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Updated: May 26, 2026

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Autoencoder Denoising for Network-Based Spatial Transcriptomics Data with Applications for Cell Signaling Estimation.

Azka Javaid1, H Robert Frost1

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

Complex Networks & Their Applications. International Conference on Complex Networks and Their Applications
|May 25, 2026
PubMed
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This study introduces an autoencoder framework to denoise spatial transcriptomics networks for cell signaling analysis. The method improves network reconstruction and generates biologically plausible cell signaling estimates from spatial transcriptomics data.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Spatial Transcriptomics (ST) data offers insights into cellular organization and function.
  • Accurate network reconstruction is crucial for cell signaling analysis from ST data.
  • Existing denoising methods may not optimally handle the complexity of ST-derived networks.

Purpose of the Study:

  • To develop and validate an autoencoder-based framework for denoising networks from ST data.
  • To enhance cell signaling estimation using denoised networks.
  • To compare autoencoder denoising with Singular Value Decomposition (SVD) for different graph models.

Main Methods:

  • An unsupervised encoder-decoder framework was employed for denoising network adjacency matrices.
Keywords:
autoencoderscell signalingnetwork denoisingspatial transcriptomics

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  • A supervised framework was utilized for cell signaling estimation.
  • Denoising performance was validated using the Frobenius norm on simulated Barabási-Albert (BA) and Erdős-Rényi (ER) graphs.
  • Cell signaling estimates were validated on real ST data for specific ligand-receptor interactions.

    • Main Results:

    • The autoencoder framework demonstrated superior adjacency matrix reconstructions for superlinear BA and dense ER graphs compared to SVD.
    • Cell signaling estimates derived from the denoised networks were regionally specific.
    • The estimated cell signaling interactions were biologically plausible.
    • The study provides a novel application of neural networks for network-based cell signaling analysis in ST data.

    Conclusions:

    • The proposed autoencoder-based framework effectively denoises networks from ST data, improving cell signaling analysis.
    • This approach offers a powerful tool for uncovering cell-cell communication in complex biological tissues.
    • Benchmarking highlights the advantages of autoencoder denoising over SVD for specific graph structures in ST data analysis.