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Superpixelization demixing method

This repo is an early version of https://github.com/paninski-lab/funimag. Check funimag repo for latest code.

Description

The whole pipeline is as follows:

  1. Threshold data to increase SNR.
  2. Correlation analysis to find out superpixels.
  3. Rank-1 svd to extract the temporal trace of superpixels.
  4. Successive Projection Algorithm to find pure superpixel.
  5. NMF with temporal trace and spatial support of pure superpixels as initialization to extract neurons and their activities.

Example

See Demo_superpixel_pipeline.ipynb on example data. This example data is from https://github.com/simonsfoundation/CaImAn.

See more demos on different datasets in other .ipynb files.

User guide

  1. Run in python3.6.
  2. Recommend running PMD then using the default parameters in demix function first. Note the parameters in demo have been tuned to adapt to the example data.
  3. Tuning parameters: i) See detailed description of each parameters in the comments of demix function. ii) cut_off_point and length_cut are two key parameters which may need to tune in superpixel initialization. corr_th_fix is another parameter which may need to tune in following NMF step. Other parameters can generally take the default values.

Reference

https://www.biorxiv.org/content/early/2018/06/03/334706.

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Demixing for functional imaging data

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