---
license: mit
task_categories:
- other
tags:
- spatial-transcriptomics
- xenium
- spatial-omics
- bioinformatics
pretty_name: DeepSpaceDB Xenium Preview
---

# DeepSpaceDB Xenium Preview

A collection of processed 10x Genomics Xenium spatial transcriptomics samples, curated from public GEO
depositions, stored as [SpatialData](https://spatialdata.scverse.org/) zarr stores.

**This is a small preview** of the full DeepSpaceDB Xenium collection: 3 human breast cancer samples
(DSIDX00001-DSIDX00003, GEO series GSE243168), each provided as an unfiltered `data/` zarr and an analysis-ready `processed/` zarr (see below). `metadata.csv` lists only these samples.

## Structure

```
metadata.csv          # one row per sample: organ, condition, species, source, quality, etc.
data/
  DSIDX00001.zarr.zip  # one zipped SpatialData zarr store per sample (unfiltered)
  ...
processed/
  DSIDX00001.processed.zarr.zip  # same samples, filtered / normalized / clustered / annotated
  ...
```

Each `<id>.zarr.zip` (and `<id>.processed.zarr.zip`) is a [SpatialData](https://spatialdata.scverse.org/) store containing (where available):
- `images`: `morphology_focus` (DAPI/nuclear stain) and `he_image` (H&E, aligned via an `Affine`
  transform to the same coordinate space as the other elements)
- `labels`: `cell_labels`, `nucleus_labels` (raster segmentation masks)
- `shapes`: `cell_boundaries`, `nucleus_boundaries`, `cell_circles`
- `points`: `transcripts` (per-molecule detections)
- `tables`: `table` (cell x gene raw count matrix, AnnData)

## `data/` vs `processed/`

Both folders hold the same samples as SpatialData zarr stores with the same images, labels, shapes and
transcripts. They differ in the `tables/table` AnnData (cell x gene table):

| | `data/` | `processed/` |
|---|---|---|
| Cells | all cells from the Xenium output (DSIDX00001: 167,780) | after cell-level QC filtering (DSIDX00001: 164,000) |
| Expression | raw counts in `X` only | `X` plus `layers`: `counts`, `normalized`, `log1p` |
| Embeddings / graph | none | `obsm`: `X_pca`, `X_umap`; `varm`: `PCs`; `obsp`: `connectivities`, `distances` |
| Clustering | none | `obs.leiden` (Leiden clusters), `obs.banksy_domain` (BANKSY spatial domains) |
| Cell types | none | `obs.predicted_cell_type`, `obs.main_cell_type`, `obs.cell_type_correlation` |
| Per-cell QC | Xenium metrics only | adds `n_counts`, `n_genes_by_counts`, `log1p_n_genes_by_counts`, `log1p_total_counts`, `x`, `y` |
| Per-gene stats | none | `var`: `mean_counts`, `log1p_mean_counts`, `total_counts`, `log1p_total_counts`, `n_cells`, `n_cells_by_counts`, `pct_dropout_by_counts` |
| `uns` | `spatialdata_attrs` | adds `leiden`, `log1p`, `pca`, `umap` |

So `data/` is the minimal, unfiltered Xenium output converted to SpatialData, and `processed/` is the
analysis-ready version: filtered, normalized, embedded, clustered and annotated. Cell types are
assigned per Leiden cluster (by correlation to a reference), so all cells in a cluster share one label.
`morphology_mip` is omitted from both. Files are named `processed/<id>.processed.zarr.zip` and load
exactly like `data/` (extract the zip, then `sd.read_zarr`).

## Loading a sample

`spatialdata.read_zarr()` cannot currently read directly from a `.zip` (this is an open upstream
limitation - see [scverse/spatialdata#1141](https://github.com/scverse/spatialdata/pull/1141)) - extract
first, then read normally:

```python
import zipfile
import spatialdata as sd

zipfile.ZipFile("DSIDX00001.zarr.zip").extractall("DSIDX00001.zarr")
sdata = sd.read_zarr("DSIDX00001.zarr")
```

## Selecting samples by metadata

`metadata.csv` lets you filter before downloading any zarr data:

```python
import pandas as pd
from huggingface_hub import hf_hub_download

meta = pd.read_csv(hf_hub_download(repo_id="vhoncharuk/DeepSpaceDB-Preview", repo_type="dataset", filename="metadata.csv"))
breast_samples = meta[meta.organ == "breast"]

for dsidx_id in breast_samples["id"]:
    hf_hub_download(repo_id="vhoncharuk/DeepSpaceDB-Preview", repo_type="dataset", filename=f"data/{dsidx_id}.zarr.zip")
```

## Metadata columns

`id` (internal accession), `sample_id` (GEO GSM or source sample name), `series_id` (GEO GSE, or
source collection), `organism`, `condition` / `condition_detailed` (+ Disease Ontology IDs), `organ` /
`organ_detailed` (+ UBERON IDs), `sex`, `age_or_stage`, `ethnicity_or_strain`, `description`, `source`,
`URL`, `PMID`, `date_published`, `has_manual_annotation`, `platform`, `quality`.