Reproducible machine learning analysis of gene expression and alternative splicing data
Jupyter Notebook Python
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flotilla Logo

What is flotilla?

flotilla is a Python package for visualizing transcriptome (RNA expression) data from hundreds of samples. We include utilities to perform common tasks on these large data matrices, including:

  • Dimensionality reduction
  • Classification and Regression
  • Outlier detection
  • Network graphs from covariance
  • Hierarchical clustering

And common tasks for biological data including:

  • Renaming database features to gene symbols
  • Coloring/marking samples based on experimental phenotype
  • Removing poor-quality samples (technical outliers)

Finally, flotilla is a platform for active collaboration between bioinformatics scientists and traditional "wet lab" scientists. Leveraging interactive widgets in the iPython Notebook, we have created tools for simple and streamlined data exploration including:

  • Subsetting sample groups and feature (genes/splicing events) groups
  • Dynamically adjusting parameters for analysis
  • Integrating external lists of features from the web or local files

These empower the "wet lab" scientists to ask questions on their own and gives bioniformatics scientists a platform and share their analysis tools.

What flotilla is not

flotilla is not a genomics pipeline. We expect that you have already generated data tables for gene expression, isoform expression and metadata. flotilla only makes it easy to integrate all those data parts together once you have the pieces.

Learn how to use flotilla

Please refer to our talks to learn more about how you can apply our tools to your data.


Docker Installation Instructions

Docker is the preferred method to obtain the most up-to-date version of flotilla. Every change we make to the source code triggers a new build of a virtual machine that contains flotilla and all its dependencies.

Please follow instructions here to get, install, and run the flotilla image.

Local install (on your computer)

To install, first install the Anaconda Python Distribution, which comes pre-packaged with a bunch of the scientific packages we use all the time, pre-installed.

Create a Flotilla sandbox

We recommend creating a "sandbox" where you can install any and all packages without disturbing the rest of the Python distribution. You can do this with

conda create --yes --name flotilla_env --file conda_requirements.txt

You've now just created a "virtual environment" called flotilla_env (the first argument). Now activate that environment with,

source activate flotilla_env

Now at the beginning of your terminal prompt, you should see:


Which indicates that you are now in the flotilla_env virtual environment. Now that you're in the environment, follow along with the non-sandbox installation instructions.

Install and update all packages in your environment

To make sure you have the latest packages, on the command line in your terminal, enter this command:

conda install --yes --file conda_requirements.txt

Not all packages are available using conda, so we'll install the rest using pip, which is a Python package installer and installs from PyPI, the Python Package Index.

pip install -r requirements.txt

Next, to install the latest release of flotilla, do

pip install flotilla

If you want the bleeding-edge master version (that we work really hard to make sure it's always working but could be buggy!), then install the git master with,

pip install git+git://

Test dataset

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
study = flotilla.embark(flotilla._shalek2013)





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.

Problems? Questions?

We invite your input! Please leave any feedback on our issues page.

NumFOCUS logo

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