Spanve: A Statistical Method for Detecting Downstream-friendly Spatially Variable Genes in Large-scale Spatial Transcriptomics Data
@preprint{b.23.SpanveStatistical,
title = {Spanve: An {{Statistical Method}} to {{Detect Clustering-friendly Spatially Variable Genes}} in {{Large-scale Spatial Transcriptomics Data}}},
author = {{Guoxin Cai} and {Yichang Chen} and {Shuqing Chen} and {Xun Gu} and {Zhan Zhou}},
date = {2023-01-01},
journaltitle = {bioRxiv},
pages = {2023.02.08.527623},
doi = {10.1101/2023.02.08.527623},
url = {http://biorxiv.org/content/early/2023/03/08/2023.02.08.527623.abstract},
}
Analysis code for the paper is available at Evaluate
directory.
- Install by pip (recommend):
pip install Spanve
- no install usage:
# install required packages
cd Evaluate/Softs
pip install spanve_requirements.txt
cp Spanve.py $your_path
spanve --help
Usage: Spanve [OPTIONS]
Options:
-i, --input_file PATH input anndata file(h5ad file.)
-r, --running_mode TEXT running mode, default is f(c:cluster;
i:imputation; f:fitting)
-s, --save_path PATH save path
-v, --verbose BOOLEAN verbose
-n, --njobs INTEGER
-p, --preprocessed BOOLEAN int preprocessed or not.
--help Show this message and exit.****
command line usage can only run in a standard h5ad file, where there is a anndata.AnnData.obsm
key named 'spatial'
.
from Spanve import Spanve
adata = sc.read_h5ad('data.h5ad')
spanve = Spanve(adata)
# -- fitting for spatial genes
spanve.fit()
spanve.save('result.csv')
# -- spatial imputation
X = adata_preprocess(adata)
X_ = spanve.impute_from_graph(X[:,spanve.rejects])
see tutorial notebook or html page.