scAVI is a web-based platform developed to enable users to analyze and visualize published and not published single cell RNA sequencing (scRNA-seq) datasets with state-of-the-art algorithms and visualization methods.
The scAVI platform supports the analysis and visualization of 463 publicly available scRNA-seq studies from GEO covering 194,653 single cells/samples. Analyses are provided as 463 dedicated landing pages and Jupyter Notebook reports for each study.
scAVI depends on several external elements including:
The Dockerfile has detailed directions about setting up a system with the necessary R & python dependencies while the docker-compose.yml demonstrates how scavi can connect to the various external components. More likely, you would run those databases on the cloud and configure them.
The .env.example file has some example values, it can be copied over to .env and modified with the credentials and paths you use in your own application prior to using the docker-compose.
For help setting up scavi for yourself it is recommended that you contact us for further direction.
We use nodejs for some of the UI development, for this purpose the npm package manager has a package.json file which identifies all necessary dependencies. Assuming you already have npm installed, this is a simple npm install command. For more information on npm, including installing it, please consult the npm docs.
We use Dockerfile and docker-compose.yml to document the process of constructing a complete virtual system capable of running this application. This includes R and python installations; the docker-compose goes a level higher and documents how scavi interacts with external databases. For more information on using docker-compose, please consult the docker-compose documentation..
# Ensure you install npm dependencies in the static directory prior to building the dockerfile
cd static && npm i
# Then build the dockerfile
docker-compose build scavi
# And run
docker-compose up scavi
# Or deploy
docker-compose push scavi# ensure virtualenv is installed
python2 -m pip install virtualenv
# ensure setup virtualenv
python2 -m virtualenv venv
# establish virtualenv
source venv/bin/activate
# install packages
pip install -r requirements.txt# ensure R stores things here
export R_LIBS=$(pwd)/renv
# setup R environment
R -e "source('setup.R')"cd static
# Install node dependencies
npm i# activate environments
source venv/bin/activate
export R_LIBS=$(pwd)/renv
# export .env variables
set -o allexport
source .env
set +o allexport
# run
bash run.sh