1. SCIV usage

1.1 Install

conda create --name sciv python=3.10
conda activate sciv
pip install sciv==0.0.111b1

1.2 SCIV execution process

1.2.1 Download scATAC-seq sample data

Download PBMC case file： GSE139369_ELM_sim_snapATAC2.h5ad

import sciv
adata = sciv.dl.read_sc_atac_file()

1.2.2 Download trait example data

Download the fine-mapping results for monocytes, red blood cells, CD4+ and CD8+ T cells

variants, trait_info = sciv.dl.read_trait_file()

1.2.3 Run SCIV

Create Python file:

touch sciv_pbmc.py

The file content is as follows:

# -*- coding: UTF-8 -*-

import sciv

if __name__ == '__main__':

    # scATAC-seq data
    adata = sciv.dl.read_sc_atac_file()

    # read variant information
    variants, trait_info = sciv.dl.read_trait_file()

    # run
    trs = sciv.ml.core(
        adata=adata,
        variants=variants,
        trait_info=trait_info,
        save_path="./result",
        model_dir="./result/poisson_vi",
        is_file_exist_loading=True
    )

    print(trs)

Executable the file:

python3 sciv_pbmc.py

The output log information is as follows:

python3 sciv_pbmc.py