DenVar: Density-based Variation analysis of multiplex imaging data

Abstract

Multiplex immunohistochemistry (mIHC) and multiplexed ion beam imaging (MIBI) platforms have become increasingly popular for studying complex single-cell biology in the tumor microenvironment (TME) of cancer subjects. Studying the intensity of the proteins that regulate important cell-functions, often known as functional markers, in the TME becomes extremely crucial for subject-specific assessment of risks, such as risk of recurrence and risk of death. The conventional approach requires selection of two thresholds, one to define the cells of the TME as positive or negative for a particular functional marker, and the other to classify the subjects based on the proportion of the positive cells. The selection of the thresholds has a large impact on the results and an arbitrary selection can lead to an incomprehensible conclusion. In light of this problem, we present a threshold-free distance between the subjects based on the probability densities of the functional markers. The distance can be used to classify the subjects into meaningful groups or can be used in a linear mixed model setup for testing association with clinical outcomes. The method gets rid of the subjectivity bias of the thresholding-based approach, enabling an easier but interpretable analysis of these types of data. With the proposed method, we analyze a lung cancer dataset from an mIHC platform, finding the difference in the density of functional marker HLA-DR to be significantly associated with the overall survival. The approach is also applied on an MIBI triple-negative breast cancer dataset to analyze effects of multiple functional markers. Finally, we demonstrate the reliability of our method through extensive simulation studies.