Single cell analysis coupled with TCR sequencing of gastric tumor reveals complex intercellular interaction and an alternative T cell exhaustion trajectory

Code for the central analyses in the study.

Environment

There is no required non-standard hardware.
Red Hat 4.8.5-16, Linux version 3.10.0; R version 3.5.1; Python version 3.6.7;

Install software

This should take less than half an hour, with fast internet speed (e.g. 100 Mbps).

Install basic python packages

pip install numpy==1.19.5 pandas==1.1.5 matplotlib==3.3.4 seaborn==0.11.1 scipy==1.5.4

Install python package scanpy

pip install scanpy==1.7.2

Install python package scvelo

pip install scvelo==0.2.3

Install python package pySCENIC

pip install pyscenic==0.11.0

Install R package InferCNV

if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("infercnv")

Insatll software CellPhoneDB v2.00

pip install cellphonedb==2.00

Demo data

Files in the ./Data folder are small real data from the study.

Full data

The expression matrices and annotations can be download from: https://ngdc.cncb.ac.cn/omix/release/OMIX001073

Runing the code

The scripts with .py and .r should be tested in an interactive console.
Instructions, expected outputs, and expected runtime are annotated in the scripts.
The way to run on full data is the same as that for the demo data.
We recommend to use absolute paths for files in the scripts.

Possible issue

If there is any error with the umap module inside scanpy, reinstall umap-learn with conda

pip uninstall umap-learn
conda install umap-learn

Zenodo DOI

Publication

Sun, K., Xu, R., Ma, F. et al. scRNA-seq of gastric tumor shows complex intercellular interaction with an alternative T cell exhaustion trajectory. Nat Commun 13, 4943 (2022). https://doi.org/10.1038/s41467-022-32627-z