Spatial Mixture Modelling for Unobserved Point Processes: Examples in Immunofluorescence Histology.
Spatial Mixture Modelling for Unobserved Point Processes: Examples in Immunofluorescence Histology.
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DOI：
10.1214/09-ba411
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发表时间：
2009-12-04
影响因子：
4.4
通讯作者：
West M
中科院分区：
文献类型：
--
作者：
Ji C;Merl D;Kepler TB;West M
我们讨论了贝叶斯建模和计算方法在分析间接观测空间点过程。上下文涉及对潜在点过程的噪声测量，该过程提供关于点结果位置的间接和噪声数据。我们对空间强度函数可能是高度异质的问题感兴趣，因此通过灵活的非参数贝叶斯混合模型进行建模。分析的目的是估计潜在的强度函数和已实现但未观测到的点的丰度。我们的激励应用涉及淋巴组织切片的多重荧光强度图像的免疫学研究，其中点过程代表细胞的地理构型。我们感兴趣的是估计每一系列这样的数据集的强度函数和细胞丰度，以促进在不同时间和不同实验条件下的结果比较。分析是大量计算的，利用最近引入的MCMC方法进行空间点过程混合，并将其扩展到更广泛的新背景下的未观察到的结果。此外，我们的示例应用程序是这样的问题，其中感兴趣的单个对象不是简单的点，而是小组像素；这意味着需要在聚合像素区域级别工作，我们为此开发了由此产生的新方法。两个具有免疫荧光组织学数据的例子演示了模型和计算方法。
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 flexible nonparametric 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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影响因子：
0.9
作者：
Kottas, Athanasios;Sanso, Bruno
通讯作者：
Sanso, Bruno
DOI：
10.1198/016214501750332758
发表时间：
2001-03-01
影响因子：
3.7
作者：
Ishwaran, H;James, LF
通讯作者：
James, LF
DOI：
10.1080/03610919408813196
发表时间：
1994-01-01
影响因子：
0.9
作者：
MACEACHERN, SN
通讯作者：
MACEACHERN, SN
影响因子：
2.7
作者：
Wolpert, RL;Ickstadt, K
通讯作者：
Ickstadt, K
影响因子：
2.4
作者：
MacEachern, SN;Muller, P
通讯作者：
Muller, P