Simple example on how you can automate reproducible analyses using jupyter notebooks
This repository demonstrates how jupyter notebooks can be used to run reproducible research; the example assumes that the end result of the analysis is a sharable web-page with free text, plots, and the code used to generate them. The code is hidden for easy digestion by non-computational people and to improve general readability.
Everything is controlled by a makefile, so there is absolutely no need to open an interactive jupyter session (except to create the notebook in the first place).
The analysis scenario assumes that the data necessary to the plots is being generated by simple scripts or other analysis pipelines; the notebook is used solely to plot the data.
The example analysis is very simple, but one could immagine a very complex scenario, with a data generation pipeline under the control of the makefile, and a series of notebooks that answer several questions with the necessary plots and small statistical tests. The output can be easily shared in the form of a structured analysis, together with a narrative structure.
Simply type:
make all
and then:
firefox notebooks/gene_sizes.html
If you like virtualenv, you can just type:
virtualenv venv
source venv/bin/activate
pip install -r requirements.txt
Copyright (C) <2016> EMBL-European Bioinformatics Institute
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