Variational autoencoder enabled cell clustering method for spatial transcriptomics
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.
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