B1603
Title: Accurate and efficient integrative reference-informed spatial domain detection for spatial transcriptomics
Authors: Ying Ma - Brown University (United States) [presenting]
Abstract: Spatially resolved transcriptomics (SRT) studies are becoming increasingly common and large, offering unprecedented opportunities to characterize complex tissues' spatial and functional organization. A computational method, IRIS, is introduced that characterizes the spatial organization of complex tissues through accurate and efficient detection of spatial domains. IRIS uniquely leverage the widespread availability of single-cell RNA-seq data for reference-informed spatial domain detection, integrates multiple SRT tissue slices jointly while explicitly considering correlation within and across slices, produces biologically interpretable spatial domains, and benefits from multiple algorithmic innovations for highly scalable computation. The advantages of IRIS are demonstrated through an in-depth analysis of six SRT datasets from different technologies across various tissues, species, and spatial resolutions. In these applications, IRIS attains an unprecedented 58\% ~ 1,083\% accuracy gain over existing methods in the gold standard dataset with known ground truth. As a result, IRIS uncovers the fine-scale structures of brain regions, reveals the spatial heterogeneity of distinct tumour microenvironments, and characterizes the structural changes of the seminiferous tubes in the testis associated with diabetes, all at a speed and accuracy unachievable by existing approaches.
