RT Journal Article
SR Electronic
T1 DeepST: A versatile graph contrastive learning framework for spatially informed clustering, integration, and deconvolution of spatial transcriptomics
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.08.02.502407
DO 10.1101/2022.08.02.502407
A1 Yahui Long
A1 Kok Siong Ang
A1 Mengwei Li
A1 Kian Long Kelvin Chong
A1 Raman Sethi
A1 Chengwei Zhong
A1 Hang Xu
A1 Zhiwei Ong
A1 Karishma Sachaphibulkij
A1 Ao Chen
A1 Zeng Li
A1 Huazhu Fu
A1 Min Wu
A1 Hsiu Kim Lina Lim
A1 Longqi Liu
A1 Jinmiao Chen
YR 2022
UL http://biorxiv.org/content/early/2022/08/03/2022.08.02.502407.abstract
AB Advances in spatial transcriptomics technologies has enabled gene expression profiling of tissues while retaining the spatial context. To effectively exploit the data, spatially informed analysis tools are required. Here, we present DeepST, a versatile graph contrastive self-supervised learning framework that incorporates spatial location information and gene expression profiles to accomplish three key tasks, spatial clustering, spatial transcriptomics (ST) data integration, and single-cell RNA-seq (scRNA-seq) transfer onto ST. DeepST combines graph neural networks (GNNs) with contrastive self-supervised learning to learn spot representations in the ST data, and an auto-encoder to extract informative features in the scRNA-seq data. Spatial contrastive self-supervised learning enables the learned spatial spot representation to be more informative and discriminative by minimizing the embedding distance between spatially adjacent spots and vice versa. With DeepST, we found biologically consistent clusters with greater accuracy than competing methods. We next demonstrated DeepST’s ability to jointly analyze multiple tissue slices in both vertical and horizontal integration while correcting for batch effects. Lastly, we used DeepST to deconvolute cell types present in ST with scRNA-seq data, showing better performance than cell2location. We also demonstrated DeepST’s accurate cell type mapping to recover immune cell distribution in the different regions of breast tumor tissue. DeepST is an easily usable and computationally efficient tool for capturing and dissecting the heterogeneity within ST data, enabling biologists to gain insights into the cellular states within tissues.Competing Interest StatementThe authors have declared no competing interest.