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Spatially resolved transcriptomics (SRT) technologies measure gene expression across thousands of spatial locations in a tissue, with different technologies having varying resolution, gene coverage, and sequencing depth. Integration of multiple SRT technologies can overcome the limitations of individual technologies; for example enabling gene expression imputation in spatial technologies with limited gene coverage (e.g. 10x Genomics Xenium) or deconvolution of cells in technologies with low spatial resolution (e.g. 10x Genomics Visium). We introduce Spatial Integration for Imputation and Deconvolution (SIID), a joint non-negative factorization model that aligns and integrates paired SRT datasets that profile nearby tissue slices with different SRT technologies. We show that SIID outperforms existing tools in reconstructing cell-type assignments, recovering gene expression, and imputing missing data on simulated data and 10x Genomics Xenium-Visium paired datasets from breast and colon cancer.

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Joint Imputation and Deconvolution of Gene Expression Across Spatial Transcriptomics Platforms

  • 会议论文

  • 发表日期：

    2025年

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  • Hongyu Zheng,
  • Hirak Sarkar,
  • Benjamin J. Raphael

摘要

Spatially resolved transcriptomics (SRT) technologies measure gene expression across thousands of spatial locations in a tissue, with different technologies having varying resolution, gene coverage, and sequencing depth. Integration of multiple SRT technologies can overcome the limitations of individual technologies; for example enabling gene expression imputation in spatial technologies with limited gene coverage (e.g. 10x Genomics Xenium) or deconvolution of cells in technologies with low spatial resolution (e.g. 10x Genomics Visium). We introduce Spatial Integration for Imputation and Deconvolution (SIID), a joint non-negative factorization model that aligns and integrates paired SRT datasets that profile nearby tissue slices with different SRT technologies. We show that SIID outperforms existing tools in reconstructing cell-type assignments, recovering gene expression, and imputing missing data on simulated data and 10x Genomics Xenium-Visium paired datasets from breast and colon cancer.