The Clustering of Infected SIV Cells in Lymphatic Tissue

Abstract

Here we investigate the clustering of simian immunodeficiency virus (SIV)–infected cells in a lymphatic tissue sample taken from a rhesus macaque to test a spatial proximity model of the spread of infection. We see that standard methods for analysis of the clustering of point processes are not entirely satisfactory for this application, so we define a novel statistic to understand the clustering in the data. This statistic examines how events spread out from certain points deemed cluster centers. Using this statistic, we can demonstrate the statistical significance of the clustering and examine over what distances this clustering is witnessed. We use Bayesian methods to fully assess the uncertainty in the estimation of this statistic by positing a model for the process. (We assume the process is a nonhomogeneous Poisson process with an intensity that is a linear combination of Gaussian densities.) We see that the distances at which clustering is present are consistent with a simple model of SIV spread within the lymph node.

KEY WORDS:

• Bayesian inference
• Cluster analysis
• Point processes
• Ripley's k function
• SIV
• Pathogenesis