RT Journal Article
SR Electronic
T1 DiffuST: a latent diffusion model for spatial transcriptomics denoising
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2024.06.19.599672
DO 10.1101/2024.06.19.599672
A1 Jiao, Shaoqing
A1 Lu, Dazhi
A1 Zeng, Xi
A1 Wang, Tao
A1 Wang, Yongtian
A1 Dong, Yunwei
A1 Peng, Jiajie
YR 2024
UL http://biorxiv.org/content/early/2024/06/23/2024.06.19.599672.abstract
AB Spatial transcriptomics technologies have enabled comprehensive measurements of gene expression profiles while retaining spatial information and matched pathology images. However, noise resulting from low RNA capture efficiency and experimental steps needed to keep spatial information may corrupt the biological signals and obstruct analyses. Here, we develop a latent diffusion model DiffuST to denoise spatial transcriptomics. DiffuST employs a graph autoencoder and a pre-trained model to extract different scale features from spatial information and pathology images. Then, a latent diffusion model is leveraged to map different scales of features to the same space for denoising. The evaluation based on various spatial transcriptomics datasets showed the superiority of DiffuST over existing denoising methods. Furthermore, the results demonstrated that DiffuST can enhance downstream analysis of spatial transcriptomics and yield significant biological insights.Competing Interest StatementThe authors have declared no competing interest.