ms2ldaviz

A web application developed in Django+D3 to visualise how topics inferred from Latent Dirichlet Allocation can be used to assist in the unsupervised characterisation of fragmented (LC-MS-MS) metabolomics data.

Demo available at http://ms2lda.org

Run it for development

pipenv --python 2.7
pipenv install
pipenv shell
cd ms2ldaviz
export DJANGO_SETTINGS_MODULE=ms2ldaviz.settings_redisdebug

In their own shell (within pipenv shell) run:

docker run --name some-redis -d -p 6379:6379 redis
docker run --name some-pg -d -p 5432:5432 -e POSTGRES_PASSWORD=j7z3rL40w9 -e POSTGRES_USER=django postgres

and

./start_celery_redisdebug.sh

and

python manage.py migrate
python manage.py createsuperuser
python setup_feat.py
python manage.py runserver

Goto http://localhost:8000

Run gensim lda

Requires server to be up and running.

Performs 3 steps:

1. Generate corpus/features from MS2 file
2. Run lda using gensim
3. Insert lda result into db

cd ms2ldaviz
./run_gensim.py corpus -f mgf myexp.mgf myexp.corpus.json
./run_gensim.py gensim myexp.corpus.json myexp.ldaresult.json
./run_gensim.py insert myexp.ldaresult.json stefanv myexp

Run gensim with faster insert

This will exclude the lda info from the json file and write/import a gensim formatted lda dataset.

./run_gensim.py corpus -f mgf myexp.mgf myexp.corpus.json
./run_gensim.py gensim --ldaformat gensim myexp.corpus.json myexp.lda.gensim
./run_gensim.py insert_gensim myexp.corpus.json myexp.lda.gensim stefanv myexp

The last command inserts the gensim lda results into the database. This can also be done by using the web interface by going to /uploads/upload_gensim_experiment/ url on the ms2lda server. The gensim result must be tarballed with for example tar -zcf myexp.lda.gensim.tar.gz myexp.lda.gensim* and then uploaded in the form.

Docker

Run ms2lda website using docker-compose with

# Make sure lda/ is filled
docker-compose up -d
# For first time initialize db with
docker-compose run web python manage.py migrate
docker-compose run web python manage.py createsuperuser
docker-compose run web python setup_feat.py

Goto http://localhost:8001 to visit site

To run on different port then 8001 use PORT=8123 docker-compose up -d.

To clean up run

docker-compose down