Official implementation of Cell Spatial And Neighborhood Pattern (CellSNAP), a computational method that learns a single-cell representation embedding by integrating cross-domain information from tissue samples. Through the analysis of datasets spanning spatial proteomic and spatial transcriptomic modalities, and across different tissue types and disease settings, we demonstrate CellSNAP’s capability to elucidate biologically relevant cell populations that were previously elusive due to the relinquished tissue morphological information from images.
NOTE: this repository is under active development, and the current version is only meant for reviewing and early access testing etc. We will provide more detailed installation instruction and tutorial soon.
CellSNAP is hosted on pypi and can be installed via pip. We recommend working with a fresh virtual environment. In the following example we use conda.
conda create -n cellsnap python=3.9 # create a new vm
conda activate cellsnap # activate cellsnap vm
pip install cellsnap==0.0.4 # install cellsnap in vm