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Related Experiment Video

Updated: May 12, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Benchmarking sketching methods on spatial transcriptomics data.

Ian K Gingerich1,2, Brittany A Goods2, Hildreth Robert Frost1

  • 1Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, United States.

Nucleic Acids Research
|May 11, 2026
PubMed
Summary
This summary is machine-generated.

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Intelligent sub-sampling for spatial transcriptomics (ST) needs to consider physical location. Spatially smoothed leverage scores balance tissue coverage and transcriptomic heterogeneity, enabling faster, unbiased ST data analysis.

Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • High-throughput spatial transcriptomics (ST) generates massive datasets, leading to computational challenges.
  • Existing sub-sampling methods (sketching) optimize for expression data but neglect spatial information, potentially introducing bias in ST analysis.
  • This bias can distort tissue architecture by over- or under-sampling specific regions.

Purpose of the Study:

  • To systematically evaluate the impact of different sketching strategies on spatial transcriptomics data analysis.
  • To identify sketching methods that preserve both transcriptomic heterogeneity and spatial tissue architecture.
  • To develop improved sketching approaches for large-scale ST datasets.

Main Methods:

  • Benchmarking of uniform sampling, leverage-score sampling, Geosketch, and scSampler on diverse ST datasets (mouse ovary, MERFISH brain, human breast cancer, lung) and simulations.

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  • Evaluation using input representations including PCA embeddings, spatial coordinates, and spatially smoothed embeddings.
  • Development and testing of a spatially aware sketching method using smoothed leverage scores from randomized SVD.

    • Main Results:

    • Expression-only sketching captures global heterogeneity but distorts tissue architecture.
    • Coordinate-only sketching maintains tissue coverage but misses transcriptional extremes.
    • Spatially smoothed leverage scores effectively balance tissue coverage and transcriptomic representation, outperforming other methods in Hausdorff distance, clustering stability, PCA loading drift, and MSE.
    • The proposed method recovers rare cell states and avoids edge effects.

    Conclusions:

    • Standard sketching methods are insufficient for unbiased spatial transcriptomics analysis due to their neglect of spatial information.
    • A novel, spatially aware sketching approach using smoothed leverage scores offers a robust solution for analyzing large ST datasets.
    • This method enables fast, accurate, and unbiased exploration of complex spatial transcriptomic data, preserving both tissue structure and cellular heterogeneity.