scDRS#
scDRS (single-cell disease-relevance score) is a method for associating individual cells in scRNA-seq data with disease GWASs, built on top of AnnData and Scanpy.
Check out our manuscript Zhang*, Hou*, et al. “Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data.
Explore results for 74 diseases/traits and the TMS FACS data on cellxgene.
Installation#
git clone https://github.com/martinjzhang/scDRS.git
cd scDRS
pip install -e .
Quick test:
python -m pytest tests/test_CLI.py -p no:warnings
Install via PyPI
pip install scdrs==1.0.2
Quick test for PyPI installation: open Python (>=3.5) and run the code in the Usage section below.
Usage#
Use scDRS command-line interface (CLI) for standard analyses.
Use scDRS Python API for customized analyses.
Here is a toy example for computing scDRS scores.
import os
import pandas as pd
import scdrs
DATA_PATH = scdrs.__path__[0]
H5AD_FILE = os.path.join(DATA_PATH, "data/toydata_mouse.h5ad")
COV_FILE = os.path.join(DATA_PATH, "data/toydata_mouse.cov")
GS_FILE = os.path.join(DATA_PATH, "data/toydata_mouse.gs")
# Load .h5ad file, .cov file, and .gs file
adata = scdrs.util.load_h5ad(H5AD_FILE, flag_filter_data=False, flag_raw_count=False)
df_cov = pd.read_csv(COV_FILE, sep="\t", index_col=0)
df_gs = scdrs.util.load_gs(GS_FILE)
# Preproecssing .h5ad data compute scDRS score
scdrs.preprocess(adata, cov=df_cov)
gene_list = df_gs['toydata_gs_mouse'][0]
gene_weight = df_gs['toydata_gs_mouse'][1]
df_res = scdrs.score_cell(adata, gene_list, gene_weight=gene_weight, n_ctrl=20)
print(df_res.iloc[:4])
Expected results:
index |
raw_score |
norm_score |
mc_pval |
pval |
nlog10_pval |
zscore |
|---|---|---|---|---|---|---|
N1.MAA000586.3_8_M.1.1-1-1 |
4.741197 |
6.3260064 |
0.04761905 |
0.0016638935 |
2.7788744 |
2.9357162 |
F10.D041911.3_8_M.1.1-1-1 |
4.739066 |
5.916272 |
0.04761905 |
0.0016638935 |
2.7788744 |
2.9357162 |
A17_B002755_B007347_S17.mm10-plus-7-0 |
4.6366262 |
5.5523157 |
0.04761905 |
0.0016638935 |
2.7788744 |
2.9357162 |
C22_B003856_S298_L004.mus-2-0-1 |
4.6805663 |
7.2986684 |
0.04761905 |
0.0016638935 |
2.7788744 |
2.9357162 |
G12.B002765.3_38_F.1.1-1-1 |
4.640043 |
5.7792473 |
0.04761905 |
0.0016638935 |
2.7788744 |
2.9357162 |
H5.B003278.3_38_F.1.1-1-1 |
4.4457436 |
-0.5613674 |
0.7619048 |
0.687188 |
0.16292442 |
-0.48789537 |
O14.MAA000570.3_8_M.1.1-1-1 |
4.4552336 |
-1.5821338 |
0.95238096 |
0.9467554 |
0.023762206 |
-1.6141763 |
J21.B000634.3_56_F.1.1-1-1 |
4.4433637 |
-2.3119287 |
1.0 |
0.9916805 |
0.0036282123 |
-2.3945906 |
E5.B002765.3_38_F.1.1-1-1 |
4.4870768 |
1.1566308 |
0.23809524 |
0.13311148 |
0.87578446 |
1.1118028 |
K20_B000268_B009896_S260.mm10-plus-4-0 |
4.53548 |
-3.1656132 |
1.0 |
1.0 |
-0.0 |
-10.0 |
Examples#
Coming soon