oasis-deconv 0.3.1

Fast algorithm for deconvolution of neural calcium imaging traces

OASIS: Fast online deconvolution of calcium imaging data

Tools for extracting the neural activity from fluorescence calcium imaging data  

The code can be readily run on neural temporal fluorescence calcium imaging data. Please have a look at the demo.

Requirements

The package is tested on Linux, macOS, and Windows with Python 3.8–3.14. Dependencies (numpy, scipy, matplotlib) are handled automatically by pip.

Optionally, to perform comparisons with interior point methods:

• cvxpy (https://www.cvxpy.org)
• gurobi (www.gurobi.com, free academic license)
• mosek (https://mosek.com, free academic license)

Installation

Package based

The easiest way to install OASIS is using either pip:

pip install oasis-deconv

or if you are using conda (or mamba):

conda install -c conda-forge oasis-deconv

However, you won't have the examples provided in the GitHub repo.

Compile from source

Alternatively you can clone the repo

git clone [e-mail]:j-friedrich/OASIS.git
cd OASIS

and install using pip, which will automatically compile the Cython extensions:

pip install .

For development, use an editable install (Python files take effect immediately; Cython files still require a reinstall when changed):

pip install -e .

Examples

The scripts to produce the figures and table are in the subfolder 'examples' with names obvious from the PLoS Comput Biol paper. They can be run with python examples/fig[1-6].py.

The results of fig4 and table1 will be even better than in the paper, because the version in the master branch includes later improvements, in particular up to an order of magnitude less computing time. The specific points in history marking the time of the publications have been tagged.

To demonstrate how to use the methods on your own data, we included a demo jupyter notebook in the subfolder 'examples' as well.

Other implementations

• Matlab

Related packages

In order to deal not just with temporal, but with raw spatio-temporal fluorescence data, we added OASIS also to CaImAn, the computational toolbox for large scale Calcium Imaging Analysis.

References

The code accompanies a short NIPS paper and an extended journal version with full details. If you use our code in your research, please cite one of them:

• Friedrich J, Paninski L. Fast Active Set Method for Online Spike Inference from Calcium Imaging. In: Adv Neural Inf Process Syst. 2016; p. 1984–1992.
• Friedrich J, Zhou P, Paninski L. Fast Online Deconvolution of Calcium Imaging Data. PLoS Comput Biol. 2017; 13(3):e1005423.