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  1. Home
  3. Denoising Image-based Spatial Transcriptomics Data With Denoist.
  1. Home
  3. Denoising Image-based Spatial Transcriptomics Data With Denoist.

Related Experiment Video

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

Published on: September 5, 2025

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Denoising image-based spatial transcriptomics data with DenoIST.

Aaron Wing Cheung Kwok1,2,3, Annika Vannan4, Nicholas E Banovich4

  • 1Bioinformatics and Cellular Genomics, St Vincent's Institute of Medical Research, Fitzroy, 3065, Victoria, Australia.

Biorxiv : the Preprint Server for Biology
|November 26, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Denoising Image-based Spatial Transcriptomics (DenoIST) software removes gene expression noise from spatial transcriptomics data. This computational tool enhances biological structure clarity and improves cell type annotation accuracy in tissue samples.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Image-based spatial transcriptomics (IST) offers high-resolution gene expression data within tissues.
  • Imperfect cell segmentation in IST leads to cross-contamination of gene expression profiles.
  • This contamination obscures true biological signals and complicates downstream analysis.

Purpose of the Study:

  • To develop a computational tool, DenoIST, for accurate identification and removal of contaminating transcripts in IST data.
  • To enhance the specificity and interpretability of gene expression data generated by IST technologies.
  • To improve cell type annotation and biological structure resolution in spatial transcriptomics.

Main Methods:

  • DenoIST employs a Poisson mixture model to capture local neighborhood contamination.
  • The model explicitly accounts for transcript spillover between adjacent cells.
  • The tool was validated on multiple real-world IST datasets with varying cell densities.

    • Main Results:

    • DenoIST effectively identifies and removes contaminating transcripts, restoring gene expression specificity.
    • The denoised data reveal clearer local biological structures by filtering spurious signals.
    • Cell type annotation becomes more consistent and interpretable, reducing ambiguous cell profiles.

    Conclusions:

    • DenoIST significantly improves the biological interpretability and robustness of IST data.
    • The tool can be seamlessly integrated into existing IST analysis workflows.
    • By mitigating cross-contamination, DenoIST enhances the reliability of spatial transcriptomics for biological discovery.