  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
cells affect their growth. We include two case studies; the first evaluates
interactions between cancer cells and fibroblasts, which are normally present
in the tumor microenvironment, whilst the second evaluates interactions between
cloned cancer cells when grown as different combinations.
