Deconvolution and demixing of calcium imaging data
The code implements a method for simultaneous source extraction and spike inference from large scale calcium imaging movies. The code is suitable for the analysis of somatic imaging data. Implementation for the analysis of dendritic/axonal imaging data will be added in the future.
The algorithm is presented in more detail in
Pnevmatikakis, E.A., Soudry, D., Gao, Y., Machado, T., Merel, J., ... & Paninski, L. (2016). Simultaneous denoising, deconvolution, and demixing of calcium imaging data. Neuron 89(2):285-299, http://dx.doi.org/10.1016/j.neuron.2015.11.037
Pnevmatikakis, E.A., Gao, Y., Soudry, D., Pfau, D., Lacefield, C., ... & Paninski, L. (2014). A structured matrix factorization framework for large scale calcium imaging data analysis. arXiv preprint arXiv:1409.2903. http://arxiv.org/abs/1409.2903
Code description and related packages
This repository contains a MATLAB implementation of the spatio-temporal demixing, i.e., (source extraction) code for large scale calcium imaging data. Related code can be found in the following links:
Matlab
- Constrained deconvolution and source extraction with CNMF (this package)
- MCMC spike inference
- Group LASSO initialization and spatial CNMF
Python
- Constrained deconvolution for neural activity (spike) extraction
- Source extraction with CNMF
- Group LASSO initialization and spatial CNMF
Integration with other libraries
- SIMA: The constrained deconvolution method has been integrated with SIMA, a Python based library for calcium imaging data analysis.
- Thunder: The group LASSO initialization and spatial CNMF method has been integrated with Thunder, a library for large scale neural data analysis with Spark.
Usage and Documentation
Check the demo scripts and documentation.pdf to get started.
Dependencies
The following matlab toolboxes are needed for the default parameter settings:
- Statistics and Machine Learning Toolbox
- Image processing toolbox
Depending on the settings the following toolboxes may also be required
- Signal processing toolbox (recommended but not required)
- Parallel computing toolbox (recommended for large datasets but not required)
- Optimization toolbox (not required)
The default options for the algorithm require the following packages:
- The CVX library which can be downloaded from http://cvxr.com/cvx/download/ (after unpacking CVX open Matlab and run cvx_setup from inside the CVX directory to properly install and add CVX to the Matlab path)
Depending on the settings the following packages may also be required
- SPGL1 package from https://github.com/mpf/spgl1 (for solving constrained_foopsi using SPGL1)
- Bayesian spike inference package from https://github.com/epnev/continuous_time_ca_sampler (for using the 'MCMC" deconvolution method).
Wiki
Some issues are covered in the wiki. More pages will be added and suggestions are welcome.
Questions, comments, issues
Please use the gitter chat room (use the button above) for questions and comments and create an issue for any bugs you might encounter.
License
This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.
//: # (demoMovie.tif | Sample dataset for running the code (by W. Yang and D. Peterka)
)
//: # (update_spatial_components.m | update spatial components given temporal components and data
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//: # (update_temporal_components.m | update temporal components given spatial components and data
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//: # (merge_ROIs.m | merge spatially overlapping components that are temporally correlated
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//: # (utilities/arpfit.m | estimation of noise level for every pixel and global time constants
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//: # (utilities/bigread2.m | read (parts of) large tiff stacks)
//: # (utilities/com.m: | calculation of the center of mass of each component
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//: # (utilities/correlation_image.m | calculates the correlation image of the movie
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//: # (utilities/extract_DF_F.m | transforming the temporal components in the DF/F domain
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//: # (utilities/graph_connected_comp.m | finds the connected components in a graph
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//: # (utilities/greedyROI2d.m | Greedy method for initializing the spatial and temporal components
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//: # (utilities/interp_missing_data.m | Filling in missing data using linear interpolation
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//: # (utilities/lars_regression_noise.m | solve a basis pursuit denoising problem using the LARS algorithm
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//: # (utilities/make_G_matrix.m | construct a convolution/deconvolution matrix
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//: # (utilities/make_patch_video.m | construct a video that displays the results of the algorithm
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//: # (utilities/order_ROIs.m | order found components based on their temporal activation and spatial size
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//: # (utilities/plain_foopsi.m | projection of fluorescence onto the cone formed by the indicator dynamics )
//: # (utilities/plot_contours.m | contour plot of found components and creation of a json file
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//: # (utilities/tiff_reader.m | loading a tiff stack into matlab
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//: # (utilities/threshold_components.m | mild post-processing of spatial components
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//: # (utilities/view_patches.m | plotting of each found component and its temporal activation
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