Spatial Mixture Modelling for Unobserved Point Processes: Examples in Immunofluorescence Histology
[作者] Chunlin Ji Daniel Merl Thomas B. Kepler Mike West
[单位] Department of Statistical Science, Duke University, Durham, NC , mailto Department of Immunology, Duke University Medical Center
[摘要] We discuss Bayesian modelling and computational methods in analysis of indirectly observed spatial point processes. The context involves noisy measurements on an underlying point process that p…
[关键词] Bayesian computation blocked Gibbs sampler Dirichlet process mixture model inhomogeneous Poisson process
We discuss Bayesian modelling and computational methods in analysis of indirectly observed spatial point processes. The context involves noisy measurements on an underlying point process that provide indirect and noisy data on locations of point outcomes. We are interested in problems in which the spatial intensity function may be highly heterogenous, and so is modelled via flexiblenonparametric Bayesian mixture models. Analysis aims to estimate the underlying intensity function and the abundance of realized but unobserved points. Our motivating applications involve immunological studies of multiple fluorescent intensity images in sections of lymphatic tissue where the point processes represent geographical configurations of cells. We are interested in estimating intensity functions and cell abundance for each of a series of such data sets to facilitate comparisons of outcomes at different times and with respect to differing experimental conditions. The analysis is heavily computational, utilizing recently introduced MCMC approaches for spatial point process mixtures and extending them to the broader new context here of unobserved outcomes. Further, our example applications are problems in which the individual objects of interest are not simply points, but rather small groups of pixels; this implies a need to work at an aggregate pixel region level and we develop the resulting novel methodology for this. Two examples with with immunofluorescence histology data demonstrate the models and computational methodology.
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原文发布时间:2009/9/24
引用本文:
Chunlin Ji;Daniel Merl;Thomas B. Kepler;Mike West.Spatial Mixture Modelling for Unobserved Point Processes: Examples in Immunofluorescence Histology.http://kmmc.firstlight.cn/View.aspx?infoid=807707&cb=wangzhijuanxg.
发布时间:2009/9/24.检索时间:2024/12/15