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.
TCellAtlas is a comprehensive scRNA-seq database focused on T cells, containing 1,654,675 high-quality T cells from 328 samples across 35 conditions and 16 tissues, as well as 47,215 T cells from Smart-seq projects. It includes all 68 T cell subtypes and states, making it the most extensive T cell reference database. Information of STCAT can be also found in our TCellAtlas portal. The database is accessible at TCellAtlas.
conda create -n STCAT python=3.9.16 pandas=2.1.2
conda activate STCAT
pip install STCATSTCAT 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)Here is an example for guidance, and the demo.h5ad file mentioned in the example can be found below. Tutorial