We designed a Nextflow DSL2-based pipeline, Spatial Transcriptomics Quantification (STQ), for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a matched hematoxylin and eosin (H&E)-stained whole slide image (WSI), optimized for Patient-Derived Xenograft (PDX) cancer specimens. Our pipeline enables the classification of sequenced transcripts for deconvolving the mouse and human species and mapping the transcripts to reference transcriptomes. We align the H&E WSI with the spatial layout of the Visium slide and generate imaging and quantitative morphology features for each Visium spot. The pipeline design enables multiple analysis workflows, including single or dual reference genomes input and stand-alone image analysis. We show the utility of our pipeline on a dataset from Visium profiling of four melanoma PDX samples. The clustering of Visium spots and clustering of H&E imaging features reveal similar patterns arising from the two data modalities.
from sddb import Catalog
cohort = Catalog().collection("geo-gse238004")
cohort.members # the 4 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/")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-gse238004")
coll.ordered_artifacts.to_dataframe() # the 4 members, ordered
# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse238004 --dest data/Licenses: 4 NCBI GEO policy
@article{Domanskyi_2024,
title = {Nextflow pipeline for Visium and H&E data from patient-derived xenograft samples},
volume = {4},
ISSN = {2667-2375},
url = {http://dx.doi.org/10.1016/j.crmeth.2024.100759},
DOI = {10.1016/j.crmeth.2024.100759},
number = {5},
journal = {Cell Reports Methods},
publisher = {Elsevier BV},
author = {Domanskyi, Sergii and Srivastava, Anuj and Kaster, Jessica and Li, Haiyin and Herlyn, Meenhard and Rubinstein, Jill C. and Chuang, Jeffrey H.},
year = {2024},
month = May,
pages = {100759}
}