The FastLZeroSpikeInference package is publicly available on Github. The C++ implementation of Algorithms 1 and 2 of (Jewell, Hocking, Fearnhead, & Witten, 2019) are accessible through R and python wrappers. These wrappers are available at https://github.com/jewellsean/FastLZeroSpikeInference.

If you are using the Allen Institute for Brain Science (AIBS) Brain Observatory data, note that the AIBS recently released an update to their software development kit that provides users with the output from Algorithm 2 for close to 60,000 neurons during different experimental conditions. See https://allensdk.readthedocs.io/en/latest/ for additional information.

Installation

R

In R, make sure that the devtools package is installed (install.packages("devtools")) and then run

devtools::install_github("jewellsean/FastLZeroSpikeInference").

This command installs the latest version of the package from Github. The package has been submitted to CRAN, though may not yet be available.

Python

In python, we recommend directly cloning the repository and building using the supplied make script. Run the following commands

git clone "https://github.com/jewellsean/FastLZeroSpikeInference.git"
cd FastLZeroSpikeInference/python
./make.sh

to clone the repository, build the package, and install to your local python directory.

References

1. Jewell, S. W., Hocking, T. D., Fearnhead, P., & Witten, D. M. (2019). Fast nonconvex deconvolution of calcium imaging data. Biostatistics. https://doi.org/10.1093/biostatistics/kxy083
   Bibtex

   @article{jewell2018fast,
     author = {Jewell, Sean W and Hocking, Toby Dylan and Fearnhead, Paul and Witten, Daniela M},
     title = {{Fast nonconvex deconvolution of calcium imaging data}},
     year = {2019},
     month = feb,
     journal = {Biostatistics},
     doi = {10.1093/biostatistics/kxy083},
     url = {https://doi.org/10.1093/biostatistics/kxy083},
     preprint = {https://arxiv.org/pdf/1802.07380.pdf},
     code = {https://github.com/jewellsean/FastLZeroSpikeInference}
   }