BIOSELECT

Note: all the datasets are not provided in this repository since they are very heavy.

Master thesis presentation (Google Slides)

You can find the presentation I have done (in French) here

Master thesis report

You can find the report I have done (in French) in the report folder.

Setup the project using Docker (recommended)

Requirements

• You will need a GNU/Linux distribution with Docker support. Xubuntu 16.04 is recommended.
• You need to have Docker installed

./build-docker.sh

Then you can run Jupyter to work on the notebooks

./run-jupyter-docker.sh

To run the unit tests

./run-unit-tests.sh

Setup the project using Python virtualenv

Warning: You will not be able to run Limma if you use this method.

Information: You don't need to follow these instructions if you followed the Docker ones

Requirements

• A GNU/Linux operating system, it is assumed that you use an Ubuntu-like distribution
• Git
• Python 2 and pip

Get the project

git clone https://github.com/krypty/BIO-SELECT.git
cd BIO-SELECT

# If you don't have pip installed
wget https://bootstrap.pypa.io/get-pip.py
sudo python2 get-pip.py

Create and activate the virtual environment

sudo pip install virtualenv
virtualenv -p python2 bioselect
source bioselect/bin/activate

Install the dependancies

# Those packages need to be installed on the system because some librairies require them
sudo apt-get update && sudo apt-get install build-essential python-dev

# Install python dependencies
pip install -r requirements.txt

Run a Jupyter notebook

To see if everything has been installed correctly, start Jupyter and run a notebook:

jupyter-notebook

Now open your web browser at http://localhost:8888 and open a Juypter notebook like DatasetVisualisation.ipynb for instance.

Getting started

The project is composed of Jupyter notebooks and Python classes. The notebooks shows the graphs and the general workflow of the project and use the Python classes in the background.

The first notebook you might want to check out is the one called features_selection.ipynb. It contains the dataset loadings and executes the algorithms. To see what happens behind the scenes you should look at the import statements and what is going on in these classes.

An other thing you can do to getting started is to review the unit tests. It can help you to understand how a class work