聚类分析
计算机科学
空间分析
图形
仿形(计算机编程)
卷积神经网络
转录组
人工智能
数据挖掘
计算生物学
生物
基因表达
理论计算机科学
基因
数学
统计
操作系统
生物化学
作者
Jiachen Li,Siheng Chen,Xiaoyong Pan,Ye Yuan,Hong‐Bin Shen
出处
期刊:Research Square - Research Square
日期:2021-12-08
被引量:8
标识
DOI:10.21203/rs.3.rs-990495/v1
摘要
Abstract Spatial transcriptomics data can provide high-throughput gene expression profiling and spatial structure of tissues simultaneously. An essential question of its initial analysis is cell clustering. However, most existing studies rely on only gene expression information and cannot utilize spatial information efficiently. Taking advantages of two recent technical development, spatial transcriptomics and graph neural network, we thus introduce CCST, C ell C lustering for S patial T ranscriptomics data with graph neural network, an unsupervised cell clustering method based on graph convolutional network to improve ab initio cell clustering and discovering of novel sub cell types based on curated cell category annotation. CCST is a general framework for dealing with various kinds of spatially resolved transcriptomics. With application to five in vitro and in vivo spatial datasets, we show that CCST outperforms other spatial cluster approaches on spatial transcriptomics datasets, and can clearly identify all four cell cycle phases from MERFISH data of cultured cells, and find novel functional sub cell types with different micro-environments from seqFISH+ data of brain, which are all validated experimentally, inspiring novel biological hypotheses about the underlying interactions among cell state, cell type and micro-environment.
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