We propose a new method, named scDSC, to integrate the structural information into deep clustering of scRNA-seq data. The proposed scDSC consists of a ZINB-based autoencoder, a graph neural network (GNN) module, and a mutual-supervised module. To learn the data representation from the sparse and zero-inflated scRNA-seq data, we add a ZINB model to the basic autoencoder. The GNN module is introduced to capture the structural information among cells. By joining the ZINB-based autoencoder with the GNN module, the model transfers the data representation learned by autoencoder to the corresponding GNN layer.
Furthermore, we adopt a mutual supervised strategy to unify these two different deep neural architectures and to guide the clustering task. Extensive experimental results on six real scRNA-seq datasets demonstrate that scDSC outperforms state-of-the-art methods in terms of clustering accuracy and scalability.
*MTAB* | *EMBL-EBOI* | \https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-3929/?query=1529+** |
---|---|---|
*lps* | *GEO-NCBI* | \https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17721** |
*GSE70256* | *GEO-NCBI* | \https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi** |
*PBMC* | *10X* | \https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc4k** |
*Mouse* | *Microwell-seq* | \https://figshare.com/s/865e694ad06d5857db4b** |
*Worm neuron cells* | *sci-RNA-seq* | [http://atlas.gs.washington.edu/worm-rna/docs |