Work

Scan-IT: Domain segmentation of spatial transcriptomics images by graph neural network

Year: 2021

Type: conference-paper

Abstract: Complex biological tissues consist of numerous cells in a highly coordinated manner and carry out various biological functions. Therefore, segmenting a tissue into spatial and functional domains is critically important for understanding and controlling the biological functions. The emerging spatial transcriptomics technologies allow simultaneous measurements of thousands of genes with precise spatial information, providing an unprecedented opportunity for dissecting biological tissues. However, how to utilize such noisy, sparse, and high dimensional data for tissue segmentation remains a major challenge. Here, we develop a deep learning-based method, named SCAN-IT by transforming the spatial domain identification problem into an image segmentation problem, with cells mimicking pixels and expression values of genes within a cell representing the color channels. Specifically, SCAN-IT relies on geometric modeling, graph neural networks, and an informatics approach, DeepGraphInfomax. We demonstrate that SCAN-IT can handle datasets from a wide range of spatial transcriptomics techniques, including the ones with high spatial resolution but low gene coverage as well as those with low spatial resolution but high gene coverage. We show that SCAN-IT outperforms state-of-the-art methods using a benchmark dataset with ground truth domain annotations.

Source: British Machine Vision Conference (BMVC)

DOI: 10.5244/c.35.320

Authors: Zixuan Cang, Xinyi Ning, Annika Nie, Min Xu, Jing Zhang

Institutions: University of California, Irvine, Tsinghua University, Carnegie Mellon University

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: National Institute of Mental Health, National Institute on Drug Abuse, National Institute of General Medical Sciences

Awards: Novel machine learning approaches for improving structural discrimination in cryo-electron tomography, A big data approach to explore epigenetic heterogeneity and interpret noncoding variants for psychiatric disorders, High Performance Computing for Multiscale Modeling of Biological Systems, M-SCORCH: Methamphetamine use disorder data generation center for Single Cell Opioid Responses in the Context of HIV