ADCL: an adaptive dual contrastive learning framework based on MHGAT and VAE for cell type deconvolution in spatial transcriptomics
- Resource Type
- Conference
- Authors
- Zhang, Shilin; Zhang, Qingchen
- Source
- 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Bioinformatics and Biomedicine (BIBM), 2023 IEEE International Conference on. :355-358 Dec, 2023
- Subject
- Bioengineering
Computing and Processing
Engineering Profession
Robotics and Control Systems
Signal Processing and Analysis
Deconvolution
Transcriptomics
Noise reduction
Self-supervised learning
Spatial databases
Gene expression
Data mining
Spatial transcriptomics
Cell type deconvolution
Contrastive learning
MHGAT
VAE
- Language
- ISSN
- 2156-1133
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.