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intensity values are now mean values
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| Original file line number | Diff line number | Diff line change |
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| %% load files and do cross correlation alignment | ||
| %Choose input folder, where all trial folders are and output folder where | ||
| %shift corrected tiff stacks for each trial shall be generated. You can | ||
| %also choose a binning factor [x, y, t]. nSubIm ist the number of | ||
| %iterations the shift correction will do. The more the better, but of | ||
| %course it is more time consuming. The standard value here is 10. | ||
| inFolder = 'F:\Arbeit\testData\PCAICA'; | ||
| outFolder = 'F:\Arbeit\testData\PCAICA\corr'; | ||
| binningFactor = [1, 1, 2]; | ||
| nSubIm = 10; | ||
| CellSortXCorr(inFolder, outFolder, nSubIm, binningFactor); | ||
| %% load files | ||
| %go to corr folder | ||
| outFolder = 'F:\Arbeit\testData\PCAICA'; | ||
| %shifft correction on all frames | ||
| %[data_g, data_r, frames, fn_green, fn_red, fileNamesGreen, fileNamesRed] = CellSortLoadTrials(outFolder); | ||
| %shift correction only on trials | ||
| [data_g, data_r, frames, fn_green, fn_red, fileNamesGreen, fileNamesRed] = CellSortLoadTrialsShiftCorrOnTrials(outFolder); | ||
| %If you want to load single files, use the functions below. | ||
| % fn_green = 'data_g.tif'; | ||
| % fn_red = 'data_r.tif'; | ||
| % data_g = readTiff(fn_green); | ||
| % data_r = readTiff(fn_red); | ||
| %% Crop Data | ||
| %If you want to crop the data to get rid of the shift correction artifacts, | ||
| %type in the intervals of pixels in x and y directions you want to keep and | ||
| %evaluate this section. Use matVis(data_r) to look at your data. | ||
| xInterval = 40:380; | ||
| yInterval = 40:380; | ||
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| data_g = data_g(xInterval,yInterval,:); | ||
| data_r = data_r(xInterval,yInterval,:); | ||
| %% 1. PCA | ||
| %calculate PCAs | ||
| nPCs = 100; | ||
| flims = []; | ||
| dSamp = [1 1]; %temporal, spatial downsampling | ||
| [mixedsig, mixedfilters, CovEvals, covtrace, movm, movtm] = ... | ||
| CellsortPCA(fn_green, flims, nPCs, dSamp, [], [], data_g); | ||
| %% 2a. Choose PCs | ||
| %choose PCAs, type f/b in command line to go back or forward. type two number | ||
| %to select the range "1 ENTER 25 ENTER" would select the PCAs from 1 to 25 | ||
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| [PCuse] = CellsortChoosePCs(fn_green, mixedfilters, data_g); | ||
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| %OR specify the PCAs to use manually: | ||
| %PCuse = [7,8,18,20,21,25,27,30,32,35,36,46]; | ||
| %% 2b. Plot PC spectrum | ||
| %optionally, you can display the PC spectrum.. | ||
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| CellsortPlotPCspectrum(fn_green, CovEvals, PCuse, data_g) | ||
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| %% 3a. ICA | ||
| %calculate ICAs, set mu to a value between 0 and 1, where 1 means pure | ||
| %temporal identification and 0 pure spatial. A value of 0.1-0.2 seems to be | ||
| %best.. make sure that the algorithm converges (see command line). If not | ||
| %run it again | ||
| nIC = length(PCuse); | ||
| mu = 0.2; | ||
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| [ica_sig, ica_filters, ica_A, numiter] = CellsortICA(mixedsig, mixedfilters, CovEvals, PCuse, mu, nIC); | ||
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| %% 3b. Plot ICs | ||
| %optionally, plot ICs (you have to use at least 20.. or change the number | ||
| %on the buttom | ||
| tlims = []; | ||
| dt = 0.1; | ||
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| figure(2) | ||
| CellsortICAplot('series', ica_filters, ica_sig, movm, tlims, dt, [], [], [1:size(ica_sig,1)]); | ||
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| %% 4a. Segment contiguous regions within ICs | ||
| %search for indepent segments, you have to adjust these values well: | ||
| %smwidth: width of a smoothing filter (1 should be okay) | ||
| %thresh: threshold for normalized standard deviation in ICs (verey | ||
| % sensitive, standard: 2) | ||
| %arealims: limit for the detected areas in pixeks: [min max] or [min] | ||
| %plotting: 1 = yes or 0 = no.. shall a plot be shown or not? | ||
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| smwidth = 1; | ||
| thresh = 1.3; | ||
| arealims = [200]; | ||
| plotting = 1; | ||
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| [ica_segments, segmentlabel, segcentroid] = CellsortSegmentation(ica_filters, smwidth, thresh, arealims, plotting); | ||
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| %% 4b. CellsortApplyFilter | ||
| %filter signal and extract regions signals | ||
| cell_sig = CellsortApplyFilter(ica_segments, flims, , data_g); | ||
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| %% 5. CellsortFindspikes | ||
| %optionally, find spikes in signal | ||
| deconvtau = 0; | ||
| spike_thresh = 2; | ||
| normalization = 1; | ||
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| [spmat, spt, spc] = CellsortFindspikes(ica_sig, spike_thresh, dt, deconvtau, normalization); | ||
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| %% Show results | ||
| %optionally, plot signals | ||
| figure(2) | ||
| CellsortICAplot('series', ica_filters, ica_sig, movm, tlims, dt, 1, 2, 1:20, spt, spc); | ||
| %% caluclate correlation matrix for cell_sig | ||
| clusterThreshold = 0.9; | ||
| [ica_segments, cluster, subPlotCols, subPlotWidth, sortedIdx] = calcCorrEff(cell_sig, ica_segments, clusterThreshold); | ||
| %% plot | ||
| %acustic sweep parameters: | ||
| %number of frames after trial begin when first sweep occurrs | ||
| sweepDelay = 100; | ||
| %number of frames between sweeps | ||
| sweepInterval = 30; | ||
| %number of sweeps | ||
| numberOfSweeps = 5; | ||
| %number of frames a sweep lasts | ||
| sweepDuration = 10; | ||
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| %number of time courses to show when slide bar is used | ||
| clusterSize = 10; | ||
| frameSteps = 20; | ||
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| cell_sig_merged = alternativePlot(ica_segments, cluster, cell_sig, subPlotCols, sortedIdx, data_r, frames, fileNamesGreen, clusterSize, sweepDelay, sweepInterval, numberOfSweeps, sweepDuration, frameSteps); | ||
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| %% let user deselect and merge rois | ||
| %merge -> change cluster ->recereate plots | ||
| merge = {[1,2,3], [5,6]}; | ||
| delete = [8,4,11]; | ||
| figure; | ||
| cluster_new = cluster; | ||
| %delete | ||
| for i = 1:size(delete,2) | ||
| cluster_new{delete(i)} = []; | ||
| end | ||
| %merge | ||
| for i = 1:size(merge, 2) | ||
| for j = 2:size(merge{i},2) | ||
| cluster_new{merge{i}(1)} = [cluster_new{merge{i}(1)}, cluster_new{merge{i}(j)}]; | ||
| cluster_new{merge{i}(j)} = []; | ||
| end | ||
| end | ||
| cluster_new = cluster_new(~cellfun(@isempty, cluster_new)); | ||
| subPlotCols = size(cluster_new, 2); | ||
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| %redraw | ||
| cell_sig_merged = alternativePlot(ica_segments, cluster_new, cell_sig, subPlotCols, sortedIdx, data_r); |
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