Skip to content

ikinsella/trefide

master
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
doc
 
 
 
 
 
 
 
 
 
 
src
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

TreFiDe - Trend Filter Denoising

TreFiDe is the software package accompanying the research publication "Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data".

TreFiDe is an imporved appproach to compressing and denoising functional image data. The method is based on a spatially-localized penalized matrix decomposition (PMD) of the data to separate (low-dimensional) signal from (temporally-uncorrelated) noise. This approach can be applied in parallel on local spatial patches and is therefore highly scalable, does not impose non-negativity constraints or require stringent identifiability assumptions (leading to significantly more robust results compared to NMF), and estimates all parameters directly from the data, so no hand-tuning is required. We have applied the method to a wide range of functional imaging data (including one-photon, two-photon, three-photon, widefield, somatic, axonal, dendritic, calcium, and voltage imaging datasets): in all cases, we observe ~2-4x increases in SNR and compression rates of 20-300x with minimal visible loss of signal, with no adjustment of hyperparameters; this in turn facilitates the process of demixing the observed activity into contributions from individual neurons. We focus on two challenging applications: dendritic calcium imaging data and voltage imaging data in the context of optogenetic stimulation. In both cases, we show that our new approach leads to faster and much more robust extraction of activity from the video data.

Getting Started

Docker

  1. docker run -it -p 34000:34000 paninski/trefide:1.2

  2. localhost:34000 (in a browser of your choise)

Build from source

Prerequisites

  • Anaconda or Miniconda

  • Linux (this package was developed & tested on Ubuntu 18.04, Ubuntu 20.04, and Manjaro)

Note: these instructions will assume that you clone the repo into your home directory

  1. Clone the repository
git clone git@github.com:ikinsella/trefide.git
  1. Navigate into the trefide repo you just cloned:
cd ~/trefide
  1. Create the conda environment using the provided config:
conda env create -f environments/devel.yml
  1. Activate the conda environment:
conda activate trefide_devel
  1. Compile the underlying source code (written in C++) by running
make all -j $(nproc)
  1. Compile the Cython extensions and install the trefide library:
pip install .

Try it out!

  1. Execute PMD demo code using a sample dataset:
cd ~/trefide
jupyter notebook demos/Matrix_Decomposition/Demo_PMD_Compression_Denoising.ipynb --no-browser --port=34000

The aforementioned notebook automatically downloads the sample dataset on your behalf. If you wish to manually download the sample dataset, it is available here.

Rebuilding & Modification

If you modify or pull updates this package will need to be rebuilt for the changes to take effect. This can be done as follows:

make clean && make all -j $(nproc) && pip uninstall trefide -y && pip install .

Uninstalling

If you wish to remove the entire project from your machine, you can run:

conda deactivate trefide_devel
conda remove --name trefide_devel --all
rm -rf ~/trefide

References

@article {Buchanan334706,
    author = {Buchanan, E. Kelly and Kinsella, Ian and Zhou, Ding and Zhu, Rong and Zhou, Pengcheng and Gerhard, Felipe and Ferrante, John and Ma, Ying and Kim, Sharon and Shaik, Mohammed and Liang, Yajie and Lu, Rongwen and Reimer, Jacob and Fahey, Paul and Muhammad, Taliah and Dempsey, Graham and Hillman, Elizabeth and Ji, Na and Tolias, Andreas and Paninski, Liam},
    title = {Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data},
    elocation-id = {334706},
    year = {2019},
    doi = {10.1101/334706},
    publisher = {Cold Spring Harbor Laboratory},
    URL = {https://www.biorxiv.org/content/early/2019/01/21/334706},
    eprint = {https://www.biorxiv.org/content/early/2019/01/21/334706.full.pdf},
    journal = {bioRxiv}
}

Troubleshooting