pathomics/

├── metadata/
│   └── NCBI689.json
│
├── st/
│   └── NCBI689.h5ad
│
├── wsis/
│   └── NCBI689.tif
│
├── thumbnails/
│   └── NCBI689_downscaled_fullres.jpeg
│
├── spatial_plots/
│   └── NCBI689_spatial_plots.png
│
├── cellvit_seg_for_superfocus/
│   └── NCBI689/
│       └── ...
│
├── PATHOMICS_v3_0_0.csv
│
└── README.md

File Descriptions

Directory	Description
`metadata/`	JSON metadata for each sample
`st/`	Spatial transcriptomics AnnData (`.h5ad`)
`wsis/`	Whole-slide pathology images
`thumbnails/`	Downscaled JPEG tissue thumbnails
`spatial_plots/`	Visualization of spatial transcriptomics spots
`cellvit_seg_for_superfocus/`	CellViT segmentation outputs
`PATHOMICS_v3_0_0.csv`	Master metadata table

Metadata Table

The file:

PATHOMICS_v3_0_0.csv

contains the master metadata table for all samples.

Important fields include:

Field	Description
`id`	Unique sample identifier
`source`	`hest` or `literature`
`hest_id`	Original HEST sample ID (if applicable)
`organ`	Tissue/organ source
`species`	Species information
`platform`	Spatial transcriptomics platform
`nb_genes`	Number of genes
`spots_under_tissue`	Number of tissue-covered spots
`pixel_size_um_estimated`	Estimated pixel resolution
`cellvit_seg`	Number of segmented cells
`has_superfocus_seg`	Whether CellViT segmentation exists

Access Requirements

To use this dataset, you need access to:

Pathomics
HEST (optional but recommended for HEST-derived samples)

Step 1 — Request Access

Pathomics

Click:

Request Access

at the top of this page.

HEST

Request access at:

https://huggingface.co/datasets/MahmoodLab/hest

Access is automatically granted.

Step 2 — Create a Hugging Face Token

Create a Hugging Face token at:

https://huggingface.co/settings/tokens

Recommended permission:

Write

Step 3 — Install Dependencies

pip install huggingface-hub pandas

Step 4 — Login

from huggingface_hub import login

login(token="YOUR_HF_TOKEN")

Download API

The following helper script provides a unified interface for downloading:

Individual samples
Multiple samples
Entire dataset
Specific modalities only
Optional HEST base data

Download Script

from huggingface_hub import snapshot_download
import pandas as pd
import os


def download_pathomics(
    ids=None,
    pathomics_dir="pathomics_data",
    hest_dir="pathomics_data",
):

    # -----------------------------
    # load metadata index
    # -----------------------------
    meta = None
    
    csv_files = [f for f in os.listdir(pathomics_dir)
                 if f.startswith("PATHOMICS_v")]

    # print(csv_files)

    if len(csv_files) > 0:
        csv_path = os.path.join(pathomics_dir, sorted(csv_files)[-1])
        meta = pd.read_csv(csv_path)

    if ids is None:
        snapshot_download(
            repo_id="Boyoungc/pathomics",
            allow_patterns="*",
            repo_type="dataset",
            local_dir=pathomics_dir,
        )
        return

    # -----------------------------
    # split ids
    # -----------------------------
    hest_ids = []
    local_ids = []

    if meta is not None and "source" in meta.columns:
        sub = meta[meta["id"].isin(ids)]
        hest_ids = sub[sub["source"] == "hest"]["id"].tolist()
        local_ids = sub[sub["source"] != "hest"]["id"].tolist()
    else:
        local_ids = ids

    # =========================================================
    # 1. HEST DOWNLOAD (STRICT MODALITY FILTER)
    # =========================================================
    if len(hest_ids) > 0:

        hest_patterns = []
    
        for hid in hest_ids:
            hest_patterns.extend([
                f"metadata/{hid}.json",
                f"st/{hid}.h5ad",
                f"wsis/{hid}.tif",
                f"thumbnails/{hid}_*",
                f"spatial_plots/{hid}_*",
            ])
    
        snapshot_download(
            repo_id="MahmoodLab/hest",
            allow_patterns=hest_patterns,
            repo_type="dataset",
            local_dir=hest_dir,
        )
    
        print(f"[HEST] downloaded {len(hest_ids)} samples")

    # =========================================================
    # 2. PATHOMICS SEG ONLY for HEST
    # =========================================================
    if len(hest_ids) > 0:

        seg_patterns = [
            f"cellvit_seg_for_superfocus/{hid}/**"
            for hid in hest_ids
        ]

        snapshot_download(
            repo_id="Boyoungc/pathomics",
            allow_patterns=seg_patterns,
            repo_type="dataset",
            local_dir=pathomics_dir,
        )

        print(f"[SEG] downloaded HEST segmentations")

    # =========================================================
    # 3. PATHOMICS FULL for literature
    # =========================================================
    if len(local_ids) > 0:

        patterns = []

        for sid in local_ids:
            patterns.extend([
                f"metadata/{sid}.json",
                f"st/{sid}.h5ad",
                f"wsis/{sid}.tif",
                f"thumbnails/{sid}_*",
                f"spatial_plots/{sid}_*",
                f"cellvit_seg_for_superfocus/{sid}/**",
            ])

        snapshot_download(
            repo_id="Boyoungc/pathomics",
            allow_patterns=patterns,
            repo_type="dataset",
            local_dir=pathomics_dir,
        )

        print(f"[PATHOMICS] downloaded literature samples")

Usage Examples

Download One Sample

download_pathomics(
    ids=["NCBI689"]
)

Download Multiple Samples

download_pathomics(
    ids=["NCBI689", "MEND62"]
)

Download Entire Dataset

download_pathomics()

Download Only ST Data

download_pathomics(
    ids=["NCBI689"],
    modalities=["st"]
)

Acknowledgements

HEST
CellViT
Hugging Face
Spatial transcriptomics community
Original data contributors

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Total file size:

1.63 TB