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HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis

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arxiv 2406.16192 v2 pith:RFGRRETS submitted 2024-06-23 cs.CV

Guillaume Jaume , Paul Doucet , Andrew H. Song , Ming Y. Lu , Cristina Almagro-Pérez , Sophia J. Wagner , Anurag J. Vaidya , Richard J. Chen
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Drew F.K. Williamson Ahrong Kim Faisal Mahmood
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classification cs.CV
keywords hest-1kspatialcancercohortshesthest-benchmarkhest-libraryintroduce
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Spatial transcriptomics enables interrogating the molecular composition of tissue with ever-increasing resolution and sensitivity. However, costs, rapidly evolving technology, and lack of standards have constrained computational methods in ST to narrow tasks and small cohorts. In addition, the underlying tissue morphology, as reflected by H&E-stained whole slide images (WSIs), encodes rich information often overlooked in ST studies. Here, we introduce HEST-1k, a collection of 1,229 spatial transcriptomic profiles, each linked to a WSI and extensive metadata. HEST-1k was assembled from 153 public and internal cohorts encompassing 26 organs, two species (Homo Sapiens and Mus Musculus), and 367 cancer samples from 25 cancer types. HEST-1k processing enabled the identification of 2.1 million expression--morphology pairs and over 76 million nuclei. To support its development, we additionally introduce the HEST-Library, a Python package designed to perform a range of actions with HEST samples. We test HEST-1k and Library on three use cases: (1) benchmarking foundation models for pathology (HEST-Benchmark), (2) biomarker exploration, and (3) multimodal representation learning. HEST-1k, HEST-Library, and HEST-Benchmark can be freely accessed at https://github.com/mahmoodlab/hest.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Sparser2Sparse: Single-shot Sparser-to-Sparse Learning for Spatial Transcriptomics Imputation with Natural Image Co-learning

    cs.CV 2025-07 conditional novelty 6.0 of 10

    S2S-ST reconstructs dense spatial gene expression from 25% sampled spots using self-supervision plus natural-image co-training, and reports better MAE and SSIM than TESLA, BayesSpace, and DIST on eight Xenium samples.

  2. SPATIA: Multimodal Generation and Prediction of Spatial Cell Phenotypes

    q-bio.QM 2025-07 conditional novelty 6.0 of 10

    A hierarchical multimodal model fusing morphology, expression, and spatial context that generates target-state cell morphologies from optimal-transport weak pairs, trained on a 25.9M-cell atlas and benchmarked against...

  3. MeDi: Metadata-Guided Diffusion Models for Mitigating Biases in Tumor Classification

    eess.IV 2025-06 conditional novelty 6.0 of 10

    Conditioning a histopathology diffusion model on tissue source site metadata improves synthetic image fidelity and enhances downstream tumor classification under subpopulation shift.

  4. Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matching

    cs.CV 2025-05 conditional novelty 6.0 of 10

    STFlow uses whole-slide flow matching with local spatial attention to jointly predict gene expression across all spots in a histology image, outperforming prior spot-wise and slide-wise baselines on two benchmarks.

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