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Leveraging information in spatial transcriptomics to predict super-resolution gene expression from histology images in tumors

Minxing Pang, Kenong Su, View ORCID ProfileMingyao Li
doi: https://doi.org/10.1101/2021.11.28.470212
Minxing Pang
1Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
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Kenong Su
2Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, USA
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  • For correspondence: suken@pennmedicine.upenn.edu mingyao@pennmedicine.upenn.edu
Mingyao Li
2Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, USA
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  • ORCID record for Mingyao Li
  • For correspondence: suken@pennmedicine.upenn.edu mingyao@pennmedicine.upenn.edu
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ABSTRACT

Recent developments in spatial transcriptomics (ST) technologies have enabled the profiling of transcriptome-wide gene expression while retaining the location information of measured genes within tissues. Moreover, the corresponding high-resolution hematoxylin and eosin-stained histology images are readily available for the ST tissue sections. Since histology images are easy to obtain, it is desirable to leverage information learned from ST to predict gene expression for tissue sections where only histology images are available. Here we present HisToGene, a deep learning model for gene expression prediction from histology images. To account for the spatial dependency of measured spots, HisToGene adopts Vision Transformer, a state-of-the-art method for image recognition. The well-trained HisToGene model can also predict super-resolution gene expression. Through evaluations on 32 HER2+ breast cancer samples with 9,612 spots and 785 genes, we show that HisToGene accurately predicts gene expression and outperforms ST-Net both in gene expression prediction and clustering tissue regions using the predicted expression. We further show that the predicted super-resolution gene expression also leads to higher clustering accuracy than observed gene expression. Gene expression predicted from HisToGene enables researchers to generate virtual transcriptomics data at scale and can help elucidate the molecular signatures of tissues.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 28, 2021.
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Leveraging information in spatial transcriptomics to predict super-resolution gene expression from histology images in tumors
Minxing Pang, Kenong Su, Mingyao Li
bioRxiv 2021.11.28.470212; doi: https://doi.org/10.1101/2021.11.28.470212
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Leveraging information in spatial transcriptomics to predict super-resolution gene expression from histology images in tumors
Minxing Pang, Kenong Su, Mingyao Li
bioRxiv 2021.11.28.470212; doi: https://doi.org/10.1101/2021.11.28.470212

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