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DeepSpot

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Deep learning model predicting spatial transcriptomics from H&E images at spot and single-cell resolution.

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Computational Biology
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2026-10-10
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  1. DeepSpot2Cell github.com

    Predicts virtual single-cell spatial transcriptomics from H&E using spot-level supervision (NeurIPS 2025 Imageomics).

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  2. DeepSpot-M github.com

    Multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology.

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  3. TCGA virtual spatial transcriptomics atlas huggingface.co

    DeepSpot-M predicted transcriptome-wide ST for TCGA H&E (FF + FFPE; 28,664 slides / 32 cancer types; gated). Paper: DeepSpot-M.

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  4. HEST Xenium virtual spatial transcriptomics huggingface.co

    DeepSpot-M predicted transcriptome-wide ST for 59 HEST-1k 10x Xenium samples (~13.3M cells) (gated). Paper: DeepSpot-M.

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  5. Spotiflow github.com

    A deep learning-based, threshold-agnostic, and subpixel-accurate spot detection method developed for spatial transcriptomics workflows.

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  6. DeepTalk github.com

    Graph attention network for deciphering cell-cell communication from spatial transcriptomics.

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Computational Biology › Machine Learning Tasks and Models > Foundation Models > Single-cell Foundation Models: “Deep learning model predicting spatial transcriptomics from H&E images at spot and single-cell resolution.”