Human brain
GSM8376639
human brain melanoma 13.6 MB 1,732 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuebrain
Conditionmelanoma
Spots1,732
Magnificationother · 4.139 µm/px
bronze H&E NCBI GEO policy
Collections / spatialGE is a User-Friendly Web Application that Facilitates Spatial Transcriptomics Data Analysis
spatialGE is a User-Friendly Web Application that Facilitates Spatial Transcriptomics Data Analysis
Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, the advanced data analysis and programming skills required can hinder researchers from realizing the full potential of ST. To address this, we developed spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provided a user-friendly interface that guides users without programming expertise through various analysis pipelines, including quality control, normalization, domain detection, phenotyping, and multiple spatial analyses. It also enabled comparative analysis among samples and supported various ST technologies. The utility of spatialGE was demonstrated through its application in studying the tumor microenvironment of two data sets: 10× Visium samples from a cohort of melanoma metastasis and NanoString CosMx fields of vision from a cohort of Merkel cell carcinoma samples. These results support the ability of spatialGE to identify spatial gene expression patterns that provide valuable insights into the tumor microenvironment and highlight its utility in democratizing ST data analysis for the wider scientific community.
Datasets2
Size~23.1 MB
AssayVisium
Samplehuman · brain
KindOther
Download the whole collection
Python · sddb client
from sddb import Catalog
cohort = Catalog().collection("geo-gse271422")
cohort.members # the 2 datasets as a table
print(cohort.licenses()) # check the terms before you download
sdatas = cohort.to_dict() # {uid: SpatialData}, cached locally
# or keep the .zarr files in a folder of your choice:
# sdatas = cohort.to_dict(dest="data/") 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")
coll = db.Collection.get(key="geo-gse271422")
coll.ordered_artifacts.to_dataframe() # the 2 members, ordered
# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()] Shell
# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse271422 --dest data/Licenses: 2 NCBI GEO policy
Member datasets · 2
Human brain
GSM8376640
human brain melanoma 9.5 MB 1,302 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuebrain
Conditionmelanoma
Spots1,302
bronze H&E NCBI GEO policy
Cite this collection
doi.org/10.1158/0008-5472.can-24-2346 ↗ BibTeX
@article{Ospina_2024,
title = {spatialGE Is a User-Friendly Web Application That Facilitates Spatial Transcriptomics Data Analysis},
volume = {85},
ISSN = {1538-7445},
url = {http://dx.doi.org/10.1158/0008-5472.can-24-2346},
DOI = {10.1158/0008-5472.can-24-2346},
number = {5},
journal = {Cancer Research},
publisher = {American Association for Cancer Research (AACR)},
author = {Ospina, Oscar E. and Manjarres-Betancur, Roberto and Gonzalez-Calderon, Guillermo and Soupir, Alex C. and Smalley, Inna and Tsai, Kenneth Y. and Markowitz, Joseph and Khaled, Mariam L. and Vallebuona, Ethan and Berglund, Anders E. and Eschrich, Steven A. and Yu, Xiaoqing and Fridley, Brooke L.},
year = {2024},
month = Dec,
pages = {848–858}
}