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Abstract:
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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, becomes extremely crucial for subject-specific assessment of risks. The conventional approach requires selection of two thresholds, one to define particular cells as positive for a marker, and the other to classify the subjects based on the number of 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. With the proposed method, we analyze a lung cancer dataset from an mIHC platform and a MIBI triple-negative breast cancer dataset.
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