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Striping artifact removal in VisiumHD data through nuclear counts modeling.

Paola Malsot1,2, Malte Londschien2,3, Valentina Boeva1

  • 1Department of Computer Science, ETH Zurich, Zürich, 8092, Switzerland.

Bioinformatics (Oxford, England)
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Summary
This summary is machine-generated.

We developed a new statistical method to remove striping artifacts in 10x Genomics VisiumHD spatial transcriptomics data. Our approach improves accuracy and preserves biological signals, outperforming existing methods for enhanced data analysis.

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Area of Science:

  • Spatial transcriptomics
  • Computational biology
  • Bioinformatics

Background:

  • 10x Genomics VisiumHD spatial transcriptomics offers high resolution (2µm x 2µm).
  • It suffers from slide-specific striping artifacts due to lane-width variations, distorting data and biasing analyses.
  • Current destriping methods, like bin2cell, are asymmetric and can introduce new artifacts.

Purpose of the Study:

  • To develop a novel statistical destriping method for VisiumHD spatial transcriptomics data.
  • To accurately correct striping artifacts while preserving biological signals.
  • To improve upon existing destriping techniques.

Main Methods:

  • A statistical destriping approach leveraging nuclei segmentation from co-registered H&E images.
  • Modeling bin counts using a negative binomial distribution with nucleus-specific and stripe-specific factors.
  • Generalized linear modeling with cross-validated regularization and iterative dispersion estimation.

Main Results:

  • Improved stripe-factor estimation accuracy and reduced error in corrected counts on synthetic data.
  • Consistent reduction in striping intensity across four public VisiumHD datasets.
  • Superior preservation of biological signals and avoidance of artifacts compared to existing methods.

Conclusions:

  • The proposed statistical destriping method effectively removes artifacts from VisiumHD data.
  • This approach enhances the reliability of spatial transcriptomics data for downstream analysis.
  • It offers a more robust solution for correcting lane-width variability artifacts.