multiqc_jupyterlab

JupyterLab extension for MultiQC

Installation guide

conda create -n multiqc_jupyterlab_env -c conda-forge nodejs yarn python jupyterlab
conda activate multiqc_jupyterlab_env
git clone https://github.com/olkuka/multiqc_jupyterlab.git
cd multiqc_jupyterlab/multiqc_jupyterlab/MultiQC/
pip install .
cd ..
cd .. 
pip install .

Usage

The extension allows you to display MultiQC in one JupyterLab notebook cell for a given module and list of samples.

Initialization

To initialize MultiQC widget:

from multiqc_jupyterlab import MultiQC

m = MultiQC()

Data

You can load data directly from analysis directory as in MultiQC or add data that is prepared by MultiQC and stored in multiqc_data/multiqc_data.json file.

m.load('./data') # load raw data from analysis directory

m.add('./multiqc_data') # add data prepared by MultiQC 

Show available modules or samples

m.get_modules() # to see available modules

m.get_samples(module) # too see available samples for a given module 

Show module

To see a module for a given subset of samples (not only from one analysis):

m.show(module, list_of_samples)

Development Installation

Create a dev environment:

conda create -n multiqc_jupyterlab-dev -c conda-forge nodejs yarn python jupyterlab
conda activate multiqc_jupyterlab-dev

Install the python. This will also build the TS package.

pip install -e ".[test, examples]"

When developing your extensions, you need to manually enable your extensions with the notebook / lab frontend. For lab, this is done by the command:

jupyter labextension develop --overwrite .
yarn run build

For classic notebook, you need to run:

jupyter nbextension install --sys-prefix --symlink --overwrite --py multiqc_jupyterlab
jupyter nbextension enable --sys-prefix --py multiqc_jupyterlab

Note that the --symlink flag doesn't work on Windows, so you will here have to run the install command every time that you rebuild your extension. For certain installations you might also need another flag instead of --sys-prefix, but we won't cover the meaning of those flags here.

How to see your changes

Typescript:

If you use JupyterLab to develop then you can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the widget.

# Watch the source directory in one terminal, automatically rebuilding when needed
yarn run watch
# Run JupyterLab in another terminal
jupyter lab

After a change wait for the build to finish and then refresh your browser and the changes should take effect.

Python:

If you make a change to the python code then you will need to restart the notebook kernel to have it take effect.