Multicolor cell spatio-temporal image data have become important to investigate organ development and regeneration, malignant growth or immune responses by tracking different cell types both in vivo and in vitro. Statistical modeling of image data from common longitudinal cell experiments poses significant challenges due to the presence of complex spatio-temporal interactions between different cell types and difficulties related to measurement of single cell trajectories. Current analysis methods focus mainly on univariate cases, often not considering the spatio-temporal effects affecting cell growth between different cell populations. In this paper, we propose a conditional spatial autoregressive model to describe multivariate count cell data on the lattice, and develop inference tools. The proposed methodology is computationally tractable and enables researchers to estimate a complete statistical model of multicolor cell growth. Our methodology is applied on real experimental data where we investigate how interactions between cancer cells and fibroblasts affect their growth, which are normally present in the tumor microenvironment. We also compare the performance of our methodology to the multivariate conditional autoregressive (MCAR) model in both simulations and real data applications.

中文翻译：

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用于多色细胞生长的纵向计数数据的时空模型和推断工具。

通过跟踪体内和体外不同的细胞类型，多色细胞时空图像数据对于研究器官发育和再生，恶性生长或免疫反应已变得非常重要。由于不同细胞类型之间存在复杂的时空相互作用以及与测量单个细胞轨迹有关的困难，来自常见纵向细胞实验的图像数据的统计建模提出了重大挑战。当前的分析方法主要集中在单变量情况下，通常不考虑影响不同细胞群之间细胞生长的时空效应。在本文中，我们提出了一个条件空间自回归模型来描述网格上的多元计数单元数据，并开发了推理工具。所提出的方法在计算上易于处理，并使研究人员能够估计多色细胞生长的完整统计模型。我们的方法应用于真实的实验数据，我们在其中研究了癌细胞与成纤维细胞之间的相互作用如何影响其生长，而这种作用通常存在于肿瘤微环境中。在仿真和实际数据应用中，我们还将比较我们的方法与多元条件自回归（MCAR）模型的性能。