生物信息学2024，Vol.22Issue(4)：270-276,7.DOI:10.12113/202305003
基于变分自编码器的空间转录组细胞聚类研究
Variational autoencoder enabled cell clustering method for spatial transcriptomics
摘要
Abstract
Spatial resolved transcriptomics technology can simultaneously generate gene expression profiles while preserving the positional information of cells within the tissue.How to fully utilize gene expression profiles and spatial positional information to identify spatial regions and complete cell subpopulation clustering is the basis and key for spatial transcriptomics data analysis.In this paper,a spatial transcriptomics cell clustering method based on the combination of variational autoencoder and graph neural network is presented.A two-layer encoder structure is constructed,with each layer containing Simple graph convolution(SGC)to generate low-dimensional representations.The decoder is used to reconstruct the feature matrix and improve the quality of low-dimensional representations by minimizing the loss function.Downstream clustering is performed on the low-dimensional representations to generate different cell subpopulations.The proposed clustering method is compared with several benchmark methods on multiple datasets and has advantages in clustering accuracy and adaptability,demonstrating the effectiveness of the proposed method.关键词
空间转录组学/变分自编码器/图神经网络/细胞聚类Key words
Spatial transcriptomics/Variational autoencoder/Graph neural network/Cell clustering分类
生物科学引用本文复制引用
刘腾,李鑫,印明柱..基于变分自编码器的空间转录组细胞聚类研究[J].生物信息学,2024,22(4):270-276,7.基金项目
科技部重点研发基金项目(No.2022YFC3601800) （No.2022YFC3601800）
重庆市教育委员会科学技术研究计划重大项目(No.KJZD-K202300105) （No.KJZD-K202300105）
重庆大学附属三峡医院基础医学重点项目(No.2023YJKYXML-001). （No.2023YJKYXML-001）
