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FICTURE: Scalable segmentation-free analysis of submicron resolution spatial transcriptomics

Si, Y.; Lee, C.; Hwang, Y.; Yun, J. H.; Cheng, W.; Cho, C.-S.; Quiros, M.; Nusrat, A.; Zhang, W.; Jun, G.; Zoellner, S.; Lee, J. H.; Kang, H. M.

2023-11-07 bioinformatics
10.1101/2023.11.04.565621 bioRxiv
Show abstract

Spatial transcriptomics (ST) technologies have advanced to enable transcriptome-wide gene expression analysis at submicron resolution over large areas. Analysis of high-resolution ST data relies heavily on image-based cell segmentation or gridding, which often fails in complex tissues due to diversity and irregularity of cell size and shape. Existing segmentation-free analysis methods scale only to small regions and a small number of genes, limiting their utility in high-throughput studies. Here we present FICTURE, a segmentation-free spatial factorization method that can handle transcriptome-wide data labeled with billions of submicron resolution spatial coordinates. FICTURE is orders of magnitude more efficient than existing methods and it is compatible with both sequencing- and imaging-based ST data. FICTURE reveals the microscopic ST architecture for challenging tissues, such as vascular, fibrotic, muscular, and lipid-laden areas in real data where previous methods failed. FICTUREs cross-platform generality, scalability, and precision make it a powerful tool for exploring high-resolution ST.

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