| --- |
| 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`. |
|
|
