SPAC is a scalable, automated pipeline, under the Single Cell Spatial Analysis Workflow (SCSAWorkflow) project aiming at analyzing single-cell spatial protein data of multiplexed whole-slide tissue images generated from technologies such as MxIF Codex and Imaging Mass Cytometry (IMC). This Python-based package leverages the anndata framework for easy integration with other single-cell toolkits. It includes a multitude of functional and visualization modules, test utilities, and is capable of running in user-friendly web interfaces. Spac offers insights into cell interactions within various environments, aiding in studies of the cancer microenvironment, stem cell niches, and drug response effects etc.
Run the following command to establish the Conda environment supporting usage and contribution to spac package:
cd <home directory of SCSAWorkflow folder>
# If conda is not activate
conda activate
# Adding constumized scimap conda pacakge channel supported by DMAP
conda config --add channels https://fnlcr-dmap.github.io/scimap/
# Create the Conda environment from environment.yml
conda env create -f environment.yml
# Once environment is established
conda activate spacThe envrionment works for Linux and noarc, if your are working on amd processor (commonly seen for latest Mac users), please replace the - numpy=1.19.5 with numpy>=1.19.5,<2.0.0
If error occurs suggesting SSL certificate not found for our scimap channel, please run the following command before the environment creation:
conda config --set ssl_verify false
Then set the verification to True after the installation:
conda config --set ssl_verify true
Review the developer guide
spac was created by Fang Liu, Rui He, and George Zaki. It is licensed under the terms of the BSD 3-Clause license.
spac was created with cookiecutter and the py-pkgs-cookiecutter template.