Installation

SPLISOSM can be installed via pip:

# from PyPI (stable version)
$ pip install splisosm

# or from GitHub (latest version)
$ pip install git+https://github.com/JiayuSuPKU/SPLISOSM.git#egg=splisosm

Minimal software dependencies:

torch
scipy
pandas
tqdm
matplotlib
anndata
smoother-omics

We also provide a Python wrapper, splisosm.utils.run_sparkx(), for the gene-level spatial variability test SPARK-X. To use this functionality, ensure that the conda environment where SPLISOSM is installed has R (>=4.0) available in PATH, and that the R package SPARK-X is properly installed.

# install R and the R package SPARK-X in the conda environment
$ conda install -c conda-forge r-base r-devtools
$ Rscript -e "devtools::install_github('xzhoulab/SPARK')"

# install the rpy2 package to interface R from python
$ pip install rpy2
$ pip install splisosm # if not already installed

# test whether SPARK-X is correctly configured
$ python -c "import numpy as np; from splisosm.utils import run_sparkx; run_sparkx(np.random.randn(10,5), np.random.rand(10,2))"

Additionally, the new gene-level spatial variability test, HSIC-GC, is available as a standalone function (splisosm.utils.run_hsic_gc()). It can be used as a drop-in replacement for SPARK-X, where we optimize the spatial kernel to achieve higher statistical power while maintaining computational efficiency.

from splisosm.utils import run_hsic_gc
import numpy as np

# gene expression matrix: (n_spot, n_gene)
gene_counts = np.random.randn(100, 50)

# spatial coordinates: (n_spot, 2)
coordinates = np.random.rand(100, 2)

# run HSIC-GC test
test_results = run_hsic_gc(gene_counts, coordinates)
print(test_results['statistic'])  # test statistics, (n_gene,)
print(test_results['pvalue'])    # p-values, (n_gene,)