https://github.com/cortex-lab/Suite2P
Tip revision: fdf7b227351adcfb44b7449848f3582107e1c1a2 authored by carsen-stringer on 20 December 2016, 19:21:30 UTC
modified readme
modified readme
Tip revision: fdf7b22
master_file_example.m
%% SET ALL DEFAULT OPTIONS HERE
% UPDATE Christmas 2016: number of clusters determined automatically, but
% do specify the "diameter" of an average cell for best results. You can do this with either
% db(iexp).diameter, or ops0.diameter
% check out the README file for detailed instructions (and extra options)
addpath('D:\CODE\MariusBox\runSuite2P') % add the path to your make_db file
% overwrite any of these default options in your make_db file for individual experiments
make_db_example; % RUN YOUR OWN MAKE_DB SCRIPT TO RUN HERE
ops0.toolbox_path = 'C:\CODE\GitHub\Suite2P';
if exist(ops0.toolbox_path, 'dir')
addpath(genpath(ops0.toolbox_path)) % add local path to the toolbox
else
error('toolbox_path does not exist, please change toolbox_path');
end
% mex -largeArrayDims SpikeDetection/deconvL0.c (or .cpp) % MAKE SURE YOU COMPILE THIS FIRST FOR DECONVOLUTION
ops0.useGPU = 1; % if you can use an Nvidia GPU in matlab this accelerates registration approx 3 times. You only need the Nvidia drivers installed (not CUDA).
ops0.fig = 1; % turn off figure generation with 0
% root paths for files and temporary storage (ideally an SSD drive. my SSD is C:/)
ops0.RootStorage = '//zserver4/Data/2P'; % Suite2P assumes a folder structure, check out README file
ops0.temp_tiff = 'C:/DATA/temp.tif'; % copy each remote tiff locally first, into this file
ops0.RegFileRoot = 'C:/DATA/'; % location for binary file
ops0.DeleteBin = 1; % set to 1 for batch processing on a limited hard drive
ops0.ResultsSavePath = 'D:/DATA/F'; % a folder structure is created inside
ops0.RegFileTiffLocation = []; %'D:/DATA/'; % leave empty to NOT save registered tiffs (slow)
% registration options
ops0.doRegistration = 1; % skip (0) if data is already registered
ops0.showTargetRegistration = 1; % shows the image targets for all planes to be registered
ops0.PhaseCorrelation = 1; % set to 0 for non-whitened cross-correlation
ops0.SubPixel = Inf; % 2 is alignment by 0.5 pixel, Inf is the exact number from phase correlation
ops0.NimgFirstRegistration = 500; % number of images to include in the first registration pass
ops0.nimgbegend = 250; % frames to average at beginning and end of blocks
% cell detection options
ops0.ShowCellMap = 1; % during optimization, show a figure of the clusters
ops0.sig = 0.5; % spatial smoothing length in pixels; encourages localized clusters
ops0.nSVDforROI = 1000; % how many SVD components for cell clustering
ops0.NavgFramesSVD = 5000; % how many (binned) timepoints to do the SVD based on
% red channel options
% redratio = red pixels inside / red pixels outside
% redcell = redratio > mean(redratio) + redthres*std(redratio)
% notred = redratio < mean(redratio) + redmax*std(redratio)
ops0.redthres = 1.5; % the higher the thres the less red cells
ops0.redmax = 1; % the higher the max the more NON-red cells
% spike deconvolution options
ops0.imageRate = 30; % imaging rate (cumulative over planes!). Approximate, for initialization of deconvolution kernel.
ops0.sensorTau = 2; % decay half-life (or timescale). Approximate, for initialization of deconvolution kernel.
ops0.maxNeurop = Inf; % for the neuropil contamination to be less than this (sometimes good, i.e. for interneurons)
ops0.recomputeKernel = 1; % whether to re-estimate kernel during optimization (default kernel is "reasonable", if you give good timescales)
ops0.sameKernel = 1; % whether the same kernel should be estimated for all neurons (robust, only set to 0 if SNR is high and recordings are long)
db0 = db;
%% RUN THE PIPELINE HERE
for iexp = [3:length(db) 1:2]
run_pipeline(db(iexp), ops0);
% deconvolved data into (dat.)cl.dcell, and neuropil subtraction coef
add_deconvolution(ops0, db0(iexp));
% add red channel information (if it exists)
if isfield(db0,'expred') && ~isempty(db0(iexp).expred)
ops0.nchannels_red = db0(iexp).nchannels_red;
run_REDaddon(iexp, db0, ops0) ; % create redcell array
DetectRedCells; % fills dat.cl.redcell and dat.cl.notred
end
end
%% STRUCTURE OF RESULTS FILE
% cell traces are in dat.Fcell
% neuropil traces are in dat.FcellNeu
% manual, GUI overwritten "iscell" labels are in dat.cl.iscell
% stat(icell) contains all other information
% autoamted iscell label, based on anatomy
% neuropil subtraction coefficient
% st are the deconvolved spike times (in frames)
% c are the deconvolved amplitudes
% kernel is the estimated kernel