AbstractMultiplex immunohistochemistry (mIHC) and multiplexed ion beam imaging (MIBI) platforms have become increasingly popular for studying complex single-cell biology in cancer patients. In such studies, researchers test for association between functional markers and survival in a two-step process. First, they count the number of positive cells, defined as the number of cells where a functional marker is significantly expressed. Then, they partition the patients into two groups and test for association between the group label with survival. Consequently, the approach suffers from subjectivity and lack of robustness. In this paper, we propose a threshold-free distance metric between patients solely based on their marker probability densities. Using the proposed distance, we have developed two association tests, one based on hierarchical clustering and the other based on linear mixed model. Our method obviates the need for the arduous step of threshold selection, getting rid of the subjectivity bias. The method also intuitively generalizes to joint analysis of multiple markers. We assessed the performance of our method through extensive simulation studies and also used it to analyze two multiplex imaging datasets.
Numerical simulation studies of the LBNL heavy-ion beam combiner experiment