Use Graph Neural Network to Study Spatial Proteomic Data

Music City Center 

Spatial proteomic technique often measures 20-40 protein markers of each cell, using samples collected from tissue microarrays. While each tissue sample is small and the number of protein markers is limited, this approach is more scalable than other spatial omic techniques. A few studies have collected spatial proteomic data from hundreds or thousands of patients. We propose to use graph neural network to extract features from spatial proteomic data so that those features are comparable across samples. Graph neural network combines spatial information (graph connections) together with attribute of each cell (e.g., the cell type of each cell inferred from the spatial protein omic data). Such features can be used for supervised or unsupervised down-stream analysis. We will focus on unsupervised analysis to identify meaningful domains in tumor samples.

Graph Neural Network

Spatial Proteomic

Clinical Outcomes