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A visual-omics foundation model to bridge histopathology image with transcriptomics.

Weiqing Chen1,2, Pengzhi Zhang1,3,4,5, Tu N Tran1,3,4,5

  • 1Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, TX, 77030, USA.

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This summary is machine-generated.

We developed OmiCLIP, a visual-omics foundation model, to integrate tissue histology images and transcriptomics data. The Loki platform, built on OmiCLIP, enables advanced analysis, demonstrating superior accuracy in computational biology tasks.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Artificial intelligence in genomics

Background:

  • Omics technologies like single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) generate detailed genomic data.
  • Existing computational models often analyze omics data or histology images separately, hindering integrated analysis.
  • There is a need for models that can effectively link histological features with transcriptomic profiles.

Purpose of the Study:

  • To develop a novel visual-omics foundation model, OmiCLIP, for integrating hematoxylin and eosin (H&E) images with transcriptomic data.
  • To create the Loki platform, leveraging OmiCLIP, to provide a suite of tools for visual-omics analysis.
  • To evaluate the performance of the Loki platform against state-of-the-art models.

Main Methods:

  • Developed OmiCLIP, a foundation model trained on 2.2 million paired tissue images and transcriptomic data from 32 organs.
  • Transformed transcriptomic data into a "sentence" format by concatenating top-expressed gene symbols per tissue patch.
  • Built the Loki platform with functionalities including tissue alignment, annotation, cell type decomposition, retrieval, and ST gene expression prediction.

Main Results:

  • OmiCLIP successfully integrates histology and transcriptomics data.
  • The Loki platform demonstrated consistent accuracy and robustness across diverse datasets.
  • Loki outperformed 22 state-of-the-art models in comparative analyses on simulation and experimental datasets.

Conclusions:

  • OmiCLIP represents a significant advancement in visual-omics by bridging the gap between image and omics data.
  • The Loki platform provides a powerful and versatile tool for researchers in computational biology and related fields.
  • Integrated visual-omics analysis holds great potential for deeper biological insights.