Xenium

Human pancreas

PanIN and CAF Transitions in Pancreatic Carcinogenesis Revealed with Spatial Data Integration [Xenium]: PanIN1131_S1C (Pancreatic Intraductal Neoplasm, human, pancreas)

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Xenium

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'morphology_focus': DataTree[cyx] (1, 27354, 25721), (1, 13677, 12860), (1, 6838, 6430), (1, 3419, 3215), (1, 1709, 1607)
│     └── 'morphology_mip': DataTree[cyx] (1, 27354, 25721), (1, 13677, 12860), (1, 6838, 6430), (1, 3419, 3215), (1, 1709, 1607)
├── Labels
│     ├── 'cell_labels': DataTree[yx] (27354, 25721), (13677, 12860), (6838, 6430), (3419, 3215), (1709, 1607)
│     └── 'nucleus_labels': DataTree[yx] (27354, 25721), (13677, 12860), (6838, 6430), (3419, 3215), (1709, 1607)
├── Points
│     └── 'transcripts': DataFrame with shape: (6257466, 10) (3D points)
├── Shapes
│     ├── 'cell_boundaries': GeoDataFrame shape: (55524, 1) (2D shapes)
│     ├── 'cell_circles': GeoDataFrame shape: (55524, 2) (2D shapes)
│     └── 'nucleus_boundaries': GeoDataFrame shape: (55524, 1) (2D shapes)
└── Tables
      └── 'table': AnnData (55524, 380)
with coordinate systems:
    ▸ 'global', with elements:
        morphology_focus (Images), morphology_mip (Images), cell_labels (Labels), nucleus_labels (Labels), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("hf2mHsCQtXNtGZ2F0000")
ds.elements()          # inspect without downloading
sdata = ds.to_sdata()  # cached locally, reused
print(sdata)

Install (Python 3.12+) Docs →

Python · lamindb

Needs pip install lamindb and network access to the Lamin database, which is limited during the alpha. If it fails, use the Client tab.

import lamindb as ln

db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("hf2mHsCQtXNtGZ2F")   # stem uid: Lamin resolves it to the latest version

sdata = artifact.load()   # cache, then load into memory
group = artifact.open()   # stream without downloading
path  = artifact.cache()  # sync into the local cache, returns the path

Install (Python 3.12+) Docs →

Shell

# sddb client: download the .zarr into data/
sddb-cli download hf2mHsCQtXNtGZ2F0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/hf2mHsCQtXNtGZ2F

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid hf2mHsCQtXNtGZ2F

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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Part of collection

geo-gse267680→

5 datasets · publication

Metadata

Technology: Xenium
Vendor: 10x Genomics
Organism: human
Tissue: pancreas
Modality: RNA
Observations (cells): 55,524
Transcripts: 6,257,466
Target genes: 380
Tier: silver
Size: 1.7 GB
Sample ID: GSM8273020
License: NCBI GEO policy
Published: May 20, 2024

Cite this dataset

doi.org/10.1016/j.cels.2024.07.001 ↗

BibTeX

@article{geo:GSE267680,
  doi = {10.1016/j.cels.2024.07.001},
  title = {PanIN and CAF Transitions in Pancreatic Carcinogenesis Revealed with Spatial Data Integration [Xenium]: PanIN1131_S1A (Pancreatic Intraductal Neoplasm, human, pancreas)},
  year = {2024}
}

Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB