spatial in-situ imaging-based
Pythonconda environment for in-situ imaging-based spatial transcriptomics based on <a href="https://spatialdata.scverse.org/en/latest/api.html">scverse SpatialData structure</a>. conda create -p /data/analysis/data_lebrigand/0-envs/spatial python==3.11 conda activate spatial pip install git+https://github.com/cobioda/scispy.git@main pip install sopa pip install novae pip install rapids-singlecell pip install 'rapids-singlecell[rapids11]' --extra-index-url=https://pypi.nvidia.com
This environment can run on:
Bego
Directly hosted at IPMC, Bego offers the possibility to run statistical analyses by running Python notebooks via VSCode software and SSH remote connexion, as well as by connecting to the Rstudio server
Configuration: HPE DL380 2 x Xeon-G 6248 2x20 cores, 2Tb RAM, 40Tb disk, 1 GPU Nvidia A100, Debian
Guidelines: To connect to Bego, you will need to be granted a Linux account by Kevin Lebrigand (lebrigand@ipmc.cnrs.fr) which is Bego's administrator. To use Bego, you need to be comfortable with the Linux command line and the rules of good practice on a shared computing cluster.
