Statistical Inference in Partially Observed Stochastic Compartmental Models with Application to Cell Lineage Tracking of In-Vivo Hematopoiesis

• Activity Number: 33
• Type: Contributed
• Date/Time: Sunday, July 31, 2016 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Jason Xu* and Vladimir Minin and Peter Guttorp and Samson Koelle and Janis Abkowitz and Chuanfeng Wu and Cynthia Dunbar
• Companies: University of Washington and University of Washington and University of Washington and University of Washington and University of Washington and National Heart, Lung, and Blood Institute and National Heart, Lung, and Blood Institute
• Keywords: Branching processes ; Hematopoiesis ; Hidden Markov models ; Compartmental models ; Single-cell lineage tracking ; Cell ifferentiation

Abstract

Single-cell lineage tracking strategies enabled by recent experimental technologies have already produced significant insights into cell fate decisions, but lack the quantitative framework necessary for rigorous statistical tasks such as parameter estimation. In this paper, we develop such a framework with corresponding moment-based parameter estimation techniques for continuous-time stochastic compartmental models. We apply this to hematopoiesis, the complex mechanism of blood cell production. Our method enables efficient rate estimation in a much richer class of multi-compartment models than previous statistical studies of hematopoiesis, and provides the first rate estimation procedure to our knowledge for fitting such models to time series data generated from cellular barcoding experiments. We apply our estimator to hematopoiesis lineage tracking data in rhesus macaques. The methodology is transferrable to compartmental models and multi-type branching process models more broadly, commonly used in studies of cancer progression, epidemiology, and many other fields.