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.

The reference paper is here. The deconvolution algorithm is based on this paper, with settings based on this paper.

You can now run suite2p in google colab, no need to locally install (although we recommend doing so eventually): . 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 for GUI demonstrations.

The matlab version is available here. Note that the algorithm is older and will not work as well on non-circular ROIs.

Lectures on how suite2p works are available here.

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:

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

If you are using a GPU, make sure its drivers and the cuda libraries are correctly installed.

1. Install an Anaconda 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), run the following in the environment: ~~~~ pip install --upgrade suite2p ~~~~

Dependencies

This package relies on the awesomeness of pyqtgraph, PyQt6, torch, numpy, numba, scanimage-tiff-reader, scipy, scikit-learn, tifffile, natsort, and our neural visualization tool 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 if you have installation problems.

GPU version (CUDA) on Windows or Linux

If you plan on running Cellpose-SAM (anatomical ROI detection), you may want to install a GPU version of torch. To use your NVIDIA GPU with python, you will need to make sure the NVIDIA driver for your GPU is installed, check out this website to download it. You can also install the CUDA toolkit, or use the pytorch cudatoolkit (installed below with conda). If you have trouble with the below install, we recommend installing the CUDA toolkit yourself, choosing one of the 11.x releases here.

With the latest versions of pytorch on Linux, as long as the NVIDIA drivers are installed, the GPU version is installed by default with pip. You can check if the GPU support is working by opening the GUI. If the GPU is working then the GPU box will be checked and the CUDA version will be displayed in the command line.

If it's not working, we will need to remove the CPU version of torch: ~~~ pip uninstall torch ~~~

To install the GPU version of torch, follow the instructions here. The pip or conda installs should work across platforms, you will need torch and torchvision, e.g. for windows + cuda 12.6 the command is ~~~ pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu126 ~~~

Info on how to install several older versions is available here. After install you can check conda list for pytorch, and its version info should have cuXX.X, not cpu.

Installing the latest github version of the code

If you are using a GPU, make sure its drivers and the cuda libraries are correctly installed.

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

If you are using a GPU, make sure its drivers and the cuda libraries are correctly installed.

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 and test_outputs.

Examples

An example dataset is provided here. 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 datapath (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 savepath ((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 savepath, which by default is the same as the datapath. 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
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.runs2p import runs2p ops1 = run_s2p(ops, db) ~~~~

See our example jupyter notebook here.

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 for more details).

Logo

Logo was designed by Shelby Stringer and Chris Czaja.