Assessing Topological Associations Between Immune Cell Structures in Spatial Proteomic Imaging Data

Music City Center 

Spatial proteomic technologies reveal immune cells organization, offering critical information about immune function and disease mechanisms. Standard methods for assessing immune cell interactions rely on simplistic summary statistics that fail to accommodate variations in scan orientation, cell count, and location variations across individuals, thereby do not directly evaluate spatial structures.

To address this, we use topological data analysis (TDA) with persistent homology (PH) to capture spatial structure. PH systematically translates spatial information of cells into topological summaries, producing k*n persistence diagrams-one for each cell type per individual. Pairwise L1 distances between persistence diagrams for individual cell types form k n*n distance matrices that capture structural differences across the population. The Kernel RV is then used to identify associations between the spatial structures of different immune cell types. Simulations and real data analyses show our approach is often more powerful, particularly at assessing global structures, while still protecting type I error, serving as a powerful new approach investigating spatial immune cell interaction.

Spatial Proteomic Imaging Data

Persistent Homology

Kernel RV

Topological Data Analysis 

Section on Statistics in Imaging