gnasello/sc-env repository overview

⁠Standard use

To use this container after pulling you have to run the following command (you might need sudo privileges):

docker run -it --rm -p 7777:7777 --volume $HOME:/home/host_home --workdir /home/host_home --entrypoint /usr/bin/bash --user root gnasello/sc-env:latest

Command shortcut to start jupyter lab inside the container:

jl

Command shortcut to start RStudio Server inside the container:

rs

To login in RStudio server use the following credentials:

username: rstudio password: rstudio

You will create new files and directories as root. Thus, if you want to allow any further user to access everything you created in the current directory, type the following command:

chmod -R 777 ./ 

⁠Run the Docker Image with docker-compose

Instead of the pulling the image and executing the docker run command, you can simply download the docker-compose.yaml file from GitHub⁠.

First, you have to run the docker-composecommand where the .yaml file is located (you might need sudo privileges):

docker-compose up -d

the detached -d mode allows you to continue using the terminal and run the service you have just created:

docker-compose run --service-ports singlecell-environment

When the work is finished, you exit the Docker Container by pressing ctrl + d. You then need to stop and remove your containers as well as any network created.

docker-compose down -v

Where the -v flag removes all volumes.

⁠List of packages

Docker container built on gabnasello/datascience-env⁠

Conda environments added:

sc-R (for Seurat-based analysis)

 - r-essentials
 - r-devtools
 - r-biocmanager
 - r-rcolorbrewer
 - r-remotes
 - r-latex2exp
 - r-r.utils
 - r-rstatix
 - r-ggpubr
 - r-ggsci
 - r-repr
 - r-patchwork
 - r-monocle3
 - bioconductor-scater
 - numba
 - rpy2
 - anndata2ri
 - ipykernel
 - bash_kernel

- Seurat
- SeuratWrappers 
- MAST
- scran
- DropletUtils
- clusterExperiment
- LoomExperiment
- sceasy
- SeuratData

sc-py (for scanpy-based analysis)

 - louvain
 - umap-learn
 - scanpy
 - python-igraph
 - leidenalg
 - rpy2
 - anndata2ri
 - multicore-tsne
 - scvelo
 - opentsne
 - plotly
 - scirpy
 - bash_kernel
 - ipykernel

cell-comm (for cellphonedb-based analysis)

 - python=3.7
 - bash_kernel
 - ipykernel
 - pip
 - pip:
   - cellphonedb

grn-inf (for CellOracle-based analysis)

 - python=3.6
 - r-essentials
 - louvain
 - numba
 - cython
 - pybedtools
 - notebook
 - pysam
 - bash_kernel
 - ipykernel
 - pip
 - pip:
   - git+https://github.com/morris-lab/CellOracle.git

⁠Dockerfile

You find instructions to build this Docker Image in this GitHub page⁠.

⁠Acknowledgements

Thanks to leanderd⁠ and Theis Lab⁠ for inspiring this container.