Tensor decomposition to identify context-aware spatial neighborhoods in the tumor microenvironment.

The tumor microenvironment (TME) is a complex ecosystem composed of diverse cell types whose spatial interactions play an important role in tumor progression. Many cell types have characteristic biomarkers that can be detected at single cell resolution through multiplex tissue imaging methods. There exist several image analysis tools to quantify such interactions, but they calculate summary statistics one image at a time.

Three-dimensional characterization of sex differences in abdominal aortic aneurysm progression via vascular deformation mapping.

Although abdominal aortic aneurysms (AAA) are more common in men, women are at greater risk for AAA growth/rupture. Vascular deformation mapping (VDM) utilizes deformable image registration to qualify and quantify 3D-AAA growth using computed tomography angiograms (CTA). In this study we leveraged VDM to investigate sex differences in AAA growth patterns.

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.