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.
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.
Please refer to our talks to learn more about how you can apply our tools to your data.
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.
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.
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:
(flotilla_env)
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.
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://github.com/yeolab/flotilla.git
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)
study.interactive_pca()
study.interactive_graph()
study.interactive_classifier()
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.
We invite your input! Please leave any feedback on our issues page.
Proudly sponsored by a NumFOCUS John Hunter Technical Fellowship to Olga Botvinnik.