spatial transcriptomics

Human prostate gland

MEND3

bronze Interactive viewer H&E prostate cancer ✓ Verified publication

Preview Download via Python Download via CLI Load via sddb

print(sdata)

SpatialData object
├── Images
│     └── 'MEND3_full_image': DataTree[cyx] (3, 28672, 32768), (3, 14336, 16384), (3, 7168, 8192), (3, 3584, 4096)
├── Shapes
│     └── 'MEND3_spots': GeoDataFrame shape: (161, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (161, 17279)
with coordinate systems:
    ▸ 'global', with elements:
        MEND3_full_image (Images), MEND3_spots (Shapes)

Load in Python

import lamindb as ln

db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("zemXIEI7CrBNttoY")   # 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

Load from the CLI

# one-shot: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/zemXIEI7CrBNttoY

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid zemXIEI7CrBNttoY

Load with the Python client

# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog

ds = Catalog().cohort(uids=["zemXIEI7CrBNttoY0000"])[0]
ds.elements()                 # inspect without downloading
sdata = ds.load(lazy=False)   # copy to the local cache and open

Docs: finding & loading data →

View on Lamin ↗ Open in Vitessce ↗ Flag a data issue

Part of collection

Prostate needle biopsies pre- and post-ADT: Count matrices, histological-, and Androgen receptor immunohistochemistry images→

24 datasets · comparison

Metadata

Technology: spatial transcriptomics
Organism: human
Tissue: prostate gland
Condition: prostate cancer
Modality: RNA
Observations: 161
Tier: bronze
Chemistry: ST 1k
Magnification: 40x
Resolution: 32,768 × 28,672 px
mpp: 0.276 µm/px
Size: 806 MB
Sample ID: MEND3
License: CC BY-NC-SA 4.0 NonCommercial
Published: Jul 20, 2022

Cite this dataset

doi.org/10.1038/s41467-022-33069-3 ↗

BibTeX

@article{Marklund_2022,
  title = {Spatio-temporal analysis of prostate tumors in situ suggests pre-existence of treatment-resistant clones},
  volume = {13},
  ISSN = {2041-1723},
  url = {http://dx.doi.org/10.1038/s41467-022-33069-3},
  DOI = {10.1038/s41467-022-33069-3},
  number = {1},
  journal = {Nature Communications},
  publisher = {Springer Science and Business Media LLC},
  author = {Marklund, Maja and Schultz, Niklas and Friedrich, Stefanie and Berglund, Emelie and Tarish, Firas and Tanoglidi, Anna and Liu, Yao and Bergenstråhle, Ludvig and Erickson, Andrew and Helleday, Thomas and Lamb, Alastair D. and Sonnhammer, Erik and Lundeberg, Joakim},
  year = {2022},
  month = Sept
}

Source: NCBI GEO ↗ · License: CC BY-NC-SA 4.0 ↗ · Reprocessed to SpatialData by SpatialData-DB