scPositioner v1.1.0

Global optimal-based spatial mapping of single-cell multi-omics

scPositioner is a computational method to map single cells into spatial context and integrate multi omics

Create and activate conda environment with requirements installed.

For scPositioner, the Python version need is over 3.9. If you have already installed a lower version of Python, consider installing Anaconda, and then you can create a new environment.

cd scPositioner-main

conda env create -f environment.yml -n scpositioner
conda activate scpositioner

Install scPositioner

python setup.py build
python setup.py install

Tutorials (single cell spatial mapping)

scPositioner requires the single-cell spatial omics data (stored as .h5ad format) as input, where cell population label of each cell needs to be provided. For spot-level SRT datasets, the deconvolution step should be done in advance.

Here is an example of scPositioner on spot-level SRT reference (10X Visium):

• Demonstration of scPositioner on the spot-level data

An example of scPositioner on single-cell SRT reference (10X Xenium):

• Demonstration of scPositioner on the single-cell data

And an example of scPositioner on multi-omics (snRNA-seq, snATAC-seq, 10X Visium):

• Demonstration of scPositioner on the multi-omics data

Acknowledgements

About

scPositioner is developed by Jingyang Qian and Hudong Bao. Should you have any questions, please contact Hudong Bao at baohd@zju.edu.cn.