DIMPLE: An R package to quantify, visualize, and model spatial cellular interactions from multiplex imaging with distance matrices.

A major challenge in the spatial analysis of multiplex imaging (MI) data is choosing how to measure cellular spatial interactions and how to relate them to patient outcomes. Existing methods to quantify cell-cell interactions do not scale to the rapidly evolving technical landscape, where both the number of unique cell types and the number of images in a dataset may be large. We propose a scalable analytical framework and accompanying R package, DIMPLE, to quantify, visualize, and model cell-cell interactions in the TME. By applying DIMPLE to publicly available MI data, we uncover statistically significant associations between image-level measures of cell-cell interactions and patient-level covariates.

Investigating Ecological Interactions in the Tumor Microenvironment using Joint Species Distribution Models for Point Patterns.

The tumor microenvironment (TME) is a complex and dynamic ecosystem that involves interactions between different cell types, such as cancer cells, immune cells, and stromal cells. These interactions can promote or inhibit tumor growth and affect response to therapy. Multitype Gibbs point process (MGPP) models are statistical models used to study the spatial distribution and interaction of different types of objects, such as the distribution of cell types in a tissue sample. Such models are potentially useful for investigating the spatial relationships between different cell types in the tumor microenvironment, but so far studies of the TME using cell-resolution imaging have been largely limited to spatial descriptive statistics. However, MGPP models have many advantages over descriptive statistics, such as uncertainty quantification, incorporation of multiple covariates and the ability to make predictions. In this paper, we describe and apply a previously developed MGPP method, the saturated pairwise interaction Gibbs point process model, to a publicly available multiplexed imaging dataset obtained from colorectal cancer patients. Importantly, we show how these methods can be used as joint species distribution models (JSDMs) to precisely frame and answer many relevant questions related to the ecology of the tumor microenvironment.

DIMPLE: AN R PACKAGE TO QUANTIFY, VISUALIZE, AND MODEL SPATIAL CELLULAR INTERACTIONS FROM MULTIPLEX IMAGING WITH DISTANCE MATRICES.

The tumor microenvironment (TME) is a complex ecosystem containing tumor cells, other surrounding cells, blood vessels, and extracellular matrix. Recent advances in multiplexed imaging technologies allow researchers to map several cellular markers in the TME at the single cell level while preserving their spatial locations. Evidence is mounting that cellular interactions in the TME can promote or inhibit tumor development and contribute to drug resistance. Current statistical approaches to quantify cell-cell interactions do not readily scale to the outputs of new imaging technologies which can distinguish many unique cell phenotypes in one image. We propose a scalable analytical framework and accompanying R package, DIMPLE, to quantify, visualize, and model cell-cell interactions in the TME. In application of DIMPLE to publicly available MI data, we uncover statistically significant associations between image-level measures of cell-cell interactions and patient-level covariates.