CoDi: Contrastive distance cell type annotation for spatially resolved transcriptomics

In the era of single-cell genomics, deciphering cellular heterogeneity is paramount for understanding complex biological systems. Providing correct cell type annotations is a crucial task for enabling downstream analysis in Spatial Transcriptomics (ST). Untargeted ST technologies offer wide insight into the gene landscape, but suffer from gene dropouts, preventing complete capture of genes present in the cell. Common approaches for cell type annotation employ mapping of the reference high-quality scRNA gene expressions and cell types, to lower quality ST dataset. However, the accuracy and the performance of cell type annotation is not objectively evaluated, and methods lack the capacity to perform on large datasets with sparse data produced by the high-resolution technologies like Slide-seq v2 and Stereo-seq. We present CoDi, an innovative tool designed for precise cell type annotation leveraging the power of contrastive learning and advanced distance calculation methods using reference single-cell datasets. CoDi represents a significant advancement by demonstrating superior performance, and scalability compared to existing solutions on several different evaluation metrics, including highest retention rate of the marker genes. By harnessing the intrinsic structure of the data, CoDi effectively captures subtle features that characterize distinct cell types, resulting in enhanced annotation accuracy that can detect rare cell types such as neurons in the heart. In summary, CoDi represents a valuable tool, contributing to our understanding of cellular heterogeneity and offering insights into the specificity of various cell types within diverse tissue structures.