-
Notifications
You must be signed in to change notification settings - Fork 0
/
GCNs_14oct2013.m
606 lines (563 loc) · 28.7 KB
/
GCNs_14oct2013.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
%%% 14 Oct. 2013
%%% A pipeline to generate Genomic Connectivity Networks (GCNs) based on the
%%% BrainSpan Atlas (developing human brain atlas)
%% define some variables
atlasVersion = 3; %either 3 or 10
dataDirectory = ['C:/Users/amahfouz/Documents/MATLAB/Data/GenomicConnectivityNetworks/files/genes_matrix_csv_genCodeV' ...
num2str(atlasVersion) '/'];
expThresh = 5;% define the expression threshold (RPKM)
varThresh = 1;% define the variance threshold
corrType = 'Spearman';% define the correlation type
% define the file with the gene lists (ASD,SZ,ND)
geneListFile = 'C:/Users/amahfouz/Documents/MATLAB/Data/GenomicConnectivityNetworks/GeneLists.xls';
% % ids of donors with no missing data (based on the selected 16 structures)
% completeDonor = [12837,12365,12288,12296,12890,12297,12830,12979,12298,12841,...
% 12831,12832,13057,12300,12290,12302,12303,12291];
% extract only the 16 structures of interest
structuresToInclude = {'AMY','HIP','STR','MD','CBC','DFC','VFC','MFC','OFC',...
'IPC','STC','ITC','A1C','M1C','S1C','V1C'};
% extract donors with enough samples
if atlasVersion == 3
donorsToInclude = {'H376.IIIB.50','H376.IIIB.51','H376.IIIB.52','H376.IIIB.53',...
'H376.IV.50','H376.IV.51','H376.IV.53','H376.IV.54','H376.IX.50','H376.IX.51',...
'H376.IX.52','H376.VI.50','H376.VI.52','H376.VII.50','H376.VIII.50',...
'H376.VIII.51','H376.VIII.52','H376.VIII.53','H376.VIII.54','H376.X.50',...
'H376.X.51','H376.X.52','H376.X.53','H376.XI.60','H376.XI.50','H376.XI.52','H376.XI.53',...
'H376.XI.54','H376.XI.55','H376.XI.56'};
devPeriods = {[1, 2, 3 ,4], [5, 6, 7, 8], [9, 10, 11], [12, 13, 14, 15], ...
[16, 17, 18, 19, 20], [21, 22, 23, 24], [25, 26, 27, 28, 29, 30]};
elseif atlasVersion == 10
donorsToInclude = {'H376.IIIB.50','H376.IIIB.51','H376.IIIB.52','H376.IIIB.53',...
'H376.IV.53','H376.IV.54','H376.IV.50','H376.V.53',...
'H376.VI.50','H376.VI.52','H376.VIII.51',...
'H376.VIII.53','H376.VIII.54','H376.VIII.52','H376.VIII.50',...
'H376.IX.51','H376.IX.52','H376.IX.50','H376.X.51',...
'H376.X.50','H376.X.53','H376.X.52','H376.XI.60',...
'H376.XI.50','H376.XI.52','H376.XI.53','H376.XI.54','H376.XI.56'};
devPeriods = {[1, 2, 3 ,4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15], ...
[16, 17, 18, 19], [20, 21, 22, 23], [24, 25, 26, 27, 28]};
end
devPeriodsNames = {'Early 2nd Trimester', 'Late 2nd trimester', 'Infancy', ...
'Early Childhood', 'Late Childhood', 'Adolescence', 'Adulthood'};
devPeriods3 = {1:8, 9:20, 21:30};
devPeriodsNames3 = {'Prenatal', 'Childhood', 'Adulthood'};
%% Read original data from the BrainSpan Atlas (V3) and arrange it matlab files
arrangeBrainSpan(dataDirectory);
load([dataDirectory 'donor.mat']);
load([dataDirectory 'structure.mat']);
load([dataDirectory 'gene.mat']);
load([dataDirectory 'origExpMatrix.mat']);
%% filter the data (5 RPKM in at least one sample)
% select the donors and structures of interest
donorsSelect = find(ismember(donor.name, donorsToInclude));
structuresSelect = find(ismember(structure.acronym, structuresToInclude));
includedSamples = intersect(donorsSelect,structuresSelect);
data = origExpMatrix(:,includedSamples);
% construct a donor expression matrix
donorExpMat.expression = NaN(size(data,1),length(structuresToInclude),length(donorsToInclude));
for d = 1 : length(donorsToInclude)
% find the ids of the current donors
donorIDs = find(ismember(donor.name,donorsToInclude{d}) == 1);
% find the ids of the selected structures corresponding to the current
% donor
allDonorStructures = structure.acronym(donorIDs);
% rearrange donor structures to match the order of the structuresOfInteres
[tempSTR ia ib] = intersect(structuresToInclude,allDonorStructures);
[sortedIA sortingInd] = sort(ia);
donorStrIDs = donorIDs(ib(sortingInd));
% create a donor-specific expression matrix
donorExpMat.expression(:,sortedIA,d) = origExpMatrix(:, donorStrIDs);
% retrieve a list of ages corresponding to the list of donors
donorExpMat.ages(d) = donor.age(donorIDs(1));
clear donorIDs; clear allDonorStructures; clear tempSTR;
clear ia; clear ib; clear sortedIA; clear sortingInd; clear donorStrIDs;
end
% impute missing values
donorExpMat = customImpute(donorExpMat,devPeriods);
% remove genes that have no expression above 5 RPKM in one sample at least
data = reshape(donorExpMat.expression, size(donorExpMat.expression,1), size(donorExpMat.expression,2)*size(donorExpMat.expression,3));
[row col] = find(data >=5);
row = unique(row);
% data_max = max(data');
% expressingGenes = find(data_max ~= 0);
% figure, hist(log2(data_max(expressingGenes)), 100); grid on;
% title('Distribution of the Maximum Expression per Gene', 'FontWeight', 'bold', 'FontSize', 20)
% set(gca, 'xlim', [-15 20], 'ylim', [0 2500], 'FontWeight', 'bold', 'FontSize', 15)
% xlabel('log_2(maximum expression)', 'FontWeight', 'bold', 'FontSize', 20);
% ylabel('Count', 'FontWeight', 'bold', 'FontSize', 20)
% get the indicies of expressing genes (with no outliers) and clear
% variables
expressingGenesIDs = row;
%% filter the data (1 RPKM in 80% 0f samples)
% select the donors and structures of interest
donorsSelect = find(ismember(donor.name, donorsToInclude));
structuresSelect = find(ismember(structure.acronym, structuresToInclude));
includedSamples = intersect(donorsSelect,structuresSelect);
data = origExpMatrix(:,includedSamples);
% remove genes that have no expression at all (zero everywhere)
data_max = max(data');
expressingGenes = find(data_max ~= 0);
figure, hist(log2(data_max(expressingGenes)), 100); grid on;
title('Distribution of the Maximum Expression per Gene', 'FontWeight', 'bold', 'FontSize', 20)
set(gca, 'xlim', [-15 20], 'ylim', [0 2500], 'FontWeight', 'bold', 'FontSize', 15)
xlabel('log_2(maximum expression)', 'FontWeight', 'bold', 'FontSize', 20);
ylabel('Count', 'FontWeight', 'bold', 'FontSize', 20)
% remove outliers: genes that have an extremely high expression (>2000 RPKM)
[sortedMaxVals sortingInd] = sort(data_max);
outliers = sortingInd(sortedMaxVals > 2000);
outliersInd = find(ismember(expressingGenes,outliers));
expressingGenes_noOutliers = expressingGenes;
expressingGenes_noOutliers(outliersInd) = [];
figure, hist(log2(data_max(expressingGenes_noOutliers)), 100); grid on;
title({'Distribution of the Maximum Expression per Gene', ...
['oultiers removed (' num2str(length(outliersInd)) ' genes with maximum expression > 2000)']}, ...
'FontWeight', 'bold', 'FontSize', 20)
set(gca, 'xlim', [-15 20], 'ylim', [0 2500], 'FontWeight', 'bold', 'FontSize', 15)
xlabel('log_2(maximum expression)', 'FontWeight', 'bold', 'FontSize', 20);
ylabel('Count', 'FontWeight', 'bold', 'FontSize', 20)
% keep only genes that have expression of 1 RPKM in 80% of the samples
data_thresh = zeros(size(data));
data_thresh(data>1) = 1;
notinManySamples = find(sum(data_thresh') < (0.8*size(data,2)));
notInManySamples_ind = find(ismember(expressingGenes_noOutliers,notinManySamples));
expressingGenes_noOutliers_inManySamples = expressingGenes_noOutliers;
expressingGenes_noOutliers_inManySamples(notInManySamples_ind) = [];
figure, hist(log2(data_max(expressingGenes_noOutliers_inManySamples)), 100); grid on;
title({'Distribution of the Maximum Expression per Gene', ...
['genes with expression of 1 RPKM in 80% of samples (' num2str(length(expressingGenes_noOutliers_inManySamples)) ' genes)']}, ...
'FontWeight', 'bold', 'FontSize', 20)
set(gca, 'xlim', [-15 20], 'FontWeight', 'bold', 'FontSize', 15)
xlabel('log_2(maximum expression)', 'FontWeight', 'bold', 'FontSize', 20);
ylabel('Count', 'FontWeight', 'bold', 'FontSize', 20)
% get the indicies of expressing genes (with no outliers) and clear
% variables
expressingGenesIDs = expressingGenes_noOutliers_inManySamples;
clear donorsSelect; clear structuresSelect;
clear data; clear data_max; clear expressingGenes; clear sortedMaxVals;
clear sortingInd; clear outliers; clear outliersInd; clear expressingGenes_noOutliers;
clear data_thresh; clear notinManySamples; clear notInManySamples_ind;
clear expressingGenes_noOutliers_inManySamples;
%% Look for missing data
uniqueDonors = unique(donor.name);
dataAnalysisMat = zeros(length(uniqueDonors), length(structuresToInclude));
for d = 1 : length(uniqueDonors)
donorIDs = find(ismember(donor.name,uniqueDonors(d)) == 1);
uniqueAges(d) = donor.age(donorIDs(1));
allDonorStructures = structure.acronym(donorIDs);
[tempSTR ia ib] = intersect(structuresToInclude,allDonorStructures);
dataAnalysisMat(d,ia) = 1;
end
%% analyze the expression distribution
dataArr = reshape(origExpMatrix,1,size(origExpMatrix,1)*size(origExpMatrix,2));
dataArr(dataArr == 0) =[];
figure, hist(log2(dataArr), 100), grid on
title('Gene Expression Distribution', 'FontWeight', 'bold', 'FontSize', 20)
set(gca, 'xlim', [-20 20], 'FontWeight', 'bold', 'FontSize', 15)
xlabel('log_2(expression)', 'FontWeight', 'bold', 'FontSize', 20);
ylabel('Count', 'FontWeight', 'bold', 'FontSize', 20)
%% analyze the variance of genes across time
% remove the genes with no expression data (zeros every where)
[Mask1, filteredData] = genevarfilter(origExpMatrix, 'Percentile', 0);
figure, hist(log2(var(filteredData')), 100), grid on
title('Gene Variance Distribution', 'FontWeight', 'bold', 'FontSize', 20)
set(gca, 'xlim', [-30 30], 'FontWeight', 'bold', 'FontSize', 15)
xlabel('log_2(variance)', 'FontWeight', 'bold', 'FontSize', 15);
ylabel('Count', 'FontWeight', 'bold', 'FontSize', 20)
% analyze the variance of genes with an expression value of at least 'expThresh'
[expressingGenesIDs ~] = ind2sub(size(origExpMatrix), find(origExpMatrix > expThresh));
expressingGenesIDs = unique(expressingGenesIDs);
[Mask_exp, FData_exp] = genevarfilter(origExpMatrix(expressingGenesIDs,:), 'Percentile', 0);
figure, hist(log2(var(FData_exp')), 100), grid on
title(['Gene Variance Distribution of genes with expression > ' num2str(expThresh)], 'FontWeight', 'bold', 'FontSize', 20)
xlabel('log_2(variance)', 'FontWeight', 'bold', 'FontSize', 15);
ylabel('Count', 'FontWeight', 'bold', 'FontSize', 20)
% remove genes with variance < 1
[Mask_var, FData_var] = genevarfilter(origExpMatrix, 'AbsValue', 1);
figure, hist(log2(var(FData_var')), 100), grid on
title('Gene Variance Distribution of genes with variance > 1', 'FontWeight', 'bold', 'FontSize', 20)
xlabel('log_2(variance)', 'FontWeight', 'bold', 'FontSize', 15);
ylabel('Count', 'FontWeight', 'bold', 'FontSize', 20)
%% Filter genes with low variance
% if geneSet is 'All'
% [maskVar filteredExpMat] = genevarfilter(origExpMatrix, 'AbsValue', 1);
% [maskVar filteredExpMat] = genevarfilter(origExpMatrix, 'Percentile', 0);
% expressingGenesIDs = find(maskVar == 1);
%% Normalize across genes (rows)
origExpMatrix = (origExpMatrix - repmat(nanmean(origExpMatrix')', 1, size(origExpMatrix,2))) ./ repmat(nanstd(origExpMatrix')', 1, size(origExpMatrix,2));
%% Analyze a network of random genes
randomGenesNumber = 380;
[donorGCN_rand devPeriodGCN_rand strPairs donorExpMat_rand] = gcnPerDonor_random(...
donor,structure,gene,origExpMatrix, expressingGenesIDs, ...
structuresToInclude,donorsToInclude, ...
corrType,devPeriods,devPeriodsNames,randomGenesNumber);
%% Analyze one region across time
geneSet = 'All';% define the set of genes (All, ASD, SZ, ND)
[donorGCN_All donorExpMat_All] = gcnPerRegion( ...
donor,structure,gene,origExpMatrix, expressingGenesIDs, ...
structuresToInclude,donorsToInclude, ...
geneSet,geneListFile,corrType,devPeriods,devPeriodsNames);
%% Analyze one gene across region pairs
geneSet = 'All';% define the set of genes (All, ASD, SZ, ND)
[geneGCN] = gcnPerGene( ...
donor,structure,gene,origExpMatrix, expressingGenesIDs, ...
structuresToInclude,donorsToInclude, ...
geneSet,geneListFile,corrType,devPeriods,devPeriodsNames);
%% create genomic connectivity matrix per donor for the list of all genes and asd gene lists
geneSet = 'All';% define the set of genes (All, ASD, SZ, ND)
[donorGCN_All_sq donorGCN_All devPeriodGCN_All strPairs donorExpMat_All] = gcnPerDonor( ...
donor,structure,gene,origExpMatrix, expressingGenesIDs, ...
structuresToInclude,donorsToInclude, ...
geneSet,geneListFile,corrType,devPeriods,devPeriodsNames);
geneSet = 'ASD';% define the set of genes (All, ASD, SZ, ND)
[donorGCN_ASD_sq donorGCN_ASD devPeriodGCN_ASD strPairs donorExpMat_ASD] = gcnPerDonor( ...
donor,structure,gene,origExpMatrix, expressingGenesIDs, ...
structuresToInclude,donorsToInclude, ...
geneSet,geneListFile,corrType,devPeriods3,devPeriodsNames3);
%% SPIE FIGURE
ageStage1_expMat = mean(donorExpMat_All.expression(:,:,1:4),3);
ageStage2_expMat = mean(donorExpMat_All.expression(:,:,5:8),3);
ageStage7_expMat = mean(donorExpMat_All.expression(:,:,25:end),3);
figure, imagesc(zscore(ageStage1_expMat,[],2)), colormap(redbluecmap)
axis off
figure, imagesc(zscore(ageStage2_expMat,[],2)), colormap(redbluecmap)
axis off
figure, imagesc(zscore(ageStage7_expMat,[],2)), colormap(redbluecmap)
axis off
%% Analyze the significance of the correlation between region pairs
nPerm = 10000;
for p = 1 : nPerm
randomGenesNumber = 455;
[donorGCN_rand(:,:,p) devPeriodGCN_rand(:,:,p) strPairs donorExpMat_rand] = gcnPerDonor_random(...
donor,structure,gene,origExpMatrix, expressingGenesIDs, ...
structuresToInclude,donorsToInclude, ...
corrType,devPeriods3,devPeriodsNames3,randomGenesNumber);
end
%% compare CBC correlation between ASD and random
load('devPeriodGCN_rand_V3_3periods.mat');
C6 = distinguishable_colors(6);
set(0,'Units','pixels')
scnsize = get(0,'ScreenSize');
x = 100;
y = 100;
W = 1300;
H = 700;
F1 = 30;
LF = 20;
LW = 5;
for S = 1 : 5
inputStr = structuresToInclude{S};
K = strfind(strPairs, inputStr);
Index = find(not(cellfun('isempty', K)));
f1 = figure,
% set(f1, 'Position', [x y W H])
hold on
for i = 1 : 4
currLabel = strPairs{Index(i)};
yL = strfind(currLabel, inputStr);
if yL == 1
L{i} = currLabel(length(inputStr)+2:end);
else
L{i} = currLabel(1:strfind(currLabel, '_')-1);
end
cInd = find(ismember(structuresToInclude(1:5),L{i}) == 1);
H(i) = plot(devPeriodGCN_ASD(Index(i),:)', 'LineWidth', LW, 'Color', C6(cInd,:))
errorbar(mean(devPeriodGCN_rand(Index(i),:,:),3),std(devPeriodGCN_rand(Index(i),:,:),[],3),...
'LineWidth', LW, 'LineStyle', '--', 'Color', C6(cInd,:));
grid on;
end
H(i+1) = errorbar(mean(devPeriodGCN_ASD(Index(5:end),:)),...
std(devPeriodGCN_ASD(Index(i+1:end),:)),...
'LineWidth', LW, 'Color', C6(end,:))
ncxRand_mean = mean(devPeriodGCN_rand(Index(i+1:end),:,:),1);
ncxRand_std = std(devPeriodGCN_rand(Index(i+1:end),:,:),[],1);
errorbar(mean(ncxRand_mean,3),std(ncxRand_std,[],3),...
'LineWidth', LW, 'LineStyle', '--', 'Color', C6(end,:));
L{i+1} = 'NCX';
set(gca,'XTickLabels',devPeriodsNames3,'XTick',[1:length(devPeriods3)],...
'xlim',[0 length(devPeriods3)+1],'FontSize',F1,'FontWeight','bold')
ylim([0.5 1]);
ylabel('Correlation','FontSize',F1,'FontWeight','bold');
legend(H,L,'FontWeight','bold','FontSize',LF)
hold off
set(gcf,'NextPlot','add');
axes;
h = title(inputStr, 'FontWeight', 'bold', 'FontSize', F1);
set(gca,'Visible','off');
set(h,'Visible','on');
end
%% compare CBC correlation between ASD and random
% load('devPeriodGCN_rand.mat');
for S = 1 : 5
inputStr = structuresToInclude{S};
K = strfind(strPairs, inputStr);
Index = find(not(cellfun('isempty', K)));
figure,
for i = 1 : length(Index)
subplot(3,5,i),hold on
plot(devPeriodGCN_ASD(Index(i),:)', 'LineWidth', 2, 'Color', 'r')
errorbar(mean(devPeriodGCN_rand(Index(i),:,:),3),std(devPeriodGCN_rand(Index(i),:,:),[],3),...
'LineWidth', 2);
grid on;
currLabel = strPairs{Index(i)};
yL = strfind(currLabel, inputStr);
if yL == 1
ylabel(currLabel(length(inputStr)+2:end), 'FontWeight', 'bold');
else
ylabel(currLabel(1:3), 'FontWeight', 'bold');
end
set(gca,'XTickLabels',devPeriods3,'XTick',[1:length(devPeriods3)],'xlim',[0 length(devPeriods3)+1])
ylim([0.5 1]);
hold off
end
set(gcf,'NextPlot','add');
axes;
h = title(inputStr, 'FontWeight', 'bold', 'FontSize', 20);
set(gca,'Visible','off');
set(h,'Visible','on');
end
%% analyze the difference between the 'All' and 'ASD' networks (donors)
% plot the structural correlations of All and ASD
figure, hold on
subplot(1,2,1), imagesc(donorGCN_All), colormap('redbluecmap')
title('Correlation Change All', 'FontWeight', 'bold', 'FontSize', 15)
set(gca, 'XTickLabels', donorExpMat_All.ages, 'XTick', [1:length(donorsToInclude)], 'FontWeight', 'bold', 'FontSize', 10)
rotateXLabels(gca(), 45 );
set(gca, 'YTickLabels', strPairs, 'YTick', [1:length(strPairs)], 'FontWeight', 'bold', 'FontSize', 5)
subplot(1,2,2), imagesc(donorGCN_ASD), colormap('redbluecmap')
title('Correlation Change ASD', 'FontWeight', 'bold', 'FontSize', 15)
set(gca, 'XTickLabels', donorExpMat_All.ages, 'XTick', [1:length(donorsToInclude)], 'FontWeight', 'bold', 'FontSize', 10)
rotateXLabels(gca(), 45 );
set(gca, 'YTickLabels', strPairs, 'YTick', [1:length(strPairs)], 'FontWeight', 'bold', 'FontSize', 5)
hold off
% plot the difference in structural correlations between All and ASD
figure, imagesc(donorGCN_All-donorGCN_ASD), colormap('redbluecmap')
title('Differences in Structural Correlations between All and ASD', 'FontWeight', 'bold', 'FontSize', 15)
set(gca, 'XTickLabels', donorExpMat_All.ages, 'XTick', [1:length(donorExpMat_All.ages)], 'FontWeight', 'bold', 'FontSize', 20)
rotateXLabels(gca(), 45 );
set(gca, 'YTickLabels', strPairs, 'YTick', [1:length(strPairs)], 'FontWeight', 'bold', 'FontSize', 5)
%% analyze the difference between the 'All' and 'ASD' networks (developmental periods)
% plot the structural correlations of All and ASD
figure, hold on
subplot(1,2,1), imagesc(devPeriodGCN_All), colormap('redbluecmap')
title('Correlation Change All', 'FontWeight', 'bold', 'FontSize', 15)
set(gca, 'XTickLabels', devPeriodsNames, 'XTick', [1:length(devPeriodsNames)], 'FontWeight', 'bold', 'FontSize', 10)
rotateXLabels(gca(), 45 );
set(gca, 'YTickLabels', strPairs, 'YTick', [1:length(strPairs)], 'FontWeight', 'bold', 'FontSize', 5)
xlabel('Developmental Periods', 'FontWeight', 'bold', 'FontSize', 15);
ylabel('Region Pairs', 'FontWeight', 'bold', 'FontSize', 15);
subplot(1,2,2), imagesc(devPeriodGCN_ASD), colormap('redbluecmap')
title('Correlation Change ASD', 'FontWeight', 'bold', 'FontSize', 15)
set(gca, 'XTickLabels', devPeriodsNames, 'XTick', [1:length(devPeriodsNames)], 'FontWeight', 'bold', 'FontSize', 10)
rotateXLabels(gca(), 45 );
set(gca, 'YTickLabels', strPairs, 'YTick', [1:length(strPairs)], 'FontWeight', 'bold', 'FontSize', 5)
xlabel('Developmental Periods', 'FontWeight', 'bold', 'FontSize', 15);
ylabel('Region Pairs', 'FontWeight', 'bold', 'FontSize', 15);
hold off
% plot the difference in structural correlations between All and ASD
figure, imagesc(devPeriodGCN_All-devPeriodGCN_ASD), colormap('redbluecmap')
title('Differences in Structural Correlations between All and ASD', 'FontWeight', 'bold', 'FontSize', 15)
set(gca, 'XTickLabels', devPeriodsNames, 'XTick', [1:length(devPeriodsNames)], 'FontWeight', 'bold', 'FontSize', 20)
set(gca, 'YTickLabels', strPairs, 'YTick', [1:length(strPairs)], 'FontWeight', 'bold', 'FontSize', 5)
xlabel('Developmental Periods', 'FontWeight', 'bold', 'FontSize', 15);
ylabel('Region Pairs', 'FontWeight', 'bold', 'FontSize', 15);
%% cluster the correlation changes over time
cT = 0.4; % ASD = 0.45, All = 0.35
linkType = 'average'; % linkage type
regPairClusters = clustCorr(donorGCN_All, donorExpMat_All, strPairs, cT, linkType);
%% cluster the correlation changes over time
cT = 0.35; % ASD = 0.45, All = 0.35
linkType = 'average'; % linkage type
regPairClusters = clustCorr(donorGCN_All, donorExpMat_All, strPairs, cT, linkType);
%% cluster the differences in corr change between All and ASD
cT = 0.15; % cutoff threshold
linkType = 'average'; % linkage type
clustCorr(donorGCN_All-donorGCN_ASD, donorExpMat_ASD, strPairs, cT, linkType);
%% plot the average profiles of clusters
avgProfile(regPairClusters, donorGCN_ASD, strPairs, devPeriodsNames, devPeriods);
%% create genomic connectivity matrix per donor for the list of cell-type specific genes
geneSet = 'Neurons';
[donorGCN_N devPeriodGCN_N strPairs donorExpMat_N] = gcnPerDonor( ...
donor,structure,gene,origExpMatrix, expressingGenesIDs, ...
structuresToInclude,donorsToInclude, ...
geneSet,geneListFile,corrType,devPeriods,devPeriodsNames);
geneSet = 'Oligodendrocytes';
[donorGCN_O devPeriodGCN_O strPairs donorExpMat_O] = gcnPerDonor( ...
donor,structure,gene,origExpMatrix, expressingGenesIDs, ...
structuresToInclude,donorsToInclude, ...
geneSet,geneListFile,corrType,devPeriods,devPeriodsNames);
geneSet = 'Astrocytes';
[donorGCN_A devPeriodGCN_A strPairs donorExpMat_A] = gcnPerDonor( ...
donor,structure,gene,origExpMatrix, expressingGenesIDs, ...
structuresToInclude,donorsToInclude, ...
geneSet,geneListFile,corrType,devPeriods,devPeriodsNames);
%% Analyze the difeerences between Neurons/Oligodendrocytes and Astrocytes networks
% plot the structural correlations of N/O/A
figure, hold on
subplot(1,3,1), imagesc(donorGCN_A), colormap('redbluecmap')
title('Correlation Change Neurons', 'FontWeight', 'bold', 'FontSize', 15)
set(gca, 'XTickLabels', donorExpMat_A.ages, 'XTick', [1:length(donorsToInclude)], 'FontWeight', 'bold', 'FontSize', 10)
rotateXLabels(gca(), 45 );
set(gca, 'YTickLabels', strPairs, 'YTick', [1:length(strPairs)], 'FontWeight', 'bold', 'FontSize', 5)
subplot(1,3,2), imagesc(donorGCN_O), colormap('redbluecmap')
title('Correlation Change Oligodendrocytes', 'FontWeight', 'bold', 'FontSize', 15)
set(gca, 'XTickLabels', donorExpMat_O.ages, 'XTick', [1:length(donorsToInclude)], 'FontWeight', 'bold', 'FontSize', 10)
rotateXLabels(gca(), 45 );
set(gca, 'YTickLabels', strPairs, 'YTick', [1:length(strPairs)], 'FontWeight', 'bold', 'FontSize', 5)
subplot(1,3,3), imagesc(donorGCN_A), colormap('redbluecmap')
title('Correlation Change Astrocytes', 'FontWeight', 'bold', 'FontSize', 15)
set(gca, 'XTickLabels', donorExpMat_A.ages, 'XTick', [1:length(donorsToInclude)], 'FontWeight', 'bold', 'FontSize', 10)
rotateXLabels(gca(), 45 );
set(gca, 'YTickLabels', strPairs, 'YTick', [1:length(strPairs)], 'FontWeight', 'bold', 'FontSize', 5)
hold off
%% Calculate network topological measures for each donor's GCN
% add the path of the BCT toolbox
bctToolBox = 'C:/Users/amahfouz/Documents/MATLAB/Libraries/2012-12-04 BCT';
bgl = 'C:/Users/amahfouz/Documents/MATLAB/Libraries/matlab_bgl';
addpath(bctToolBox, bgl);
% define the output directory
resultsDirectory = 'C:/Users/amahfouz/Documents/MATLAB/Results/GenomicConnectivityNetworks/DonorsGCNs_NetMeasures/';
% define the output file
geneSet = 'ASD';
oF = [resultsDirectory 'measuresMatrix' geneSet '.xlsx'];
% call the gcnMeasure function
gcnMeasures(dataDirectory,resultsDirectory,donorsToInclude,donorGCN_ASD_sq,oF);
%% create genomic connectivity matrix per donor for the list of all genes and asd gene lists
% for big 6 structures and ncx separately
geneSet = 'All';% define the set of genes (All, ASD, SZ, ND)
[donorGCN_big6 devPeriodGCN_big6 donorGCN_ncx devPeriodGCN_ncx strPairs_big6 strPairs_ncx donorExpMat] = gcnPerDonor_2networks( ...
donor,structure,gene,origExpMatrix, expressingGenesIDs, ...
structuresToInclude,donorsToInclude, ...
geneSet,geneListFile,corrType,devPeriods,devPeriodsNames);
geneSet = 'ASD';% define the set of genes (All, ASD, SZ, ND)
[donorGCN_big6 devPeriodGCN_big6 donorGCN_ncx devPeriodGCN_ncx strPairs_big6 strPairs_ncx donorExpMat] = gcnPerDonor_2networks( ...
donor,structure,gene,origExpMatrix, expressingGenesIDs, ...
structuresToInclude,donorsToInclude, ...
geneSet,geneListFile,corrType,devPeriods,devPeriodsNames);
%% cluster the correlations for big6 and ncx
cT = 0.5; % ASD = 0.45, All = 0.35
linkType = 'average'; % linkage type
regPairClusters = clustCorr(donorGCN_big6, donorExpMat, strPairs_big6, cT, linkType);
%% prepare the network measures figures for SPIE 2014
measuresFile = 'C:\Users\amahfouz\Documents\MATLAB\Data\GenomicConnectivityNetworks\analysis_updated.xlsx';
measures_ALL = xlsread(measuresFile,2,'B2:AE14');
measures_ASD = xlsread(measuresFile,2,'B17:AE29');
[num measures] = xlsread(measuresFile,2,'A2:A14');
clear num;
for P = 1 : length(devPeriods3)
currDonors = devPeriods3{P};
meanMeasures_ALL(:,P) = mean(measures_ALL(:,currDonors)');
standardError_ALL(:,P) = (std(measures_ALL(:,currDonors)')) / sqrt(length(currDonors));
meanMeasures_ASD(:,P) = mean(measures_ASD(:,currDonors)');
standardError_ASD(:,P) = (std(measures_ASD(:,currDonors)')) / sqrt(length(currDonors));
[h,p] = ttest(measures_ALL(:,currDonors)',measures_ASD(:,currDonors)',0.05,'both');
signTest(:,P) = p';
end
%% plot Assortativity (SPIE poster)
set(0,'Units','pixels')
scnsize = get(0,'ScreenSize');
x = 100;
y = 100;
W = 1300;
H = 700;
F1 = 30;
LF = 20;
LW = 5;
f1 = figure,
set(f1, 'Position', [x y W H])
hold on
errorbar(meanMeasures_ALL(1,:),standardError_ALL(1,:),...
'LineWidth',LW,'Color','blue');
errorbar(meanMeasures_ASD(1,:),standardError_ASD(1,:),...
'LineWidth',LW,'Color','red');
grid on
legend({'AllGenes-GCNs', 'ASD-GCNs'},'FontWeight','bold','FontSize',LF);
ylabel({'Assortativity',''},'FontWeight','bold','FontSize',F1);
set(gca,'XTickLabels',devPeriodsNames3,'XTick',[1:length(devPeriods3)],...
'xlim',[0 length(devPeriods3)+1],'FontWeight', 'bold', 'FontSize', F1)
ylim([-0.2 0.5]);
text(2.97,0.44,'*','FontWeight','bold','FontSize',40,'Color','black')
hold off
saveas(f1, 'Assortativity.png');
% plot Modularity
f1 = figure,
set(f1, 'Position', [x y W H])
hold on
errorbar(meanMeasures_ALL(3,:),standardError_ALL(3,:),...
'LineWidth',LW,'Color','blue');
errorbar(meanMeasures_ASD(3,:),standardError_ASD(3,:),...
'LineWidth',LW,'Color','red');
grid on
legend({'AllGenes-GCNs', 'ASD-GCNs'},'FontWeight','bold','FontSize',LF);
ylabel({'Modularity',''},'FontWeight','bold','FontSize',F1);
set(gca,'XTickLabels',devPeriodsNames3,'XTick',[1:length(devPeriods3)],...
'xlim',[0 length(devPeriods3)+1],'FontWeight', 'bold', 'FontSize', F1)
ylim([0 0.2]);
hold off
saveas(f1, 'Modularity.png');
% plot Clustering Coefficient
f1 = figure,
set(f1, 'Position', [x y W H])
hold on
errorbar(meanMeasures_ALL(5,:),standardError_ALL(5,:),...
'LineWidth',LW,'Color','blue');
errorbar(meanMeasures_ASD(5,:),standardError_ASD(5,:),...
'LineWidth',LW,'Color','red');
grid on
legend({'AllGenes-GCNs', 'ASD-GCNs'},'FontWeight','bold','FontSize',LF);
ylabel({'Clustering Coefficient',''},'FontWeight','bold','FontSize',F1);
set(gca,'XTickLabels',devPeriodsNames3,'XTick',[1:length(devPeriods3)],...
'xlim',[0 length(devPeriods3)+1],'FontWeight', 'bold', 'FontSize', F1)
ylim([0.5 0.8]);
hold off
saveas(f1, 'ClusteringCoefficient.png');
% plot Path Length
f1 = figure,
set(f1, 'Position', [x y W H])
hold on
errorbar(meanMeasures_ALL(7,:),standardError_ALL(7,:),...
'LineWidth',LW,'Color','blue');
errorbar(meanMeasures_ASD(7,:),standardError_ASD(7,:),...
'LineWidth',LW,'Color','red');
grid on
legend({'AllGenes-GCNs', 'ASD-GCNs'},'FontWeight','bold','FontSize',LF);
ylabel({'Path Length',''},'FontWeight','bold','FontSize',F1);
set(gca,'XTickLabels',devPeriodsNames3,'XTick',[1:length(devPeriods3)],...
'xlim',[0 length(devPeriods3)+1],'FontWeight', 'bold', 'FontSize', F1)
ylim([1.5 2]);
text(2.97,1.84,'*','FontWeight','bold','FontSize',40,'Color','black')
hold off
saveas(f1, 'PathLength.png');
% plot Small-Worledness
f1 = figure,
set(f1, 'Position', [x y W H])
hold on
errorbar(meanMeasures_ALL(9,:),standardError_ALL(9,:),...
'LineWidth',LW,'Color','blue');
errorbar(meanMeasures_ASD(9,:),standardError_ASD(9,:),...
'LineWidth',LW,'Color','red');
grid on
legend({'AllGenes-GCNs', 'ASD-GCNs'},'FontWeight','bold','FontSize',LF);
ylabel({'Small-Worledness',''},'FontWeight','bold','FontSize',F1);
set(gca,'XTickLabels',devPeriodsNames3,'XTick',[1:length(devPeriods3)],...
'xlim',[0 length(devPeriods3)+1],'FontWeight', 'bold', 'FontSize',F1)
ylim([0.8 1.1]);
text(2.97,1.02,'*','FontWeight','bold','FontSize',40,'Color','black')
hold off
saveas(f1, 'SmallWorldness.png');
% plot Efficiency
f1 = figure,
set(f1, 'Position', [x y W H])
hold on
errorbar(meanMeasures_ALL(10,:),standardError_ALL(10,:),...
'LineWidth',LW,'Color','blue');
errorbar(meanMeasures_ASD(10,:),standardError_ASD(10,:),...
'LineWidth',LW,'Color','red');
grid on
legend({'AllGenes-GCNs', 'ASD-GCNs'},'FontWeight','bold','FontSize',LF);
ylabel({'Efficiency',''},'FontWeight','bold','FontSize',F1);
set(gca,'XTickLabels',devPeriodsNames3,'XTick',[1:length(devPeriods3)],...
'xlim',[0 length(devPeriods3)+1],'FontWeight', 'bold', 'FontSize', F1)
ylim([0.68 0.74]);
text(2.97,0.727,'*','FontWeight','bold','FontSize',40,'Color','black')
hold off
saveas(f1, 'Efficiency.png');