tuncat 1.1.1

Description:

tuncat 1.1.1

TUnCaT
Temporal Unmixing of Calcium Traces (TUnCaT) is an automatic algorithm to decontaminate false transients from temporal traces generated from fluorescent calcium imaging videos. TUnCaT removed false transients caused by large-scale background fluctuation using background subtraction; TUnCaT removed false transients caused by neighboring neurons, axons, and dendrites using nonnegative matrix factorization (NMF).
Copyright (C) 2021 Duke University NeuroToolbox
This repo contains the code of TUnCaT. If you want to reproduce the results in our paper, please visit the paper reproduction repo or figshare to find the data, results, and code to analyze the results.

TUnCaT
Installation on Windows or Linux
Demo
Input, Output, and Intermediate Files

Input files
Intermediate and Output files


Use your own data

Use your own videos
Use your own neuron masks
Set the unmixing parameters

Set alpha as a user-defined value or using cross-validation




Citation
Licensing and Copyright
Sponsors

Installation on Windows or Linux

First, install Python from the official website or any other distribution like Anaconda.
Launch Windows/Linux terminal or Anaconda prompt, and install TUnCaT using pip:

python -m pip install tuncat


Please be aware that the speed of TUnCaT in the Anaconda installation may be significantly slower than the speed in the offical python installation in Windows.

Demo
We provided a demo for all users to get familiar with TUnCaT. To run the demo, please download the code from our GitHub repository. We provided a one-photon imaging video c28_163_244.h5 as well as its manually labeled neurons FinalMasks_c28_163_244.mat. The demo will calculate the raw traces and background traces of all neurons, calculate the unmixed traces using TUnCaT, and export them to the folder unmixed_traces. The input, output, and intermediate files will be explained in Input, Output, and Intermediate Files.
To run the demo python script, launch system terminal or Anaconda prompt, and type the following script
cd {TUnCaT_root_path}
python demo_TUnCaT.py

You can also run the demo demo_TUnCaT.ipynb interactively using Jupyter Notebook. This notebook contains the expected figure of the background-subtracted trace and the unmixed trace of the first neuron. The saved processing time is recorded in a laptop with an AMD Ryzen 5 3500U quad-core CPU.
Input, Output, and Intermediate Files
By default, all the input, output, and intermediate files are saved under the TUnCaT folder.
Input files

A .h5 file {name}.h5 contains the input video. This is a 3D dataset with shape = (T, Lx, Ly), where T is the number of frames, and (Lx, Ly) is the lateral dimension of each frame. The demo video has (T, Lx, Ly) = (6000, 50, 50).
A .mat file FinalMasks_{name}.mat contains the nueron masks of the video. This is a 3D array with shape = (Ly, Lx, n) in MATLAB, where Ly and Lx should match the lateral dimensions of the video, and n is the number of neurons. The demo mask has (Ly, Lx, n) = (50, 50, 45).
Important notice: The default dimension order for multi-dimensional array is reversed in MATLAB and python. When you save a dataset with shape = (L1, L2, L3) in MATLAB to an .h5 file or a .mat file with version 7.3 or newer (requiring h5py.File to load in python workspace), and then load it in python, the shape will become (L3, L2, L1). However, if you save the dataset as a .mat file with version 7 or earlier (requiring scipy.loadmat to load in python workspace), the dimensions will preserve and still be (L1, L2, L3). In this document, we will use the python default order to describe the datasets in python workspace or saved in .h5, and use the MATLAB default order to describe the datasets saved in .mat. Sometimes you need to transpose the dimensions to make them consistent. In python, you can transpose the dimensions using Masks = Masks.transpose((2,1,0)). In MATLAB, you can transpose the dimensions using Masks = permute(Masks,[3,2,1]).

Intermediate and Output files
After running TUnCaT on the demo video, the intermediate and output files will be under a new folder unmixed_traces.

Intermediate file: unmixed_traces/raw/{name}.mat stores the raw neuron traces (traces) and the background traces (bgtraces). Each trace variable is 2D matrix with shape = (T, n), where T is the number of frames, and n is the number of neurons.
Output file: unmixed_traces/alpha={}/{name}.mat or unmixed_traces/list_alpha={}/{name}.mat stores the unmixed traces of the neurons (traces_nmfdemix), as well as other quantities characterizing the NMF unmixing process of each neuron, including the mixing matrix (list_mixout), final alpha (list_alpha_final), reconstruction residual (list_MSE), and number of iterations (list_n_iter). See the function descriptions for the detailed meanings of these quantities.

Use your own data
Of course, you can modify the demo scripts to process other videos. You need to set the folders of the videos and neuron masks, and change some parameters in the python scripts to correspond to your videos.
Use your own videos

Set the folder and file names of your video correctly. You need to change the variables dir_video and list_Exp_ID. The variable dir_video is the folder containing the input videos. For example, if your videos are stored in C:/Users/{username}/Documents/GitHub/TUnCaT_paper_reproduction/TUnCaT/data, set dir_video = 'C:/Users/{username}/Documents/GitHub/TUnCaT_paper_reproduction/TUnCaT/data'. You can also use relative path, such as dir_video = './data'. The variable list_Exp_ID is the list of the file names (without extension) of the input videos (e.g., list_Exp_ID = ['c28_163_244'] in the demo referes to the input file {TUnCaT_root_path}/data/c28_163_244.h5).
Currently, we only support .h5 files as the input video, so you need to convert the format of your data to .h5. You can save the video in a dataset with any name, but don't save the video under any group. The video should have a shape of (T, Lx, Ly), where T is the number of frames, and (Lx, Ly) is the lateral dimension of each frame. The support to more video formats will come soon. When doing format conversion, make sure the dimension is in the correct order. For example, if you save save the .h5 files from MATLAB, the shape of the dataset should be (Ly, Lx, T) in MATLAB. See Input files for more explanation of the dimension order.

Use your own neuron masks

Set the folder and file names of your mask files correctly. You need to change variable dir_mask to the folder containing the mask files.
Currently, we only support .mat files as the neuron masks, so you need to convert the format of your neuron masks to .mat, and save the neuron masks in dataset 'FinalMasks'. The name of the masks file should be FinalMasks_{Exp_ID}.mat, where the {Exp_ID} is the name of the corresponding video. The neuron masks should be saved as a 3D array named FinalMasks, and the dimension should be (Ly, Lx, n) in MATLAB, where Ly and Lx are the same as the lateral dimension of the video, and n is the number of neurons.
The masks created by some manual labeling software may contain an empty (all zero) image as the first frame. You need to remove the empty frame before saving them.

Set the unmixing parameters

The list of video names (e.g., list_Exp_ID = ['c28_163_244'])
The folder of the video (e.g., dir_video='./data');
The folder of the neuron masks (e.g., dir_masks='./data');
The folder of the output traces (e.g., dir_traces='./data/unmixed_traces');
list_alpha is the list of tested alpha;
multi_alpha determines the option to deal with multiple elements in list_alpha. False means the largest element providing non-trivial output traces will be used, which can be differnt for different neurons. True means each element will be tested and saved independently. These options are equivalent when there is only one element in list_alpha;
Qclip is clipping quantile. Traces lower than this quantile are clipped to this quantile value;
th_pertmin is maximum pertentage of unmixed traces equaling to the trace minimum;
epsilon is the minimum value of the input traces after scaling and shifting;
th_residual is the maximum factorization residual if this value is not zero;
nbin is the temporal downsampling ratio;
bin_option determines the temporal downsampling option. It can be 'downsample', 'sum', or 'mean'. It is not used when nbin == 1;
flexible_alpha determines whether a flexible alpha strategy is used when the smallest alpha in "list_alpha" already caused over-regularization.

Set alpha as a user-defined value or using cross-validation
Among the above parameters, we think most parameters do not need to be changed, but an optimized list_alpha can improve the unmixing accuracy. In our paper, we optimized the alpha using cross-validation, but it requires manual labeling of many traces, which is time consuming. Users can start with list_alpha = [1], because we showed that most of our optimized alpha are close to 1, and using a user-defined initial alpha=1 can give nearly the same accuracy for most videos without extreme conditions.
However, if the experimental conditions are very different from our test datasets, using cross-validation to optimize alpha will potentially be more reliable. Because cross-validation requires multiple videos, we don't provide demo for cross-validation, but users can run through our paper reproduction code in the paper reproduction repo to see how cross-validation can be done. Here, we will briefly introduce how to perform cross-validation, and use our ABO dataset as an example. The following folders will refer to the folder in the paper reproduction repo.
(1) Find a dataset containing multiple videos with similar experimental conditions. The three major datasets in our paper, ABO, NAOMi, 1p, are all qualified. (2) Manually label ground truth transients using the MATLAB GUI in the folder TemporalLabelingGUI (We already provided the ground truth transients for the paper reproduction datasets). (3) Run TUnCaT with multiple alpha in list_alpha using TUnCaT_multi_ABO.py. For a new dataset, you can also start with demo_TUnCaT.py, and set list_alpha = [0.1, 0.2, 0.3, 0.5, 1, 2, 3, 5, 10]. Make sure multi_alpha = True. (4) Calculate the F1 scores of all videos with different alpha using evaluation/eval_ABO_ours.m. (5) Find the optimal alpha for each cross-validation round using evaluation/cross_validation.m. You can load the file storing the calculated F1 scores, or run cross_validation.m immediately after eval_ABO_ours.m to avoid reloading the F1 scores.
Citation
If you use any part of this software in your work, please cite our paper:
Bao, Y., E. Redington, A. Agarwal, and Y. Gong, Decontaminate traces from fluorescence calcium imaging videos using targeted nonnegative matrix factorization. Frontiers in Neuroscience (2021 (in press)). doi: 10.3389/fnins.2021.797421.
Licensing and Copyright
TUnCaT is released under the GNU License, Version 2.0.
Sponsors