Running Jupyter as public service

Some time ago, I’ve written about setting up IPython as a public service. Today, I’ll write about setting up Jupyter, IPython descendant, that beside Python supports tons of other languages and frameworks.

Jupyter notebook will be running in separate user, so your personal files are safe, but not as system service. Therefore, you will need to restart it upon system reboot. I recommend running it in SCREEN session, so you can easily login into the server and check the Jupyter state.

  1. Install & setup Jupyter
  2. #
    sudo apt-get install build-essential python-dev
    sudo pip install jupyter
    
    # create new user
    sudo adduser jupyter
     
    # login as new user
    su jupyter
    
    # make sure to add `unset XDG_RUNTIME_DIR` to ~/.bashrc
    # otherwise you'll encounter: OSError: [Errno 13] Permission denied: '/run/user/1003/jupyter'
    echo 'unset XDG_RUNTIME_DIR' >> ~/.bashrc
    source ~/.bashrc
    
    # generate ssl certificates
    mkdir ~/.ssl
    openssl req -x509 -nodes -days 999 -newkey rsa:1024 -keyout ~/.ssl/mykey.key -out ~/.ssl/mycert.pem
    
    # generate config
    jupyter notebook --generate-config
    
    # generate pass and checksum
    ipython -c "from IPython.lib import passwd; passwd()"
    # enter your password twice, save it and copy password hash
    ## Out[1]: 'sha1:[your hashed password here]'
     
    # add to ~/.jupyter/jupyter_notebook_config.py
    c.NotebookApp.ip = '*'
    c.NotebookApp.open_browser = False
    c.NotebookApp.port = 8881
    c.NotebookApp.password = u'sha1:[your hashed password here]'
    c.NotebookApp.certfile = u'/home/jupyter/.ssl/mycert.pem'
    c.NotebookApp.keyfile = u'/home/jupyter/.ssl/mykey.key'
    
    # create some directory for notebook files ie. ~/Public/jupyter
    mkdir -p ~/Public/jupyter && cd ~/Public/jupyter
     
    # start notebook server
    jupyter notebook
    
  3. Add kernels
  4. You can add multiple kernels to Jupyter. Here I’ll cover installation of some:

    • Python
    • sudo pip install ipykernel
      
      # if you wish to use matplotlib, make sure to add to 
      # ~/.ipython/profile_default/ipython_kernel_config.py
      c.InteractiveShellApp.matplotlib = 'inline'
      
    • BASH kernel
    • sudo pip install bash_kernel
      sudo python -m bash_kernel.install
      
    • Perl
    • This didn’t worked for me:/

      sudo cpan Devel::IPerl
    • IRkernel
    • Follow this tutorial.

    • Haskell
    • sudo apt-get install cabal-install
      git clone http://www.github.com/gibiansky/IHaskell
      cd IHaskell
      ./ubuntu-install.sh
      

Then, just navigate to https://YOURDOMAIN.COM:8881/, accept self-signed certificate and enjoy!
Alternatively, you can obtain certificate from Let’s encrypt.

Using existing domain encryption aka Apache proxy
If your domain is already HTTPS, you may consider setting up Jupyter on localhost and redirect all incoming traffic (already encrypted) to particular port on localhost (as suggested by @shebang).

# enable Apache mods
sudo a2enmod proxy proxy_http proxy_wstunnel && sudo service apache2 restart

# add to your Apache config
    <Location "/jupyter" >
        ProxyPass http://localhost:8881/jupyter
        ProxyPassReverse http://localhost:8881/jupyter
    </Location>
    <Location "/jupyter/api/kernels/" >
        ProxyPass        ws://localhost:8881/jupyter/api/kernels/
        ProxyPassReverse ws://localhost:8881/jupyter/api/kernels/
    </Location>
    <Location "/jupyter/api/kernels/">
        ProxyPass        ws://localhost:8881/jupyter/api/kernels/
        ProxyPassReverse ws://localhost:8881/jupyter/api/kernels/
    </Location>

# update you Jupyter config (~/.jupyter/jupyter_notebook_config.py)
c.NotebookApp.ip = 'localhost'
c.NotebookApp.open_browser = False
c.NotebookApp.port = 8881
c.NotebookApp.base_url = '/jupyter'
c.NotebookApp.password = u'sha1:[your hashed password here]'
c.NotebookApp.allow_origin = '*'

Note, it’s crucial to add Apache proxy for kernels (/jupyter/api/kernels/), otherwise you won’t be able to use terminals due to failed: Error during WebSocket handshake: Unexpected response code: 400 error.

Selecting stable genes from RNA-Seq experiment

A friend of mine was interested in selecting stable genes from RNA-Seq experiment, stable meaning genes with expression not affected by changing conditions.
I think the easiest solution is using partitioning function implemented in cummeRbund (K-means clustering):

ic<-csCluster(myGenes,k=4)
head(ic$cluster)
icp<-csClusterPlot(ic)
icp

Of course, it’s good to play with `k` value, so you indeed get some cluster with genes that are of interest.
Then, once you have some candidates, you may want to look for genes having similar expression patterns.

mySimilar<-findSimilar(cuff,"my_gene_name",n=20)
mySimilar.expression<-expressionPlot(mySimilar,logMode=T,showErrorbars=F)
mySimilar.expression