suite2p 0.3.4

Pipeline for calcium imaging

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.

Installation

From a command line terminal, type:

pip install suite2p

If this fails, you might not have Python 3 (or pip, or a recent enough version of pip). You'll need to install a distribution of Python like Anaconda. Choose Python 3.x for your operating system. You might need to use an anaconda prompt if you did not add anaconda to the path. Try "pip install suite2p" again. If it still fails, there might be some interaction between pre-installed dependencies and the ones Suite2p needs. First thing to try is

python -m pip install --upgrade pip

And try "pip install suite2p" again. If it still fails, install Anaconda, and use the Anaconda command prompt to have a clean environment. Alternatively, if you already have Anaconda, create a new conda environment just for suite2p with

conda create --name suite2p
(source) activate suite2p 
pip install suite2p

If you choose this path, you will need to "(source) activate suite2p" every time you use suite2p. Omit the "source" on Windows.

To upgrade a current install of suite2p to the latest version (package page here), run the following command:

pip install suite2p --upgrade

If you are on Yosemite Mac OS, PyQt doesn't work, and you won't be able to install suite2p. More recent versions of Mac OS work.

Dependencies

suite2p relies on the following packages (which are automatically installed with pip if missing):

• pyqtgraph
• PyQt5
• numpy (>=1.13.0)
• scipy
• h5py
• scikit-image
• scikit-learn
• matplotlib (not for plotting (only using hsv_to_rgb function), should not conflict with PyQt5)

The software has been heavily tested on Windows 10 and Ubuntu 18.04, and less well tested on Mac OS. Please let us know if you have problems with other operating systems in the issues.

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.

python -m suite2p

Then:

1. File -> Run suite2p (or shortcut ctrl+r)
2. Setup a configuration for your own data
   • -> 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, diameter, 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

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, load the results with File -> Load results.

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
   • we will later add more views such as population-level visualizations.
2. Classify ROIs into cell / not cell (left and right views respectively)
   • the default classifier included should work well in a wide variety of scenarios.
   • this classifier can learn from manual curation, and in this way adapt to the statistics of your own data.
   • the GUI automatically saves which cells are on the left and right in the first column of "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) 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:

python -m suite2p --ops <path to ops.npy> --db <path to db.npy>

2. From Python/Jupyter

from suite2p.run_s2p import run_s2p
ops1 = run_s2p(ops, db)

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

Option defaults

    ops = {
        'reg_tif': False, # whether to save registered tiffs
        'do_registration': True, # whether to register data
        'save_mat': False, # whether to save Matlab results
        'fast_disk': [], # used to store temporary binary file, defaults to save_path0
        'delete_bin': False, # whether to delete binary file after processing
        'h5py': [], # take h5py as input (deactivates data_path)
        'h5py_key': 'data', #key in h5py where data array is stored
        'save_path0': [], # stores results, defaults to first item in data_path
        'diameter':12, # this is the main parameter for cell detection, 2-dimensional if Y and X are different (e.g. [6 12])
        'tau':  1., # this is the main parameter for deconvolution
        'fs': 10.,  # sampling rate (total across planes)
        'nplanes' : 1, # each tiff has these many planes in sequence
        'nchannels' : 1, # each tiff has these many channels per plane
        'functional_chan' : 1, # this channel is used to extract functional ROIs (1-based)
        'align_by_chan' : 1, # when multi-channel, you can align by non-functional channel (1-based)
        'look_one_level_down': False, # whether to look in all subfolders when searching for tiffs
        'baseline': 'maximin', # baselining mode
        'win_baseline': 60., # window for maximin
        'sig_baseline': 10., # smoothing constant for gaussian filter
        'prctile_baseline': 8.,# optional (whether to use a percentile baseline)
        'neucoeff': .7,  # neuropil coefficient
        'neumax': 1.,  # maximum neuropil coefficient (not implemented)
        'niterneu': 5, # number of iterations when the neuropil coefficient is estimated (not implemented)
        'maxregshift': 0.1, # max allowed registration shift, as a fraction of frame max(width and height)
        'subpixel' : 10, # precision of subpixel registration (1/subpixel steps)
        'batch_size': 200, # number of frames per batch
        'num_workers': 0, # 0 to select num_cores, -1 to disable parallelism, N to enforce value
        'num_workers_roi': -1, # 0 to select number of planes, -1 to disable parallelism, N to enforce value
        'nimg_init': 200, # subsampled frames for finding reference image
        'navg_frames_svd': 5000, # max number of binned frames for the SVD
        'nsvd_for_roi': 1000, # max number of SVD components to keep for ROI detection
        'max_iterations': 20, # maximum number of iterations to do cell detection
        'ratio_neuropil': 6., # ratio between neuropil basis size and cell radius
        'tile_factor': 1., # use finer (>1) or coarser (<1) tiles for neuropil estimation
        'threshold_scaling': 1., # adjust the automatically determined threshold by this scalar multiplier
        'inner_neuropil_radius': 2, # number of pixels to keep between ROI and neuropil donut
        'outer_neuropil_radius': np.inf, # maximum neuropil radius
        'min_neuropil_pixels': 350, # minimum number of pixels in the neuropil
        'ratio_neuropil_to_cell': 3, # minimum ratio between neuropil radius and cell radius
        'allow_overlap': False,
        'combined': True, # combine multiple planes into a single result /single canvas for GUI
        'max_overlap': 0.75, # cells with more overlap than this get removed during triage, before refinement 
	}

Logo

Logo was designed by Shelby Stringer and Chris Czaja.