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Accurate and efficient integrative reference-informed spatial domain detection for spatial transcriptomics
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AI
DOI：10.1038/s41592-024-02284-9.png)
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Abstract
En 中文
Spatially resolved transcriptomics (SRT) studies are becoming increasingly common and large, offering unprecedented opportunities in mapping complex tissue structures and functions. Here we present integrative and reference-informed tissue segmentation (IRIS), a computational method designed to characterize tissue spatial organization in SRT studies through accurately and efficiently detecting spatial domains. IRIS uniquely leverages single-cell RNA sequencing data for reference-informed detection of biologically interpretable spatial domains, integrating multiple SRT slices while explicitly considering correlations both within and across slices. We demonstrate the advantages of IRIS through in-depth analysis of six SRT datasets encompassing diverse technologies, tissues, species and resolutions. In these applications, IRIS achieves substantial accuracy gains (39-1,083%) and speed improvements (4.6-666.0) in moderate-sized datasets, while representing the only method applicable for large datasets including Stereo-seq and 10x Xenium. As a result, IRIS reveals intricate brain structures, uncovers tumor microenvironment heterogeneity and detects structural changes in diabetes-affected testis, all with exceptional speed and accuracy. Integrative and reference-informed tissue segmentation (IRIS) harnesses single-cell RNA sequencing data for the accurate identification of spatial domains in spatially resolved transcriptomics. IRIS is computationally efficient and uniquely suited for analyzing large datasets.
Keywords:
SINGLE-CELL
INCREASED EXPRESSION
DUCTAL CARCINOMAS
NEUROGENIC NICHES
ENCODED GENES
IN-SITU
ATLAS
ARCHITECTURE
CANCER
ERBB2
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