ADCL: an adaptive dual contrastive learning framework based on MHGAT and VAE for cell type deconvolution in spatial transcriptomics

Cell type deconvolution is a critical mission in spatial transcriptomics. We propose an adaptive dual contrastive learning framework (ADCL) based on MHGAT and VAE for cell type deconvolution. For spatial transcriptomic data, we construct unsupervised contrast learning module based on the multi-head graph attention networks (MHGAT) to encode gene expression profiles and spatial location information. For scRNA-seq data, we utilize a variational autoencoder (VAE) to reconstruct gene expression matrix and reduce the impact of noise. Finally, we construct contrastive learning deconvolution module to learn probability matrix to achieve cell type deconvolution. Experiments on two real datasets and one simulated dataset prove that ADCL outperforms current state-of-the-art approaches.