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  3. Representation Learning For Multi-modal Spatially Resolved Transcriptomics Data.
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  3. Representation Learning For Multi-modal Spatially Resolved Transcriptomics Data.

Related Experiment Video

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
10:22

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

Representation learning for multi-modal spatially resolved transcriptomics data.

Kalin Nonchev1,2, Sonali Andani1,2,3, Joanna Ficek-Pascual1,2

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

Bioinformatics (Oxford, England)
|May 25, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We developed AESTETIK, a deep learning model integrating spatial transcriptomics and morphology data for improved tissue analysis. This method enhances cell clustering in various tissues, including cancer, advancing precision medicine.

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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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Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
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Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Spatial transcriptomics offers detailed molecular and morphological insights while preserving spatial context.
  • Integrating multi-modal spatial transcriptomics data remains a challenge for precision medicine.

Purpose of the Study:

  • To develop a novel deep learning model for joint integration of spatial, transcriptomics, and morphology data.
  • To improve the accuracy of spot representations and cell clustering in spatial transcriptomics analysis.

Main Methods:

  • Introduced AESTETIK, a convolutional deep learning model for multi-modal data integration.
  • Applied AESTETIK to various datasets, including structured (brain) and heterogeneous (cancer) tissues.
  • Utilized widely adopted technology platforms like 10x Genomics™ and NanoString™.

Main Results:

  • AESTETIK significantly improved cluster assignments across multiple datasets and platforms.
  • Achieved a 21% increase in median ARI for structured tissues (e.g., brain) compared to state-of-the-art methods.
  • Demonstrated superior performance on cancer tissues, with notable improvements in breast cancer (2-fold), melanoma (79%), and liver cancer (21%).

Conclusions:

  • AESTETIK enables accurate spot representation by integrating spatial, transcriptomics, and morphology data.
  • The model enhances cell clustering performance, particularly in complex tissue types.
  • These advances facilitate a multi-modal understanding of biological processes and support precision medicine applications.