection results should help biologists compare competing hypotheses about how progenitor cells differentiate. The methodology is transferrable to a large class of stochastic compartmental and multi-type branching models, commonly used in studies of cancer progression, epidemiology and many other fields.


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Jason Xu. Samson Koelle. Peter Guttorp. Chuanfeng Wu. Cynthia Dunbar. Janis L. Abkowitz. Vladimir N. Minin. "Statistical inference for partially observed branching processes with application to cell lineage tracking of in vivo hematopoiesis." Ann. Appl. Stat. 13 (4) 2091 - 2119, December 2019.


Received: 1 May 2018; Revised: 1 March 2019; Published: December 2019
First available in Project Euclid: 28 November 2019

zbMATH: 07160932
MathSciNet: MR4037423
Digital Object Identifier: 10.1214/19-AOAS1272

Keywords: generalized method of moments , Markov jump processes , mechanistic modeling , stem cells , Stochastic compartmental models

Rights: Copyright © 2019 Institute of Mathematical Statistics


Vol.13 • No. 4 • December 2019
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