sc2heoca

A method for querying newly generated organoid single-cell RNA sequencing (scRNA-seq) data to the Human Endoderm Organoid Cell Atlas (HEOCA). HEOCA is an integrated single-cell transcriptome collection that includes 218 samples from organoids derived from various endoderm tissues, including the lung, pancreas, intestine, liver, biliary system, stomach, and prostate. This current version of HEOCA includes nearly one million cells from a variety of conditions, data sources, and protocols.

Citation

An integrated transcriptomic cell atlas of human endoderm-derived organoids. Quan Xu, Lennard Halle, Soroor Hediyeh-zadeh, Merel Kuijs, Umut Kilik, Qianhui Yu, Tristan Frum, Lukas Adam, Shrey Parikh, Manuel Gander, Raphael Kfuri-Rubens, Dominik Klein, Zhisong He, Jonas Simon Fleck, Koen Oost, Maurice Kahnwald, Silvia Barbiero, Olga Mitrofanova, Grzegorz Maciag, Kim B. Jensen, Matthias Lutolf, Prisca Liberali, Joep Beumer, Jason R. Spence, Barbara Treutlein, Fabian J. Theis, J. Gray Camp. bioRxiv 2023.11.20.567825; doi: https://doi.org/10.1101/2023.11.20.567825

Install

conda create -n sc2heoca python==3.9.16
conda activate sc2heoca

git clone [email protected]:devsystemslab/sc2heoca.git
pip install sc2heoca/

Prepare reference atlas and model

• Download the reference atlas from Zenodo

mkdir -p heoca_atlas
cd heoca_atlas
wget https://zenodo.org/records/10977447/files/gut_scpoli_integration.h5ad

• Download the reference model from Zenodo

wget https://zenodo.org/record/8186773/files/heoca_scpoli_model.v1.0.zip
tar xvzf heoca_scpoli_model.v1.0.zip -C heoca_atlas
rm heoca_scpoli_model.v1.0.zip

• Reference atlas and available reference models from HEOCA project

  • HEOCA atlas (all organoids)
  • HEOCA model (all organoids)
  • HIOCA model (intestine organoid)
  • HLOCA model (lung organoid)
  • HICA model (intestine tissue)

Query new organoid single cell RNA-seq data

Example data download from GEO(GSM5628936)

import scanpy as sc
from sc2heoca.sc2heoca import Query

• Load new sample

adata_query = sc.read_10x_mtx(path='Chan_NatCommun_2022', 
                              prefix='GSM5628936_SCNPO2-')

• Query new sample

model_dir = "heoca_atlas"
query = Query(model_dir=model_dir, load_ref=True)
adata_query = query.run_scpoli(adata_query=adata_query, 
                               sample_name='GSM5628936_SCNPO2')

• Plot query result UMAP

adata4plot = query.merge4plot(adata_query)
sc.pl.umap(adata4plot, color=['predict_level_2'], palette=query.colorpalette,
           frameon=False, size=5)

• Find DE genes to HECOA

de_res = query.find_de_genes(adata_query, tissue='lung')

Help and Support

• The preferred way to get support is through the Github issues page.