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- load ('CBdata.mat', 'norm_CB', 'depth')
- load('PRh axon data.mat', 'norm_MouseYFP','MouseDepth')
- %%
- samedepth = dsearchn(MouseDepth, depth);
- %% Averaging two bins in MouseYFP
- temp1 = norm_MouseYFP(1:2:end,:);
- temp2 = norm_MouseYFP(2:2:end,:);
- norm_MouseYFPcorr = (temp1+temp2)./2;
- norm_MouseYFPcorr(387,:) = norm_MouseYFP(772,:);
- %% Effect size
- effect = nanmean(norm_MouseYFPcorr,2) .* nanmean(norm_CB,2);
- %%
- figure
- boundedline(depth, nanmean(norm_MouseYFPcorr,2), nanstd(norm_MouseYFPcorr,[],2)./sqrt(size(norm_MouseYFPcorr,2)),'g-')
- hold on
- boundedline(depth, mean(norm_CB,2), std(norm_CB,[],2)./sqrt(size(norm_CB,2)),'r-')
- plot(depth,effect,'k','linew',2)
- xlim([50 1000])
- ylim([0 1])
- xlabel('Depth (um)')
- ylabel('Normmalized Amplitude')
- % Layer boundaries (Lefort et al., 2009, Neuron)
- L1 = 128;
- L23 = 418;
- L4 = 588;
- L5 = 890;
- L6 = 1154;
- yvals = get(gca,'Ylim');
- hold on
- plot([L1 L1], yvals, 'r--')
- plot([L23 L23], yvals, 'r--')
- plot([L4 L4], yvals, 'r--')
- plot([L5 L5], yvals, 'r--')
- legend({'YFP', 'Chicago blue', 'Effect'})
- %% Quantify Area under the curve
- interval = depth(2)-depth(1);
- area = effect*interval;
- startidx = dsearchn(depth,50)+1; %MouseYFP starts from 50 um
- TotalArea = sum(area(startidx:end));
- LayerBoundaries = [L1; L23; L4; L5];
- LayerIdx = zeros(4,1);
- for i = 1:4
- LayerIdx(i) = dsearchn(depth, LayerBoundaries(i));
- end
- LayerIdx = [startidx-1; LayerIdx; 387];
- LayerAreas = zeros(5,1);
- for i = 1:length(LayerIdx)-1
- LayerAreas(i) = sum(area(LayerIdx(i)+1:LayerIdx(i+1)));
- end
- Percentage = LayerAreas./TotalArea;
- Percentage = Percentage*100
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