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flywire_reciprocal_connectivity.m
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flywire_reciprocal_connectivity.m
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% Using codex data format (Albert Lin)
directory = 'DIRECTORY';
cd(directory)
%% load data from csvs
% neurons in neurons.csv and neuropil synapse table are in the same order,
% thresholded
data_directory = 'DATA DIRECTORY';
opts = detectImportOptions(fullfile(data_directory,'neurons.csv'));
disp([opts.VariableNames' opts.VariableTypes'])
opts = setvartype(opts,{'root_id'},'int64');
T_n = readtable(fullfile(data_directory,'neurons.csv'),opts);
opts2 = detectImportOptions(fullfile(data_directory,'neuropil_synapse_table.csv'));
disp([opts2.VariableNames' opts2.VariableTypes'])
opts2 = setvartype(opts2,{'root_id'},'int64');
T_np = readtable(fullfile(data_directory,'neuropil_synapse_table.csv'),opts2);
%% load colors from spreadsheet
np_color_import = readcell('neuropil color key.xlsx','Sheet','nps','Range','A2:C79');
np_ordered = np_color_import(:,1);
np_hex_colors = np_color_import(:,2);
np_names = np_color_import(:,3);
nt_hex_colors = readcell('neuropil color key.xlsx','Sheet','nts','Range','B1:B6');
superclass_import = readcell('neuropil color key.xlsx','Sheet','superclasses','Range','A2:B12');
superclasses = superclass_import(:,1);
superclass_hex_colors = superclass_import(:,2);
%% nps alphabetical with 3 new ones for 630
all_regions = {'AL_L', 'AL_R', 'AME_L', 'AME_R', 'AMMC_L', 'AMMC_R', 'AOTU_L',...
'AOTU_R', 'ATL_L', 'ATL_R', 'AVLP_L', 'AVLP_R', 'BU_L', 'BU_R',...
'CAN_L', 'CAN_R', 'CRE_L', 'CRE_R', 'EB', 'EPA_L', 'EPA_R', 'FB',...
'FLA_L', 'FLA_R', 'GA_L', 'GA_R', 'GNG', 'GOR_L', 'GOR_R', 'IB_L',...
'IB_R', 'ICL_L', 'ICL_R', 'IPS_L', 'IPS_R', 'LAL_L', 'LAL_R','LA_L','LA_R',...
'LH_L', 'LH_R', 'LOP_L', 'LOP_R', 'LO_L', 'LO_R', 'MB_CA_L',...
'MB_CA_R', 'MB_ML_L', 'MB_ML_R', 'MB_PED_L', 'MB_PED_R', 'MB_VL_L',...
'MB_VL_R', 'ME_L', 'ME_R', 'NO','OCG', 'PB', 'PLP_L', 'PLP_R',...
'PRW', 'PVLP_L', 'PVLP_R', 'SAD', 'SCL_L', 'SCL_R', 'SIP_L',...
'SIP_R', 'SLP_L', 'SLP_R', 'SMP_L', 'SMP_R', 'SPS_L', 'SPS_R',...
'VES_L', 'VES_R', 'WED_L', 'WED_R'};
all_nts = {'ach', 'gaba','glut', 'da', 'oct', 'ser'};
%%
nt_colors = hex2rgb(nt_hex_colors);
np_colors = hex2rgb(np_hex_colors);
superclass_colors = hex2rgb(superclass_hex_colors);
%% to do: extract neuron info as required
% not all neurons in T_n are also in T_np: find the np ones in the T_n list
neuron_IDs_T_n = table2array(T_n(:,1));
out_nts_T_n = table2array(T_n(:,4));
neuron_IDs = table2array(T_np(:,1));
% extract nts for T_np neurons
clear out_nts
for i = 1:length(neuron_IDs)
idx = find(neuron_IDs_T_n == neuron_IDs(i));
out_nts{i} = out_nts_T_n{idx};
end
%% load array of classification for flow
opts5 = detectImportOptions(fullfile(data_directory,'classification.csv'));
disp([opts5.VariableNames' opts5.VariableTypes'])
opts5 = setvartype(opts5,{'root_id'},'int64');
T_class = readtable(fullfile(data_directory,'classification.csv'),opts5);
neuron_IDs_T_class = table2array(T_class(:,1));
flow_T_class = table2array(T_class(:,2));
class_T_class = table2array(T_class(:,3));
clear flow
for i = 1:length(neuron_IDs)
idx = find(neuron_IDs_T_class == neuron_IDs(i));
flow{i} = flow_T_class{idx};
classes{i} = class_T_class{idx};
end
% these variables got moved to another sheet
%flow = table2array(T_n(:,12));
%classes = table2array(T_n(:,13));
%% determine neuron fractions by degree
tic
in_degree = table2array(T_np(:,3));
out_degree = table2array(T_np(:,5));
in_neuron_frac = zeros(length(neuron_IDs),length(all_regions));
out_neuron_frac = zeros(length(neuron_IDs),length(all_regions));
% synapses
in_syn = table2array(T_np(:,2));
out_syn = table2array(T_np(:,4));
in_syn_frac = zeros(length(neuron_IDs),length(all_regions));
out_syn_frac = zeros(length(neuron_IDs),length(all_regions));
% extracting in alphabetical order first since that follows the file format
% later, swap matrices into np_ordered order
for i = 1:length(neuron_IDs)
input_neuron_num = table2array(T_np(i,84:161));
output_neuron_num = table2array(T_np(i,240:317));
input_syn_num = table2array(T_np(i,6:83));
output_syn_num = table2array(T_np(i,162:239));
in_neuron_frac(i,:) = input_neuron_num./in_degree(i);
out_neuron_frac(i,:) = output_neuron_num./out_degree(i);
in_syn_frac(i,:) = input_syn_num./in_syn(i);
out_syn_frac(i,:) = output_syn_num./out_syn(i);
end
toc
%%
[max_in_deg, max_in_idx] = maxk(in_degree,10);
[max_out_deg, max_out_idx] = maxk(out_degree,10);
top10_in_deg_ns = neuron_IDs(max_in_idx)
top10_out_deg_ns = neuron_IDs(max_out_idx)
% avg synapses per incoming connection:
%sum(in_syn)/sum(in_degree)
%sum(out_syn)/sum(out_degree)
%% correlation of in and out degree
filter = (in_degree >= 5) & (out_degree >= 5);
[R,P,RL,RU] = corrcoef(in_degree(filter),out_degree(filter))
%% scatterplot of in degree vs out degree across neurons
output_dir = fullfile(directory,'Degree Scatterplots 630');
mkdir(output_dir)
%h = scatterhist(in_deg_by_in_deg,out_deg_by_in_deg,'Direction','out',...
% 'Color','k','LineStyle',{'-','-'},'Marker','.');
s = scatterhist(log10(in_degree),log10(out_degree),'Direction','out',...
'Color','k','LineStyle','none','Marker','.');
s(2).Children.EdgeColor = 'w';
s(2).Children.BinWidth = 0.07;
s(3).Children.EdgeColor = 'w';
s(3).Children.BinWidth = 0.07;
%s(2).GridLineStyle = 'none';
xlabel('in degree')
ylabel('out degree')
xlim([-0.2 4.2])
ylim([-0.2 4.2])
axis square
set(gca,'Xtick',[0 1 2 3 4])
set(gca,'XtickLabel',[1 10 100 1000 10000])
set(gca,'Ytick',[0 1 2 3 4])
set(gca,'YtickLabel',[1 10 100 1000 10000])
%h = axes;
%get(h,'XTickLabel')
%h(2).Children(1).YScale = 'log';
%xlim([0 3000])
%ylim([0 3000])
%set(gca,'xscale','log')
%set(gca,'yscale','log')
set(gcf,'position',[2000,100,800,800])
set(gca,'fontsize',20)
set(gcf,'renderer','Painters')
%saveas(gcf,strcat(output_dir,'\all_neurons_scatter'),'epsc')
%saveas(gcf,strcat(output_dir,'\all_neurons_scatter'),'png')
%% load reciprocal lists
% load KC file
data_directory_rc = 'DATA DIRECTORY';
opts_allpairs = detectImportOptions(fullfile(data_directory_rc,'v630-all-reciprocal-pairs-s1.csv'));
disp([opts_allpairs.VariableNames' opts_allpairs.VariableTypes'])
opts_allpairs = setvartype(opts_allpairs,{'n1'},'int64');
opts_allpairs = setvartype(opts_allpairs,{'n2'},'int64');
T_rc = readtable(fullfile(data_directory_rc,'v630-all-reciprocal-pairs-s1.csv'),opts_allpairs);
rc_ns = table2array(T_rc(1:end,1:2));
%% distributions of numbers of neurons participating in recurrent connections
output_dir = 'OUTPUT DIRECTORY';
rc_ns_reshape = reshape(rc_ns,[],1);
[unique_rns,ia,ic] = unique(rc_ns_reshape);
rconn_counts = accumarray(ic,1);
rconn_ns = [unique_rns, rconn_counts];
rconn_ns_ordered = sortrows(rconn_ns,2,'descend'); % row 2 is recurrent degree
rconn_ns_ids_ordered = rconn_ns_ordered(:,1);
rconns_ordered = cast(rconn_ns_ordered(:,2),'double');
% order by rconn_counts
edges = 0:0.1:4;
%histogram(in_degree,10.^edges,'FaceColor','r','EdgeColor','r')
%hold on
%histogram(out_degree,10.^edges,'FaceColor','b','EdgeColor','b')
%hold on
fig = histogram(rconns_ordered,10.^edges,'FaceColor','k','EdgeColor','k');
set(gca,'xscale','log')
set(gca,'yscale','log')
%xlim([0 5000])
ylim([0 10^5])
xlabel('number of reciprocal connections')
ylabel('neuron count')
%title(strcat('neurons for which',{' '},neuropil,' is the max-in region'))
set(gcf,'position',[100,100,300,300])
set(gca,'FontSize',14)
set(gcf,'renderer','Painters')
saveas(fig,strcat(output_dir,'\recurrent_degree_dist'),'epsc')
saveas(fig,strcat(output_dir,'\recurrent_degree_dist'),'png')
%%
histogram(in_degree,10.^edges,'FaceColor','r','EdgeColor','r')
hold on
histogram(out_degree,10.^edges,'FaceColor','b','EdgeColor','b')
hold on
fig = histogram(rconns_ordered,10.^edges,'FaceColor','k','EdgeColor','k');
set(gca,'xscale','log')
%set(gca,'yscale','log')
%xlim([0 5000])
xlabel('no. of reciprocal connections')
ylabel('neuron count')
%title(strcat('neurons for which',{' '},neuropil,' is the max-in region'))
set(gcf,'position',[100,100,500,500])
set(gca,'FontSize',14)
legend("in degree","out degree","reciprocal degree")
set(gcf,'renderer','Painters')
saveas(fig,strcat(output_dir,'\recurrent_degree_dist_in_out_deg_semilog'),'epsc')
saveas(fig,strcat(output_dir,'\recurrent_degree_dist_in_out_deg_semilog'),'png')
%% build individual vectors that contains other relevant info (nts, normal degree, highest neuropil in and out fractions)
clear rc_ns_nt rc_ns_in_np rc_ns_out_np
rc_ns_in_degree = zeros(size(unique_rns));
rc_ns_out_degree = zeros(size(unique_rns));
rc_ns_nt = {};
rc_ns_flow = {};
rc_ns_max_in_frac = zeros(size(unique_rns));
rc_ns_max_out_frac = zeros(size(unique_rns));
rc_ns_in_np = {};
rc_ns_out_np = {};
n = 0;
for i = 1:length(unique_rns)
root_id = rconn_ns_ordered(i,1);
idx = find(neuron_IDs == root_id);
if ~isempty(idx)
rc_ns_in_degree(i) = in_degree(idx);
rc_ns_out_degree(i) = out_degree(idx);
rc_ns_nt{i} = out_nts{idx};
rc_ns_flow{i} = flow{idx};
[rc_ns_max_in_frac(i),np_in_idx] = max(in_neuron_frac(idx,:)); %nps not reordered
rc_ns_in_np{i} = all_regions{np_in_idx};
[rc_ns_max_out_frac(i),np_out_idx] = max(out_neuron_frac(idx,:)); %nps not reordered
rc_ns_out_np{i} = all_regions{np_out_idx};
else
%disp(root_id);
rc_ns_in_degree(i) = NaN;
rc_ns_out_degree(i) = NaN;
n = n+1;
end
end
n
%% identify the neurotransmitters of those neurons, distribution by nt
for i = 1:6
nt_idx = strcmpi(rc_ns_nt,all_nts{i});
edges = 0:0.1:4;
%histogram(in_degree,10.^edges,'FaceColor','r','EdgeColor','r')
%hold on
%histogram(out_degree,10.^edges,'FaceColor','b','EdgeColor','b')
%hold on
histogram(rconns_ordered(nt_idx),10.^edges,'FaceColor',nt_colors(i,:),'EdgeColor','k','FaceAlpha',1);
set(gca,'xscale','log')
set(gca,'yscale','log')
%xlim([0 5000])
ylim([0 10^5])
xlabel('no. of reciprocal connections')
ylabel('neuron count')
title(strcat(all_nts{i},{' '},'neurons'))
set(gcf,'position',[100,100,350,220])
set(gca,'FontSize',14)
%legend("in degree","out degree","reciprocal degree")
set(gcf,'renderer','Painters')
saveas(gcf,strcat(output_dir,strcat('\recurrent_degree_dist_',all_nts{i})),'epsc')
saveas(gcf,strcat(output_dir,strcat('\recurrent_degree_dist_',all_nts{i})),'png')
close all
end
%% identify the neurotransmitters of those neurons, distribution by nt semilog
for i = 1:6
nt_idx = strcmpi(rc_ns_nt,all_nts{i});
edges = 0:0.1:4;
%histogram(in_degree,10.^edges,'FaceColor','r','EdgeColor','r')
%hold on
%histogram(out_degree,10.^edges,'FaceColor','b','EdgeColor','b')
%hold on
histogram(rconns_ordered(nt_idx),10.^edges,'FaceColor',nt_colors(i,:),'EdgeColor','k','FaceAlpha',1);
set(gca,'xscale','log')
%xlim([0 5000])
ylim([0 3*10^4])
xlabel('no. of reciprocal connections')
ylabel('neuron count')
title(strcat(all_nts{i},{' '},'neurons'))
set(gcf,'position',[100,100,350,220])
set(gca,'FontSize',14)
%legend("in degree","out degree","reciprocal degree")
set(gcf,'renderer','Painters')
saveas(gcf,strcat(output_dir,strcat('\recurrent_degree_dist_semilog_',all_nts{i})),'epsc')
saveas(gcf,strcat(output_dir,strcat('\recurrent_degree_dist_semilog_',all_nts{i})),'png')
close all
end
%% fraction of in degrees that are recurrent
% some are over, why?
rc_ns_in_recfrac = rconns_ordered./rc_ns_in_degree;
edges = 0:0.02:1;
fig = histogram(rc_ns_in_recfrac,edges,'FaceColor',[0.8 0.5 0.4],'EdgeColor','k','FaceAlpha',1);
%set(gca,'xscale','log')
%set(gca,'yscale','log')
xlim([0 1])
xlabel('reciprocal connections/incoming connections')
ylabel('neuron count')
%title(strcat('neurons for which',{' '},neuropil,' is the max-in region'))
set(gcf,'position',[100,100,500,400])
set(gca,'FontSize',12)
set(gcf,'renderer','Painters')
saveas(fig,strcat(output_dir,'\recurrent_in_frac'),'epsc')
saveas(fig,strcat(output_dir,'\recurrent_in_frac'),'png')
%% fraction of out degrees that are recurrent
rc_ns_out_recfrac = rconns_ordered./rc_ns_out_degree;
edges = 0:0.02:1;
fig = histogram(rc_ns_out_recfrac,edges,'FaceColor',[0.4 0.6 1],'EdgeColor','k','FaceAlpha',1);
%set(gca,'xscale','log')
%set(gca,'yscale','log')
xlim([0 1])
xlabel('reciprocal connections/outgoing connections')
ylabel('neuron count')
%title(strcat('neurons for which',{' '},neuropil,' is the max-in region'))
set(gcf,'position',[100,100,500,400])
set(gca,'FontSize',12)
set(gcf,'renderer','Painters')
saveas(fig,strcat(output_dir,'\recurrent_out_frac'),'epsc')
saveas(fig,strcat(output_dir,'\recurrent_out_frac'),'png')
%% compare recurrent degree to in and out degree fraction (scatterplots)
% remember that a recurrent connection counts as both an in degree and and
% out degree
% we could ask what fraction of degree is accounted for by recurrent
% connections
s = scatterhistogram(rc_ns_in_recfrac,rc_ns_out_recfrac,'color',[0 0 0],'MarkerStyle','.');
s.ScatterPlotLocation = 'NorthEast';
s.ScatterPlotProportion = 0.8;
s.XHistogramDirection = 'down';
s.YHistogramDirection = 'left';
s.HistogramDisplayStyle = 'bar';
s.LineStyle = 'none';
%scatterhandle = findobj(s(1))
xlabel('reciprocal connections/incoming connections')
ylabel('reciprocal connections/outgoing connections')
%xlabel('in degree')
%ylabel('out degree')
%s(2).Children(1).YScale = 'log';
xlim([0 1])
ylim([0 1])
set(gca,'FontSize',12)
%set(gca,'xscale','log')
%set(gca,'yscale','log')
%% compare recurrent degree to in and out degree fraction heatmap
% remember that a recurrent connection counts as both an in degree and and
% out degree
% we could ask what fraction of degree is accounted for by recurrent
% connections
Xedges = 0:0.05:1;
Yedges = 0:0.05:1;
h = histogram2(rc_ns_in_recfrac,rc_ns_out_recfrac,Xedges,Yedges,'DisplayStyle','tile','ShowEmptyBins','on');
h.EdgeColor = 'none';
set(gca,'ColorScale','log')
cb1 = colorbar;
cb1.Label.String = 'neuron counts';
%scatterhandle = findobj(s(1))
xlabel('reciprocal conn./incoming conn.')
ylabel('reciprocal conn./outgoing conn.')
%xlabel('in degree')
%ylabel('out degree')
%s(2).Children(1).YScale = 'log';
xlim([0 1])
ylim([0 1])
axis square
set(gca,'FontSize',20)
set(gcf,'position',[100,100,600,600])
set(gcf,'renderer','Painters')
saveas(gcf,strcat(output_dir,'\infrac_vs_outfrac_heatmap'),'epsc')
saveas(gcf,strcat(output_dir,'\infrac_vs_outfrac_heatmap'),'png')
%% try scatterplot of in vs out, colored by recurrence
f = figure;
s = scatter(rc_ns_in_degree,rc_ns_out_degree,3,rconns_ordered,'filled');
colormap(colorcet('L12','reverse',false)); % set the colorscheme, diverging
c = colorbar;
set(gca,'ColorScale','log')
xlabel('in degree')
ylabel('out degree')
c.Label.String='no. reciprocal connections';
c.Label.FontSize = 15;
xlim([0 6500])
ylim([0 6500])
set(gca,'FontSize',15)
%set(gca,'xscale','log')
%set(gca,'yscale','log')
set(gcf,'position',[100,100,600,600])
set(gcf,'renderer','Painters')
saveas(gcf,strcat(output_dir,'\in_vs_out_deg_rec_lin'),'epsc')
saveas(gcf,strcat(output_dir,'\in_vs_out_deg_rec_lin'),'png')
%% scatterplot of recurrence x 2 vs total degree
f = figure;
error_filter = (rc_ns_in_degree+rc_ns_out_degree) >= rconns_ordered*2;
s = scatter(rc_ns_in_degree(error_filter)+rc_ns_out_degree(error_filter),rconns_ordered(error_filter)*2,10,'k','filled',...
'MarkerFaceAlpha',0.2);
hold on
xline = 0:5;
plot(10.^xline,10.^xline,'k','linewidth',2)
hold on
plot(10.^xline,2*10.^xline,'--k')
hold on
plot(2*10.^xline,10.^xline,'--k')
xlabel('total degree')
ylabel('2x reciprocal degree')
xlim([0 12000])
ylim([0 12000])
set(gca,'FontSize',13)
set(gca,'xscale','log')
set(gca,'yscale','log')
set(gcf,'position',[100,100,600,600])
set(gcf,'renderer','Painters')
% works but takes forever and kind of crashes matlab
%saveas(gcf,strcat(output_dir,'\deg_vs_rec'),'pdf')
%works okay but is slow
saveas(gcf,strcat(output_dir,'\deg_vs_rec'),'svg')
% doesn't work for scatter
%plot2svg(strcat(output_dir,'\deg_vs_rec.svg'))
saveas(gcf,strcat(output_dir,'\deg_vs_rec'),'png')
%% count of neurons
rc_ns_intrinsic_filter = strcmp(rc_ns_flow,'intrinsic');
sum((rc_ns_in_degree(rc_ns_intrinsic_filter)+rc_ns_out_degree(rc_ns_intrinsic_filter))/2 < rconns_ordered(rc_ns_intrinsic_filter)*2)
%% scatterplot of recurrence x 2 vs total degree by neurotransmitter
f = figure;
error_filter = (rc_ns_in_degree+rc_ns_out_degree) >= rconns_ordered*2;
for i = 1:6
nt_idx = strcmpi(rc_ns_nt,all_nts{i});
s = scatter(rc_ns_in_degree(error_filter & nt_idx')+rc_ns_out_degree(error_filter & nt_idx'),...
rconns_ordered(error_filter& nt_idx')*2,15,nt_colors(i,:),'filled','MarkerFaceAlpha',0.3,'MarkerEdgeAlpha',0.3);
hold on
end
xline = 0:5;
plot(10.^xline,10.^xline,'k','linewidth',2)
hold on
plot(10.^xline,2*10.^xline,'--k')
hold on
plot(2*10.^xline,10.^xline,'--k')
xlabel('total degree')
ylabel('2x reciprocal degree')
xlim([0 13000])
ylim([0 13000])
%legend(all_nts,'location','northwest')
set(gca,'FontSize',13)
set(gca,'xscale','log')
set(gca,'yscale','log')
set(gcf,'position',[100,100,600,600])
set(gcf,'renderer','Painters')
%works okay but is slow (eps doesn't work for transparency)
saveas(gcf,strcat(output_dir,'\deg_vs_rec_allnt'),'svg')
saveas(gcf,strcat(output_dir,'\deg_vs_rec_allnt'),'png')
%% scatterplot of recurrence x 2 vs total degree by individual neurotransmitter
f = figure;
error_filter = (rc_ns_in_degree+rc_ns_out_degree) >= rconns_ordered*2;
for i = 1:6
nt_idx = strcmpi(rc_ns_nt,all_nts{i});
s = scatter(rc_ns_in_degree(error_filter & nt_idx')+rc_ns_out_degree(error_filter & nt_idx'),...
rconns_ordered(error_filter& nt_idx')*2,15,nt_colors(i,:),'filled','MarkerFaceAlpha',0.3,'MarkerEdgeAlpha',0.3);
hold on
xline = 0:5;
plot(10.^xline,10.^xline,'k','linewidth',2)
hold on
plot(10.^xline,2*10.^xline,'--k')
hold on
plot(2*10.^xline,10.^xline,'--k')
xlabel('total degree')
ylabel('2x reciprocal degree')
title(all_nts{i})
xlim([0 13000])
ylim([0 13000])
set(gca,'FontSize',13)
set(gca,'xscale','log')
set(gca,'yscale','log')
set(gcf,'position',[100,100,600,600])
set(gcf,'renderer','Painters')
%works okay but is slow (eps doesn't work for transparency)
saveas(gcf,strcat(output_dir,strcat('\deg_vs_rec_',all_nts{i})),'svg')
saveas(gcf,strcat(output_dir,strcat('\deg_vs_rec_',all_nts{i})),'png')
close all
end
%% distribution of max in single-neuropil fraction of recurrent neurons (break this by nt)
edges = 0:0.02:1;
% remove 0s
fig = histogram(nonzeros(rc_ns_max_in_frac),edges,'FaceColor',[0.8 0.5 0.4],'EdgeColor','k','FaceAlpha',1);
%set(gca,'xscale','log')
%set(gca,'yscale','log')
xlim([0 1])
xlabel('max frac. of incoming connections in a single neuropil')
ylabel('neuron count')
title(strcat('neurons participating in reciprocal connections'))
set(gcf,'position',[100,100,500,400])
set(gca,'FontSize',12)
set(gcf,'renderer','Painters')
saveas(gcf,strcat(output_dir,'\max_in_frac'),'epsc')
saveas(gcf,strcat(output_dir,'\max_in_frac'),'png')
%% distribution of max out single-neuropil fraction of recurrent neurons (break this by nt)
edges = 0:0.02:1;
% remove 0s
fig = histogram(nonzeros(rc_ns_max_out_frac),edges,'FaceColor',[0.4 0.6 1],'EdgeColor','k','FaceAlpha',1);
%set(gca,'xscale','log')
%set(gca,'yscale','log')
xlim([0 1])
xlabel('max frac. of outgoing connections in a single neuropil')
ylabel('neuron count')
title(strcat('neurons participating in reciprocal connections'))
set(gcf,'position',[100,100,500,400])
set(gca,'FontSize',12)
set(gcf,'renderer','Painters')
saveas(gcf,strcat(output_dir,'\max_out_frac'),'epsc')
saveas(gcf,strcat(output_dir,'\max_out_frac'),'png')
%% set a cutoff for "highly reciprocal" neurons, histogram of neuropil frac outgoing
% more than 50 reciprocal connections
thresh = 98/2;
high_filter = rconns_ordered >= thresh;
sum(high_filter)
edges = 0:0.02:1;
fig = histogram(rc_ns_max_out_frac(high_filter),edges,'FaceColor',[0.4 0.6 1],'EdgeColor','k','FaceAlpha',1);
xlim([0 1])
xlabel('max frac. of outgoing connections in a single neuropil')
ylabel('neuron count')
title(strcat('neurons with >=',num2str(thresh), 'reciprocal connections'))
set(gcf,'position',[100,100,500,400])
set(gca,'FontSize',12)
set(gcf,'renderer','Painters')
saveas(gcf,strcat(output_dir,'\max_out_frac_',num2str(thresh)),'epsc')
saveas(gcf,strcat(output_dir,'\max_out_frac_',num2str(thresh)),'png')
%% histogram of neuropil frac incoming
edges = 0:0.02:1;
fig = histogram(rc_ns_max_in_frac(high_filter),edges,'FaceColor',[0.8 0.5 0.4],'EdgeColor','k','FaceAlpha',1);
xlim([0 1])
xlabel('max frac. of incoming connections in a single neuropil')
ylabel('neuron count')
title(strcat('neurons with >=',num2str(thresh), 'reciprocal connections'))
set(gcf,'position',[100,100,500,400])
set(gca,'FontSize',12)
set(gcf,'renderer','Painters')
saveas(gcf,strcat(output_dir,'\max_in_frac_',num2str(thresh)),'epsc')
saveas(gcf,strcat(output_dir,'\max_in_frac_',num2str(thresh)),'png')
%% do neuropils (based on fraction of connections)
% if both are high in the same neuropil, then it is tiling
thresh = 37;
high_filter = (rc_ns_in_degree+rc_ns_out_degree) >= thresh;
highly_rec_filter = ((rc_ns_in_degree+rc_ns_out_degree)/2 < rconns_ordered*2);
% TO DO: set a threshold for fraction of connections before doing this
frac_thresh = 0.5;
high_fracin_filter = rc_ns_max_in_frac >= frac_thresh;
high_fracout_filter = rc_ns_max_out_frac >= frac_thresh;
high_in_np = rc_ns_in_np(high_filter & high_fracin_filter & high_fracout_filter & highly_rec_filter & rc_ns_intrinsic_filter');
high_out_np = rc_ns_out_np(high_filter & high_fracin_filter & high_fracout_filter & highly_rec_filter & rc_ns_intrinsic_filter');
high_ns_ids = rconn_ns_ids_ordered(high_filter & high_fracin_filter & high_fracout_filter & highly_rec_filter & rc_ns_intrinsic_filter');
high_ns_nt = rc_ns_nt(high_filter & high_fracin_filter & high_fracout_filter & highly_rec_filter & rc_ns_intrinsic_filter');
internal_high_filter = strcmp(high_in_np,high_out_np);
% number of "internal" reciprocal neurons
sum(internal_high_filter)
internal_high_nps = high_in_np(internal_high_filter);
internal_high_ns = high_ns_ids(internal_high_filter);
internal_high_nts = high_ns_nt(internal_high_filter);
[unique_high_internal_nps,ia,ic] = unique(internal_high_nps);
unique_high_internal_np_counts = accumarray(ic,1);
% bar plot tiling neurons
% bar(unique_high_internal_np_counts)
% xlabel('neuropils')
% set(gca,'xtick',1:length(unique_high_internal_nps),'xticklabel',unique_high_internal_nps)
% xtickangle(90)
% ylabel('neuron count')
% title(strcat('tiling neurons: frac. of neuropil conn. >= ',num2str(frac_thresh),'& reciprocal deg. >= ',num2str(thresh)))
% sort for the purposes of plotting histogram with names and colors
unique_high_internal_np_counts_forplotting = zeros(size(unique_high_internal_np_counts));
unique_high_internal_nps_forplotting = {};
unique_high_internal_npnames_forplotting = {};
idx = 1;
for i = 1:length(np_ordered)
if sum(strcmp(unique_high_internal_nps,np_ordered{i})) > 0
data_idx = find(strcmp(unique_high_internal_nps,np_ordered{i}));
unique_high_internal_np_counts_forplotting(idx) = unique_high_internal_np_counts(data_idx);
unique_high_internal_nps_forplotting{idx} = np_ordered{i};
unique_high_internal_npnames_forplotting{idx} = np_names{i};
unique_high_np_colors(idx,:) = np_colors(i,:);
idx = idx+1;
end
end
% find the proper printing version of these neuropils
bar(unique_high_internal_np_counts_forplotting,'facecolor','k','edgecolor','none')
%xlabel('neuropils')
set(gca,'xtick',1:length(unique_high_internal_npnames_forplotting),'xticklabel',unique_high_internal_npnames_forplotting)
xtickangle(90)
ax = gca;
for m = 1:length(unique_high_internal_npnames_forplotting)
ax.XTickLabel{m} = [strcat('\color[rgb]{',num2str(unique_high_np_colors(m,:)),'} '),ax.XTickLabel{m}];
end
set(gca,'FontSize',11)
ylabel('neuron count','FontSize',15)
%title(strcat('frac. of np conn. >= ',num2str(frac_thresh),'& rec. deg. >= ',num2str(thresh)))
set(gcf,'position',[100,100,800,400])
set(gcf,'renderer','Painters')
saveas(gcf,strcat(output_dir,strcat('\tiling_reciprocal_neurons_bar')),'epsc')
saveas(gcf,strcat(output_dir,strcat('\tiling_reciprocal_neurons_bar')),'png')
% print this plot
%% PRINT THESE AS CSV
%%
neuropil = "LO_R";
% find neurons associated with this neuropil
np_idx = find(strcmp(unique_high_internal_nps,neuropil));
internal_high_ns(find(ic == np_idx))
% scatterplot in frac out frac
%% nts of internal high neurons
%internal_high_ns = high_ns_ids(internal_high_filter);
internal_high_nts = high_ns_nt(internal_high_filter);
% pie chart of nt composition of 4 categories
nt_num_internal_high = zeros(1,6);
nt_frac_internal_high = zeros(1,6);
for i = 1:6
nt_num_internal_high(i) = sum(strcmpi(internal_high_nts,all_nts{i}));
nt_frac_internal_high(i) = nt_num_internal_high(i)/length(internal_high_nts);
end
explode = [0 0 0 1 1 1];
% Create pie charts
p1 = pie([nt_frac_internal_high,1-sum(nt_frac_internal_high)]); %,explode
for i=1:2:13
p1(i).EdgeColor = 'none';
p1(i+1).FontSize = 30;
end
colormap([nt_colors;0.8 0.8 0.8])
%title('all neurons','FontSize',20)
% Create legend
nt_legend = all_nts;
nt_legend{7} = 'uncertain';
lgd = legend(nt_legend,'FontSize',30,'location','eastoutside');
%lgd.Layout.Tile = 'east';
% nt fractions for all neurons
set(gcf,'position',[100,100,1000,600])
set(gcf,'renderer','Painters')
saveas(gcf,strcat(output_dir,'\internal_nt'),'epsc')
saveas(gcf,strcat(output_dir,'\internal_nt'),'png')
%% apply filters using flow and classes
n_intrinsic_filter = strcmp(flow,'intrinsic');
factor = 5;
% look at degree scatterplot for integrators and broadcasters
% to do: shade each region
% scatterplot
scatter(in_degree(n_intrinsic_filter),out_degree(n_intrinsic_filter),10,'filled','markerfacecolor',[0.5 0.5 0.5]);
hold on
% plot circle
r = 37; % double the reciprocal degree
circ_x = 1:0.001:r;
circ_y = (r^2 - circ_x.^2).^0.5;
plot(circ_x,circ_y,'k','linewidth',2)
hold on
% number of neurons in the circle
small_neuron_number = sum((in_degree(n_intrinsic_filter)+out_degree(n_intrinsic_filter)) < r)
% plot boundary for "broadcast" (few to many) neurons
% out_degree > 40, out_degree 5x in_degree
broadcast_number = sum(((in_degree(n_intrinsic_filter)+out_degree(n_intrinsic_filter)) >= r) & (out_degree(n_intrinsic_filter) >= in_degree(n_intrinsic_filter)*factor))
y_broadcast = r:7000;
x_broadcast = y_broadcast./factor;
plot(x_broadcast,y_broadcast,'k','linewidth',2)
hold on
% plot boundary for "integrator" (many to few) neurons
% in_degree > 40, out_degree 5x in_degree
integrate_number = sum(((in_degree(n_intrinsic_filter)+out_degree(n_intrinsic_filter)) >= r) & (in_degree(n_intrinsic_filter) >= out_degree(n_intrinsic_filter)*factor))
x_integrate = r:7000;
y_integrate = x_integrate./factor;
plot(x_integrate,y_integrate,'k','linewidth',2)
prolific_number = length(in_degree(n_intrinsic_filter))-small_neuron_number-broadcast_number-integrate_number
xlabel('in degree')
ylabel('out degree')
xlim([1 7000])
ylim([1 7000])
set(gca,'FontSize',16)
set(gca,'xscale','log')
set(gca,'yscale','log')
set(gcf,'position',[100,100,600,600])
set(gcf,'renderer','Painters')
saveas(gcf,strcat(output_dir,'\in_vs_out_deg_phase'),'epsc')
saveas(gcf,strcat(output_dir,'\in_vs_out_deg_phase'),'png')
%% plot scatterplot by neurotransmitter
%% look at degree scatterplot for integrators and broadcasters
% to do: shade each region
for i = 1:6
nt_idx = strcmpi(out_nts,all_nts{i});
s = scatter(in_degree(n_intrinsic_filter & nt_idx),out_degree(n_intrinsic_filter & nt_idx),...
10,nt_colors(i,:),'filled');
hold on
end
% plot circle
r = 37; % double the reciprocal degree
circ_x = 1:0.001:r;
circ_y = (r^2 - circ_x.^2).^0.5;
plot(circ_x,circ_y,'k','linewidth',2)
hold on
% number of neurons in the circle
%small_neuron_number = sum((in_degree(n_intrinsic_filter)+out_degree(n_intrinsic_filter)) < r)
% plot boundary for "broadcast" (few to many) neurons
% out_degree > 40, out_degree 5x in_degree
%broadcast_number = sum(((in_degree(n_intrinsic_filter)+out_degree(n_intrinsic_filter)) > r) & (out_degree(n_intrinsic_filter) >= in_degree(n_intrinsic_filter)*5))
y_broadcast = r:7000;
x_broadcast = y_broadcast./factor;
plot(x_broadcast,y_broadcast,'k','linewidth',2)
hold on
% plot boundary for "integrator" (many to few) neurons
% in_degree > 40, out_degree 5x in_degree
%integrate_number = sum(((in_degree(n_intrinsic_filter)+out_degree(n_intrinsic_filter)) > r) & (in_degree(n_intrinsic_filter) >= out_degree(n_intrinsic_filter)*5))
x_integrate = r:7000;
y_integrate = x_integrate./factor;
plot(x_integrate,y_integrate,'k','linewidth',2)
%prolific_number = length(in_degree(n_intrinsic_filter))-small_neuron_number-broadcast_number-integrate_number
xlabel('in degree')
ylabel('out degree')
legend(all_nts,'location','northwest')
xlim([1 7000])
ylim([1 7000])
set(gca,'FontSize',16)
set(gca,'xscale','log')
set(gca,'yscale','log')
set(gcf,'position',[100,100,600,600])