DenVar: density-based variation analysis of multiplex imaging data

Abstract

Multiplex imaging platforms have become 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 becomes extremely crucial for subject-specific assessment of risks. The conventional approach requires selection of two thresholds, one to define the cells of the TME as positive or negative for a particular protein, and the other to classify the subjects based on the proportion of the positive cells. We present a threshold-free approach in which distance between a pair of subjects is computed based on the probability density of the protein in their TMEs. The distance matrix can either be used to classify the subjects into meaningful groups or can directly be used in a kernel machine regression framework for testing association with clinical outcomes. The method gets rid of the subjectivity bias of the thresholding-based approach, enabling easier but interpretable analysis. We analyze a lung cancer dataset, finding the difference in the density of protein HLA-DR to be significantly associated with the overall survival and a triple-negative breast cancer dataset, analyzing the effects of multiple proteins on survival and recurrence. The reliability of our method is demonstrated through extensive simulation studies. Availability The associated R package can be found here, https://github.com/sealx017/DenVar. Supplementary information The Supplementary Material is attached.

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Using random forests to uncover the predictive power of distance-varying cell interactions in tumor microenvironments

“…The method we propose uses a novel combination of spatial statistics and functional data analysis, in conjunction with methods in ensemble machine learning. The application of functional data analysis to spatial point pattern data is a recent development [31][32][33][34][35].…”

FunSpace: A functional and spatial analytic approach to cell imaging data using entropy measures

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