CellSpatialGraph: Integrate hierarchical phenotyping and graph modeling to characterize spatial architecture in tumor microenvironment on digital pathology.

Pingjun Chen,Siba El Hussein,Joseph D Khoury,Muhammad Aminu,Jia Wu

doi:10.1016/j.simpa.2021.100156

Abstract

We present CellSpatialGraph, an integrated clustering and graph-based framework, to investigate the cellular spatial structure. Due to the lack of a clear understanding of the cell subtypes in the tumor microenvironment, unsupervised learning is applied to uncover cell phenotypes. Then, we build local cell graphs, referred to as supercells, to model the cell-to-cell relationships at a local scale. After that, we apply clustering again to identify the subtypes of supercells. In the end, we build a global graph to summarize supercell-to-supercell interactions, from which we extract features to classify different disease subtypes.

Highlights

The tumor is a complex ecosystem that emerges and evolves under selective pressure from its microenvironment, involving trophic, metabolic, immunological, and therapeutic factors
A few bottom-up studies focused on profiling cellular architectures from digital pathology slides have emerged using the graph theory approach and graph convolution network (GCN) approach [9,10,11,12,13]
We focus on spatial interaction among neighboring cells via building local graphs factoring in their subtypes so that closely interacting cells are merged to form supercells

Summary

Introduction

The tumor is a complex ecosystem that emerges and evolves under selective pressure from its microenvironment, involving trophic, metabolic, immunological, and therapeutic factors. Most studies phenotype the textural patterns of tissue slides in a top-down manner with the deep convolutional neural networks (CNN) to extract versatile features tailored for particular clinical scenarios [5,6,7,8]. Though these studies have achieved promising performance, they ignore the connections among individual cellular. The code (and data) in this article has been certified as Reproducible by Code Ocean: (https://codeocean.com/). More information on the Reproducibility Badge Initiative is available at https://www.elsevier.com/physical-sciences-and-engineering/computer-science/journals.

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