SEVtras 0.2.13

sEV-containing droplet identification in scRNA-seq data

SEVtras delineates small extracellular vesicles at droplet resolution from single-cell transcriptomes

SEVtras stands for sEV-containing droplet identification in scRNA-seq data.

You can freely use SEVtras to explore sEV heterogeneity at single droplet, characterize cell type dynamics in light of sEV activity and unlock diagnostic potential of sEVs in concert with cells.

Overview of SEVtras.

Prerequisites

"numpy", "pandas", "scipy", "umap",
"statsmodels", "gseapy", "scanpy"

Installation

pip install SEVtras

We also suggest to use a separate conda environment for installing SEVtras.

conda create -y -n SEVtras_env python=3.7
source activate SEVtras_env
pip install SEVtras

Simple Example

The pipeline of SEVtras only composed two parts: sEV_recognizer and ESAI_calculator.

Part I:

SEVtras.sEV_recognizer(input_path='./tests', sample_file='./tests/sample_file', out_path='./outputs', species='Homo')

Part II:

SEVtras.ESAI_calculator(adata_ev_path='./outputs/sEV_SEVtras.h5ad', adata_cell_path='./tests/adata_cell.h5ad', out_path='./outputs', Xraw=False, OBSsample='batch', OBScelltype='celltype')

Further tutorials please refer to https://SEVtras.readthedocs.io/.

Citation

He, R., Zhu, J., Ji, P. et al. SEVtras delineates small extracellular vesicles at droplet resolution from single-cell transcriptomes. Nat Methods 21, 259-266 (2024). https://doi.org/10.1038/s41592-023-02117-1