friedpine / flotilla

Reproducible gene expression and alternative splicing data analysis

flotilla

Proudly sponsored by a NumFOCUS John Hunter Technical Fellowship to Olga Botvinnik.

Installation instructions

From a clean install of Mavericks 10.9.4, follow these steps.

All others must fend for themselves to install matplotlib, scipy and their third-party dependencies.

This part only needs to be done once

• Install anaconda

• Install Xcode (this can take an hour)

• Open Xcode and agree to terms and services (it is very important to read them thoroughly)

• Install homebrew

  ruby -e "$(curl -fsSL https://raw.github.com/Homebrew/homebrew/go/install)"

• Install freetype:

  brew install freetype

• Install heavy packages (this can take an hour or more)

conda install pip numpy scipy cython matplotlib nose six scikit-learn ipython networkx pandas tornado statsmodels setuptools pytest pyzmq jinja2 pyyaml`

• Create a virtual environment

conda create -n flotilla_env pip numpy scipy cython matplotlib nose six scikit-learn ipython networkx pandas tornado statsmodels setuptools pytest pyzmq jinja2 pyyaml`

• Switch to virtual environment

  source activate flotilla_env

• Install flotilla and its dependencies (this can take a few minutes):

  pip install git+https://github.com/YeoLab/flotilla.git

• Create a scratch space for your work

  mkdir ~/flotilla_scratch

• Make a place to store flotilla projects

  mkdir ~/flotilla_projects

• Go back to the real world

  source deactivate

Start using flotilla:

Use the above instructions to create a flotilla-friendly environment, then:

• switch to virtual environment

  source activate flotilla_env

• start an ipython notebook:

  ipython notebook --notebook-dir=~/flotilla_scratch

• create a new notebook by clicking New Notebook

• rename your notebook from "Untitled" to something more informative by clicking the title panel.

• load matplotlib backend using every notebook must use this to display inline output

  %matplotlib inline

Test interactive features with example data:

We have prepared a slice of the full dataset for testing and demonstration purposes.

Run each of the following code lines in its own ipython notebook cell for an interactive feature.

import flotilla
test_study = flotilla.embark('http://sauron.ucsd.edu/flotilla_projects/neural_diff_chr22/datapackage.json')

test_study.interactive_pca()

test_study.interactive_graph()

test_study.interactive_classifier()

test_study.interactive_lavalamp_pooled_inconsistent()

IMPORTANT NOTE: for this test,several failures are expected since the test set is small. Adjust parameters to explore valid parameter spaces. For example, you can manually select all_genes as the feature_subset from the drop-down menu that appears after running these interactive functions.