Deconvolution of spatial transcriptomics data via graph contrastive learning and partial least square regression

Deciphering the cellular abundance in spatial transcriptomics (ST) is crucial for revealing the spatial architecture of cellular heterogeneity within tissues. However, some of the current spatial sequencing technologies are in low resolutions, leading to each spot having multiple heterogeneous cells. Additionally, current spatial deconvolution methods lack the ability to utilize multi-modality information such as gene expression and chromatin accessibility from single-cell multi-omics data. In this study, we introduce a graph Contrastive Learning and Partial Least Squares regression-based method, CLPLS, to deconvolute ST data. CLPLS is a flexible method that it can be extended to integrate ST data and single-cell multi-omics data, enabling the exploration of the spatially epigenomic heterogeneity. We applied CLPLS to both simulated and real datasets coming from different platforms. Benchmark analyses with other methods on these datasets show the superior performance of CLPLS in deconvoluting spots in single cell level.