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https://github.com/mouseland/suite2p
cell detection in calcium imaging recordings
https://github.com/mouseland/suite2p
data-analysis imaging neuroscience
Last synced: 6 days ago
JSON representation
cell detection in calcium imaging recordings
- Host: GitHub
- URL: https://github.com/mouseland/suite2p
- Owner: MouseLand
- License: gpl-3.0
- Created: 2018-07-16T18:12:07.000Z (over 6 years ago)
- Default Branch: main
- Last Pushed: 2024-06-19T12:28:06.000Z (5 months ago)
- Last Synced: 2024-10-03T03:49:56.561Z (about 1 month ago)
- Topics: data-analysis, imaging, neuroscience
- Language: Jupyter Notebook
- Homepage: http://www.suite2p.org
- Size: 90.6 MB
- Stars: 348
- Watchers: 18
- Forks: 240
- Open Issues: 109
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesome-janelia-software - Suite2p - Pipeline for ROI detection and extraction in 2-photon recordings (End-User Applications)
- awesome-janelia-software - Suite2p - Pipeline for ROI detection and extraction in 2-photon recordings (End-User Applications)
README
# suite2p 
[](https://suite2p.readthedocs.io/en/latest/?badge=latest)
[](https://codecov.io/gh/MouseLand/suite2p)
[](https://badge.fury.io/py/suite2p)
[](https://pepy.tech/project/suite2p)
[](https://pepy.tech/project/suite2p)
[](https://pypistats.org/packages/suite2p)
[](https://github.com/MouseLand/suite2p/blob/main/LICENSE)
[](https://github.com/MouseLand/suite2p/graphs/contributors)
[](https://www.suite2p.org)
[](https://github.com/MouseLand/suite2p/)
[](https://github.com/MouseLand/suite2p/)
[](https://github.com/MouseLand/suite2p/)
Pipeline for processing two-photon calcium imaging data.
Copyright (C) 2018 Howard Hughes Medical Institute Janelia Research Campus
suite2p includes the following modules:
* Registration
* Cell detection
* Spike detection
* Visualization GUI
This code was written by Carsen Stringer and Marius Pachitariu.
For support, please open an [issue](https://github.com/MouseLand/suite2p/issues).
The reference paper is [here](https://www.biorxiv.org/content/early/2017/07/20/061507). The deconvolution algorithm is based on [this paper](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005423), with settings based on [this paper](http://www.jneurosci.org/content/early/2018/08/06/JNEUROSCI.3339-17.2018).
You can now run suite2p in google colab, no need to locally install (although we recommend doing so eventually): [](https://colab.research.google.com/github/MouseLand/suite2p/blob/main/jupyter/run_suite2p_colab_2023.ipynb). Note you do not have access to the GUI via google colab, but you can download the processed files and view them locally in the GUI.
See this **twitter [thread](https://twitter.com/marius10p/status/1032804776633880583)** for GUI demonstrations.
The matlab version is available [here](https://github.com/cortex-lab/Suite2P). Note that the algorithm is older and will not work as well on non-circular ROIs.
Lectures on how suite2p works are available [here](https://youtu.be/HpL5XNtC5wU?list=PLutb8FMs2QdNqL4h4NrNhSHgLGk4sXarb).
**Note on pull requests**: we accept very few pull requests due to the maintenance efforts required to support new code, and we do not accept pull requests from automated code checkers. If you wrote code that interfaces/changes suite2p behavior, a common approach would be to keep that in a fork and pull periodically from the main branch to make sure you have the latest updates.
### CITATION
If you use this package in your research, please cite the [paper](https://www.biorxiv.org/content/early/2017/07/20/061507):
Pachitariu, M., Stringer, C., Schröder, S., Dipoppa, M., Rossi, L. F., Carandini, M., & Harris, K. D. (2016). Suite2p: beyond 10,000 neurons with standard two-photon microscopy. BioRxiv, 061507.
## Read the Documentation at https://suite2p.readthedocs.io/
## Local installation
1. Install an [Anaconda](https://www.anaconda.com/download/) distribution of Python -- Choose **Python 3.8** and your operating system. Note you might need to use an anaconda prompt if you did not add anaconda to the path.
2. Open an anaconda prompt / command prompt with `conda` for **python 3** in the path
3. Create a new environment with `conda create --name suite2p python=3.9`.
4. To activate this new environment, run `conda activate suite2p`
5. (Option 1) You can install the minimal version of suite2p, run `python -m pip install suite2p`.
6. (Option 2) You can install the GUI version with `python -m pip install suite2p[gui]`. If you're on a zsh server, you may need to use `' '` around the suite2p[gui] call: `python -m pip install 'suite2p[gui]'`. This also installs the NWB dependencies.
7. Now run `python -m suite2p` and you're all set.
8. Running the command `suite2p --version` in the terminal will print the install version of suite2p.
For additional dependencies, like h5py, NWB, Scanbox, and server job support, use the command `python -m pip install suite2p[io]`.
If you have an older `suite2p` environment you can remove it with `conda env remove -n suite2p` before creating a new one.
Note you will always have to run **conda activate suite2p** before you run suite2p. Conda ensures mkl_fft and numba run correctly and quickly on your machine. If you want to run jupyter notebooks in this environment, then also `conda install jupyter`.
To **upgrade** the suite2p (package [here](https://pypi.org/project/suite2p/)), run the following in the environment:
~~~~
pip install --upgrade suite2p
~~~~
### Dependencies
This package relies on the awesomeness of [pyqtgraph](http://pyqtgraph.org/), [PyQt6](http://pyqt.sourceforge.net/Docs/PyQt6/), [torch](http://pytorch.org), [numpy](http://www.numpy.org/), [numba](http://numba.pydata.org/numba-doc/latest/user/5minguide.html), [scanimage-tiff-reader](https://vidriotech.gitlab.io/scanimagetiffreader-python/), [scipy](https://www.scipy.org/), [scikit-learn](http://scikit-learn.org/stable/), [tifffile](https://pypi.org/project/tifffile/), [natsort](https://natsort.readthedocs.io/en/master/), and our neural visualization tool [rastermap](https://github.com/MouseLand/rastermap). You can pip install or conda install all of these packages. If having issues with PyQt6, then try to install within it conda install pyqt. On Ubuntu you may need to `sudo apt-get install libegl1` to support PyQt6. Alternatively, you can use PyQt5 by running `pip uninstall PyQt6` and `pip install PyQt5`. If you already have a PyQt version installed, suite2p will not install a new one.
The software has been heavily tested on Windows 10 and Ubuntu 18.04, and less well tested on Mac OS. Please post an [issue](https://github.com/MouseLand/suite2p/issues) if you have installation problems.
### Installing the latest github version of the code
The simplest way is
~~~
pip install git+https://github.com/MouseLand/suite2p.git
~~~
If you want to download and edit the code, and use that version,
1. Clone the repository with git and `cd suite2p`
2. Run `pip install -e .` in that folder
### Installation for developers
1. Clone the repository and `cd suite2p` in an anaconda prompt / command prompt with `conda` for **python 3** in the path
2. Run `conda create --name suite2p python=3.9`
3. To activate this new environment, run `conda activate suite2p` (you will have to activate every time you want to run suite2p)
4. Install the local version of suite2p into this environment in develop mode with the command `pip install -e .[all]`
5. Run tests: `python setup.py test` or `pytest -vs`, this will automatically download the test data into your `suite2p` folder. The test data is split into two parts: test inputs and expected test outputs which will be downloaded in `data/test_inputs` and `data/test_outputs` respectively. The .zip files for these two parts can be downloaded from these links: [test_inputs](https://www.suite2p.org/static/test_data/test_inputs.zip) and [test_outputs](https://www.suite2p.org/static/test_data/test_outputs.zip).
## Examples
An example dataset is provided [here](https://drive.google.com/drive/folders/0B649boZqpYG1amlyX015SG12VU0?resourcekey=0-v-pxg8FwtFV7lqynlsuc9Q&usp=sharing). It's a single-plane, single-channel recording.
## Getting started
The quickest way to start is to open the GUI from a command line terminal. You might need to open an anaconda prompt if you did not add anaconda to the path. Make sure to run this from a directory in which you have **WRITE** access (suite2p saves a couple temporary files in your current directory):
~~~~
suite2p
~~~~
Then:
1. File -> Run suite2p (or ctrl+r)
2. Setup a configuration
- -> Add directory which contains tiffs to data_path (can be multiple folders, but add them one at a time)
- -> OR choose an h5 file which has a key with the data, data shape should be time x pixels x pixels (you can type in the key name for the data after you choose the file)
- -> Add save_path ((otherwise the data directory is used as save path))
- -> Add fast_disk (this is where the binary file of registered data will be created, choose an SSD for this path) ((otherwise the save path is used as the fast disk path))
- Set some parameters (see full list below). At the minimum:
~~~~
nplanes, nchannels, tau, fs
~~~~
3. Press run and wait. Messages should start appearing in the embedded command line.
4. When the run is finished, the results will open in the GUI window and there you can visualize and refine the results (see below).
### Using the GUI
The suite2p output goes to a folder called "suite2p" inside your save_path, which by default is the same as the data_path. If you ran suite2p in the GUI, it loads the results automatically. Otherwise, you can load the results with File -> Load results or by dragging and dropping the stat.npy file into the GUI.
The GUI serves two main functions:
1. Checking the quality of the data and results.
* there are currently several views such as the enhanced mean image, the ROI masks, the correlation map, the correlation among cells, and the ROI+neuropil traces
* by selecting multiple cells (with "Draw selection" or ctrl+left-click), you can view the activity of multiple ROIs simultaneously in the lower plot
* there are also population-level visualizations, such as [rastermap](https://github.com/MouseLand/rastermap)
2. Classify ROIs into cell / not cell (left and right views respectively)
* the default classifier included should work well in a variety of scenarios.
* a user-classifier can be learnt from manual curation, thus adapting to the statistics of your own data.
* the GUI automatically saves which ROIs are good in "iscell.npy". The second column contains the probability that the ROI is a cell based on the currently loaded classifier.
Main GUI controls (works in all views):
1. Pan = Left-Click + drag
2. Zoom = (Scroll wheel) OR (Right-Click + drag)
3. Full view = Double left-click OR escape key
4. Swap cell = Right-click on the cell
5. Select multiple cells = (Ctrl + left-click) OR (SHIFT + left-click) AND/OR ("Draw selection" button)
You can add your manual curation to a pre-built classifier by clicking "Add current data to classifier". Or you can make a brand-new classifier from a list of "iscell.npy" files that you've manually curated. The default classifier in the GUI is initialized as the suite2p classifier, but you can overwrite it by adding to it, or loading a different classifier and saving it as the default. The default classifier is used in the pipeline to produce the initial "iscell.npy" file.
## Other ways to call Suite2p
1. From the command line:
~~~~
suite2p --ops --db
~~~~
2. From Python/Jupyter
~~~~python
from suite2p.run_s2p import run_s2p
ops1 = run_s2p(ops, db)
~~~~
See our example jupyter notebook [here](https://github.com/MouseLand/suite2p/blob/main/jupyter/run_suite2p_colab_2023.ipynb).
## Outputs
~~~~
F.npy: array of fluorescence traces (ROIs by timepoints)
Fneu.npy: array of neuropil fluorescence traces (ROIs by timepoints)
spks.npy: array of deconvolved traces (ROIs by timepoints)
stat.npy: array of statistics computed for each cell (ROIs by 1)
ops.npy: options and intermediate outputs
iscell.npy: specifies whether an ROI is a cell, first column is 0/1, and second column is probability that the ROI is a cell based on the default classifier
~~~~
# License
Copyright (C) 2023 Howard Hughes Medical Institute Janelia Research Campus, the labs of Carsen Stringer and Marius Pachitariu.
**This code is licensed under GPL v3 (no redistribution without credit, and no redistribution in private repos, see the [license](LICENSE) for more details).**
### Logo
Logo was designed by Shelby Stringer and [Chris Czaja](http://chrisczaja.com/).