STCAT

STCAT is an automated T cell type annotation tool for scRNA-seq datasets. It based on a high-confidence T cell subtypes and states reference. The reference can be found in our TCellAtlas portal. STCAT can automatically annotate T cell subtypes and states for scRNA-seq data of different conditions and tissues.

TCellAtlats Website

TCellAtlas contains 1,677,799 high-quality T cells of 339 samples from 38 10x Genomics projects across 37 conditions and 16 tissues. It also includes 47,215 high-quality T cells in 21 conditions and 8 tissues from 18 Smart-seq projects. TCellAtlas contains all 68 T cell subtypes/states, which makes it the most comprehensive T cell subtypes/states and T cell database with the largest number of cells. Information of STCAT can be also found in our TCellAtlas portal. The database is accessible at TCellAtlas. Our TCellAtlas portal provides STCAT online services, which you can click here to access the service.

PyPI Page

STCAT homepage on PyPI: https://pypi.org/project/STCAT/

Install STCAT

1.Create environment

conda create -n STCAT python=3.9.16
conda activate STCAT

2.Install using pip

pip install STCAT

Usage

STCAT expects a raw count matrix as input and can be implemented with only one line of code in Python. STCAT expects to use an Anndata object ( .h5ad file format ) as input, and at the same time, a raw count matrix ( reads or UMIs ) is required. The file input is in a cell-by-gene format ( cells as rows and genes as columns ). For more information, please see anndata. The barcode should be unique for each cell, with no duplicates. As for the annotation result, STCAT will be automatically added to the common anndata format of scRNA-seq analysis for easy viewing.

import scanpy as sc
import STCAT
adata = sc.read_h5ad(<file_path>)
results = STCAT.STCAT(adata)

Example:

Here is an example for guidance, and the demo.h5ad file mentioned in the example can be found below. Tutorial

demo.h5ad file in Tutorial

demo.h5ad

Citation

An automatic annotation tool and reference database for T cell subtypes and states at single-cell resolution. Wen-Kang Shen, Chu-Yu Zhang, Yi-Min Gu, Tao Luo, Si-Yi Chen, Tao Yue, Gui-Yan Xie, Yu Liao, Yong Yuan, Qian Lei, and An-Yuan Guo, Science Bulletin. 2025 Mar. https://doi.org/10.1016/j.scib.2025.02.043