---
license: cc-by-nc-sa-4.0
pretty_name: CELLO 10X-Xenium-52
tags:
  - spatial-transcriptomics
  - histology
  - pathology
  - xenium
  - single-cell
  - biology
size_categories:
  - 100K<n<1M
---

# CELLO 10X-Xenium-52

Preprocessed data for **CELLO**, which predicts the gene expression of every cell in an H&E
whole-slide image from the image and the cell locations.

The dataset pairs 52 H&E whole-slide images with single-cell Xenium spatial transcriptomics from
[HEST-1k](https://huggingface.co/datasets/MahmoodLab/hest). It covers 12 organs and about 9.5 million
cells, split by sample:

| split | samples | organs | tiles | cells |
|---|---|---|---|---|
| train | 36 | Bowel, Breast, Kidney, Liver, Lung, Lymphoid, Pancreas, Skin | 381,775 | 5,862,560 |
| val | 4 | Bowel, Breast, Lung | 23,412 | 669,472 |
| test ID | 6 | Lung, Breast, Bowel, Skin, Pancreas, Lymphoid | 59,571 | 1,614,773 |
| test OOD | 6 | Brain, Bone, Heart, Ovary; Kidney, Liver | 55,644 | 1,383,418 |

The OOD test samples come from organs (Brain, Bone, Heart, Ovary) or health conditions (Kidney,
Liver) that do not appear in training. `samples.csv` lists the organ, condition, split and source of
every sample.

## Download

With the CELLO code, one command downloads everything and unpacks the tiles into `data/xenium52`:

```bash
python scripts/download_data.py            # add --split test for the test samples only
```

Without the code:

```bash
huggingface-cli download gaozijun/cello_data --repo-type dataset --local-dir data/xenium52
cd data/xenium52/tiles && for f in *.tar; do tar -xf "$f" && rm "$f"; done
```

## Contents

```
all_patches.csv                    one row per tile; paths are relative to the dataset root
tiles/<id>.tar                     the tiles of one sample; unpacks to tiles/<id>/
st/<id>.h5ad                       all cells of one sample (AnnData)
splits/train_val_test_split.csv    sample id -> train / val / test
samples.csv                        per-sample metadata and data source
gene_vocab.csv                     gene vocabulary predicted by the model (1,915 genes)
gene_rankings/<id>.json            highly variable (hvg) and spatially variable (svg) gene rankings
                                   of the val and test samples
```

**Tiles.** Each whole-slide image is cut into non-overlapping 224×224 tiles, and a tile is kept if it
contains at least one cell. Each tile has two files named `<id>_<x>_<y>`, where `(x, y)` is its
top-left corner in the slide:

- `.png`: the H&E image.
- `.npz`: the cells in the tile, with three arrays:
  - `coords`: the (x, y) position of each cell inside the tile.
  - `X`: expression as `log1p` of counts normalised to 100 per cell.
  - `genes`: the gene names of the sample's Xenium panel.

**AnnData.** Each `st/<id>.h5ad` stores cell centroids in `obsm['spatial']`, raw counts in
`layers['counts']` and the normalised expression in `X`.

Gene panels differ between samples and contain 300–500 genes each.

## Licence

This dataset is derived from HEST-1k and is released under the same licence,
[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). The underlying Xenium
datasets were published by 10x Genomics and on NCBI GEO; `samples.csv` gives the original study and
licence of each sample.