HE-zoo — predicting spatial transcriptomics from H&E

HE-zoo — predicting spatial transcriptomics from H&E#

ov.space.histo predicts spatial gene-expression from a hematoxylin-and-eosin (H&E) histology slide. It wraps the strongest 2024–2026 methods behind one dispatcher so you can swap backends without re-staging your data:

import omicverse as ov

wsi = ov.space.histo.open_wsi('slide.tif')
ov.space.histo.tile(wsi, tile_px=224, mpp=0.5)
ov.space.histo.embed(wsi, model='gigapath')

ov.space.histo.predict_expression(wsi, method='stpath',
                                  organ='Breast', tech='Visium')

The container is a wsidata.WSIData (a spatialdata.SpatialData subclass with WSI accessors). Predictions land in wsi.tables['{method}_tiles'] as an AnnData with tile-pixel centroids in obsm['spatial']. This means downstream tools — ov.space.svg, ov.pl.spatial, zs.pl.tiles, scanpy.pl.spatial — accept HE→ST predictions and real Visium tables interchangeably.

When to use which backend#

Method

Needs paired Visium?

Pretrained weights

Output

Best for

stpath

No (zero-shot)

✅ tlhuang/STPath on HuggingFace

spot-level, 38,984 genes

Zero-shot inference on H&E-only slides across 17 organs

stflow

Yes (1 slide)

❌ trains a per-slide head

spot-level

Same-cohort prediction when STPath’s gene vocabulary doesn’t cover your panel

hest_fm

Yes (1 slide)

uses public pathology FM + ridge

spot-level

Custom panels, transparent and reproducible baseline

istar

Yes (1 slide)

uses HIPT (mahmoodlab)

sub-spot (~8 µm)

Super-resolving an existing Visium sample to near-single-cell resolution

All four are exercised on the same H&E (10x Visium Breast Cancer Block A Section 1, ~1.7 GB) so the tutorials are directly comparable.

Foundation-model access#

The strongest backbones — gigapath, uni2, conch_v1.5, virchow2, h-optimus-1 — are gated on HuggingFace. Request access on each model card, then login with huggingface-cli login once. The hest_fm tutorial also runs on the fully-public ctranspath backbone so you can verify the pipeline without any gated weights.

Tutorials#

References#

  • Huang T. et al. STPath: a generative foundation model for integrating spatial transcriptomics and whole-slide images. npj Digital Medicine (2025).

  • Huang T. et al. Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matching. ICML 2025 (Spotlight).

  • Jaume G. et al. HEST-1k: A Dataset For Spatial Transcriptomics and Histology Image Analysis. NeurIPS 2024 (Spotlight).

  • Zhang D. et al. Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology. Nature Biotechnology (2024).

  • Yu C. et al. LazySlide: scalable and modular whole slide image analysis with scverse integration. bioRxiv (2025).

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