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Updated: May 24, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Joint imputation and deconvolution of gene expression across spatial transcriptomics platforms.

Hongyu Zheng1, Hirak Sarkar1,2, Benjamin J Raphael1

  • 1Department of Computer Science, Princeton University, Princeton, NJ, USA.

Biorxiv : the Preprint Server for Biology
|March 3, 2025
PubMed
Summary
This summary is machine-generated.

Spatial Integration for Imputation and Deconvolution (SIID) integrates data from multiple spatially resolved transcriptomics (SRT) technologies. This novel algorithm accurately imputes missing gene expression and deconvolves cell types from mixed data, improving spatial transcriptomics analysis.

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Area of Science:

  • Spatial transcriptomics
  • Computational biology
  • Genomics

Background:

  • Spatially resolved transcriptomics (SRT) technologies offer insights into tissue gene expression at various resolutions.
  • Existing SRT platforms vary in spatial resolution, gene coverage, and sequencing depth.
  • Integrating data from diverse SRT technologies can overcome individual platform limitations.

Purpose of the Study:

  • To introduce Spatial Integration for Imputation and Deconvolution (SIID), an algorithm for reconstructing spatial gene expression matrices.
  • To enable imputation of unmeasured genes and deconvolution of cell types from paired SRT data.
  • To provide a computational tool for enhanced spatial transcriptomics data analysis.

Main Methods:

  • SIID utilizes spatial alignment and a joint non-negative factorization model.
  • The algorithm reconstructs a latent spatial gene expression matrix from paired SRT observations.
  • A PyTorch implementation of SIID is publicly available.

Main Results:

  • SIID demonstrates superior performance in simulations for spot-to-cell-type assignment and gene expression recovery.
  • The algorithm accurately imputes missing gene expression data in paired SRT datasets.
  • In real-world applications, SIID achieves high performance in imputing holdout gene expression from Xenium-Visium data.

Conclusions:

  • SIID effectively integrates data from different SRT technologies to overcome limitations.
  • The algorithm enhances the accuracy of gene expression imputation and cell type deconvolution.
  • SIID offers a powerful tool for advancing spatial transcriptomics research in cancer tissues and beyond.