-
Notifications
You must be signed in to change notification settings - Fork 0
/
Plot_DotComps2_sna.m
377 lines (306 loc) · 10.5 KB
/
Plot_DotComps2_sna.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
%% Plot_DotComps2_sna.m %%
%
% Analyzing New Shadow data
%
%
% Alistair Boettiger Date Begun: 03/05/10
% Levine Lab Functional Since: 03/06/10
% Last Modified: 01/12/11
%% Description
% comparison
%
%
%% Updates
% Revised 01/12/11 to use most recent formulation of age structure and
% plotting tools.
% Really should make this into a function
%% Source Code
clear all;
folder = '/Volumes/Data/Lab Data/Shadow_data/Processed';
emb_roots = {'MP05_29C_y_sna'; % 1
'MP05_22C_y_sna'; % 2
'MP06xYW_30C_y_sna'; % 3
'MP06xYW_22C_y_sna'; % 4
'MP10_29C_y_sna'; % 5
'MP10_22C_y_sna'; % 6
'MP05xYW_30C_sna_y-full'; % 7
'MP10xYW_30C_sna_y-full'; % 8
'MP05xdl6_25C_pt1'; % 9
'MP05xdl6_25C_pt2'; % 10
'MP10xdl6_25C_pt1'; % 11
'MP10xdl6_25C_pt2'}; % 12
names = {'2 enh 22C';
'2 enh 30C';
'no primary 22C';
'no primary 30C';
'no shadow 22C';
'no shadow 30C';
'2 enh dl6';
'no primary dl6'};
N = 70;
K = length(emb_roots);
G= length(names);
miss_cnt = cell(1,K);
miss_rate = cell(1,K);
nd = cell(1,K);
lowon = cell(1,K);
for z=1:K
miss_cnt{z} = zeros(N,1);
miss_rate{z} = zeros(N,1);
lowon{z} = zeros(N,1);
nd{z} = zeros(N,1);
end
xmin = .2; xmax = .8; ymin = .25; ymax = .75;
% as fractions of the original image dimensions.
for z=1:K % k=2;
for n=1:N
if n<10
emb = ['0',num2str(n)];
else
emb = num2str(n);
end
try
load([folder,'/',emb_roots{z},emb,'_data.mat']);
% get the indices of all nuclei in green that are not also red.
% require these nuclei also fall in the 'region' for red nuclei.
% s29_miss_cnt(n) = length(intersect(setdiff(pts2,pts1), ptr_nucin2));
miss_cnt{z}(n) = length(intersect(setdiff(pts2,pts1), ptr_nucin2));
miss_rate{z}(n) = miss_cnt{z}(n)/length(pts2);
lowon{z}(n) = lowon_fxn(H,handles,nin2,ptr_nucin2,emb,0);
%lowon{z}(n) = lowon_fxn(H,handles,all_nucs,pts2,nin2,Cell_bnd);
if length(H) > 2000
im_dim = 2048;
else
im_dim = 1024;
end
lims = round([xmin,xmax,ymin,ymax]*im_dim);
nd{z}(n) = NucDensity(cent,lims,0);
catch ME
disp(ME.message);
%disp(['can not find file' folder,'/',emb_roots{z},emb,'_data.mat']);
end
end
end
% save snail_SD_011211
%%
% clear all;
load('/Users/alistair/Documents/Berkeley/Levine_Lab/Projects/Shadow Enhancers/Code_Data/snail_SD_011211.mat');
foff{1} = miss_rate{6}; Nnuc{1} = nd{6}; % 2 enh 22 C, MP10
foff{2} = [miss_rate{5},miss_rate{8}]; Nnuc{2} = [nd{5},nd{8}]; % 2 enh 30C MP10
foff{3} = miss_rate{2}; Nnuc{3} = nd{2}; % MP05 22C
foff{4} = [miss_rate{1}; miss_rate{7}]; Nnuc{4} = [nd{1}, nd{7}]; % MP05 30C
foff{5} = miss_rate{4}; Nnuc{5} = nd{4}; % MP06 22C
foff{6} = miss_rate{3}; Nnuc{6} = nd{3}; % MP06 30C
foff{7} = [miss_rate{11},miss_rate{12}]; Nnuc{7} = [nd{11}, nd{12}];% MP10 dl6 25C
foff{8} = [miss_rate{9},miss_rate{10}]; Nnuc{8} = [nd{9},nd{10}]; % MP05 dl6 25C
for k=1:G
data = nonzeros(foff{k});
foff{k} = [data; zeros(200-length(data),1)];
data = nonzeros(Nnuc{k});
Nnuc{k} = [data; zeros(200-length(data),1)];
end
names = {'2 enh 22C';
'2 enh 30C';
'no primary 22C';
'no primary 30C';
'no shadow 22C';
'no shadow 30C';
'2 enh dl6';
'no primary dl6'};
%
%
% emb_roots = {'MP05_29C_y_sna'; % 1
% 'MP05_22C_y_sna'; % 2
% 'MP06xYW_30C_y_sna'; % 3
% 'MP06xYW_22C_y_sna'; % 4
% 'MP10_29C_y_sna'; % 5
% 'MP10_22C_y_sna'; % 6
% 'MP05xYW_30C_sna_y-full'; % 7
% 'MP10xYW_30C_sna_y-full'; % 8
% 'MP05xdl6_25C_pt1'; % 9
% 'MP05xdl6_25C_pt2'; % 10
% 'MP10xdl6_25C_pt1'; % 11
% 'MP10xdl6_25C_pt2'}; % 12
%%
ND = cell2mat(Nnuc);
age_offset = 5.3;
emb_cycle = age_offset + log2( nonzeros( sort(ND(:)) ) );
figure(2); clf; plot( emb_cycle ,'r.');
title(['sna embryos, N = ',num2str(length(nonzeros(ND(:))) ) ],'FontSize',15);
set(gca,'FontSize',15); grid on;
set(gcf,'color','w'); ylabel('log_2(nuc density)'); xlabel('embryo number');
ylim([10,14.99]);
%%
G= length(names);
cc14 =cell(1,G); cc13 = cell(1,G); cc12 = cell(1,G); cc11 = cell(1,G); cc10 = cell(1,G); cc9 = cell(1,G);
for z=1:G
logage = age_offset + log2( ND(:,z) );
cc14{z} = logage >14;
cc13{z} = logage <14 & logage> 13;
cc12{z} = logage <13 & logage > 12;
cc11{z} = logage <12 & logage > 0 ;
foff{z}(foff{z}==Inf) = 0;
end
%% Plot Fraction of missing nuclei distributions
xlab = 'fraction of missed nuclei';
names = {'2 enh 22C';
'2 enh 30C';
'no primary 22C';
'no primary 30C';
'no shadow 22C';
'no shadow 30C';
'2 enh dl6';
'no primary dl6'};
plot_miss = cell(1,G);
for k=1:G; plot_miss{k} = foff{k}(cc14{k}); end
%for k=1:G; plot_miss{k} = miss_rate{k}; end
figure(1); clf;
colordef black; set(gcf,'color','k');
%colordef white; set(gcf,'color','w');
x = linspace(0,1,30); % range and number of bins for histogram
xx = linspace(0,1,100); % range a number of bins for interpolated distribution
method = 'pcubic'; % method for interpolation
sigma = .1; % smoothing factor for interpolation
subplot(2,1,1);
CompDist(plot_miss([1,3,5]),x,xx,method,sigma,names([1,3,5]),xlab,12)
subplot(2,1,2);
sigma = .1; x = linspace(0,1,20);
CompDist(plot_miss([2,4,6]),x,xx,method,sigma,names([2,4,6]),xlab,12)
labs = {'30C','22C'};
figure(30); clf;
BoxDist(plot_miss,names,'fraction missed',labs);
xlim([0,1]);
%%
%% expression
F = 14;
xlab = 'missed expression';
plot_miss = cell(1,G);
for k=1:G; plot_miss{k} = foff{k}(cc14{k}); end
data = plot_miss;
Ts = length(data);% number of tracks
pW = zeros(Ts);
pA = zeros(Ts);
for i=1:Ts
for j = 1:Ts
pW(i,j) = ranksum(data{i},data{j}); % Wilcox Rank Sum
pA(i,j)=anovan([data{i}',data{j}'],{[zeros(1,length(data{i})),ones(1,length(data{j}))]},'display','off'); % 2-way ANOVA
end
end
Wpvals = [' p_{24} = ',num2str(pW(2,4),2) , ' p_{26} = ',num2str(pW(2,6),2) , ' p_{78} = ',num2str(pW(7,8),2) ];
figure(4); clf;
cumhist(data,names,xlab,F);
title(['pairwise Wilcoxon: ' Wpvals]);
set(gcf,'color','w');
disp([names{1},': ' ,num2str(median([data{1}])),'+/-',num2str(std([data{1}])), ' missed']);
disp([names{2},': ' ,num2str(median([data{2}])),'+/-',num2str(std([data{2}])), ' missed']);
disp([names{3},': ' ,num2str(median([data{3}])),'+/-',num2str(std([data{3}])), ' missed']);
figure(3); clf;
cumhist(data([1:6]),names([1:6]),xlab,F);
title(['pairwise Wilcoxon: ' [' p_{24} = ',num2str(pW(2,4),2) , ' p_{26} = ',num2str(pW(2,6),2) ];]);
set(gcf,'color','w');
figure(5); clf;
cumhist(data([1,3,5]),names([1,3,5]),xlab,F);
title(['pairwise Wilcoxon: ' [' p_{12} = ',num2str(pW(1,3),2) , ' p_{13} = ',num2str(pW(1,5),2) , ' p_{23} = ',num2str(pW(3,5),2) ];]);
set(gcf,'color','w');
figure(6); clf;
cumhist(data([2,4,6]),names([2,4,6]),xlab,F);
title(['pairwise Wilcoxon: ' [' p_{12} = ',num2str(pW(2,4),2) , ' p_{13} = ',num2str(pW(2,6),2) , ' p_{23} = ',num2str(pW(4,6),2) ];]);
set(gcf,'color','w');
figure(7); clf;
cumhist(data([1,3,7,8]),names([1,3,7,8]),xlab,F);
title(['pairwise Wilcoxon: ' [' p_{13} = ',num2str(pW(2,7),2) , ' p_{24} = ',num2str(pW(3,8),2) ];]);
set(gcf,'color','w');
%% Compare to bionmial
N = 700; % estimate of number of cells
for k=1:G
mu(k) = mean(plot_miss{k});
sig(k) = std(plot_miss{k});
bisig(k) = sqrt( mu(k)*N*(1-mu(k)) )/N;
end
figure(3); clf;
scatter(sig,bisig);
% %% Plot Fraction of missing nuclei distributions
%
% xlab = 'fraction of missed nuclei';
%
%
% plot_miss = cell(1,G);
% for k=1:G; plot_miss{k} = miss_rate{k}(cc14{k}); end
%
% figure(1); clf;
% % colordef black; set(gcf,'color','k');
% colordef white; set(gcf,'color','w');
%
% % x = linspace(0,1,8); % range and number of bins for histogram
% % xx = linspace(0,1,100); % range a number of bins for interpolated distribution
% % method = 'pcubic'; % method for interpolation
% % sigma = .1; % smoothing factor for interpolation
% % CompDist(plot_miss,x,xx,method,sigma,names,xlab)
%
% BoxDist(plot_miss,names,xlab);
% set(gcf,'color','k');
%
% V= plot_miss;
% P_var = zeros(G,G);
% for i=1:G
% for j=1:G
% P_var(i,j) = log10(ranksum(V{i},V{j}));
% end
% end
%
% figure(6); clf; imagesc(P_var); colorbar; colormap('gray');
% set(gca,'YtickLabel', str2mat(names{:}),'YTick',1:6,'fontsize',15,...
% 'YMinorTick','on'); title(xlab);
% set(gcf,'color','k');
% %% Plot Total mRNA variability distribuitons
%
% xlab = 'variability in total transcript (\sigma/\mu)';
%
%
% plot_lowon = cell(1,G);
% for k=1:G; plot_lowon{k} = lowon{k}(cc14{k}); end
% figure(2); clf;
% colordef black; set(gcf,'color','k');
% %colordef white; set(gcf,'color','w');
%
%
% % x = linspace(0,1,20);
% % xx = linspace(0,1,100);
% % method = 'pcubic';
% % sigma = .1;
% % CompDist(plot_lowon,x,xx,method,sigma,names,xlab)
%
% BoxDist(plot_lowon,names,xlab);
% set(gcf,'color','k');
%
% V= plot_lowon;
% P_var = zeros(G,G);
% for i=1:G
% for j=1:G
% P_var(i,j) = log10(ranksum(V{i},V{j}));
% end
% end
%
% figure(6); clf; imagesc(P_var); colorbar; colormap('gray');
% set(gca,'YtickLabel', str2mat(names{:}),'YTick',1:6,'fontsize',15,...
% 'YMinorTick','on'); title(xlab);
% set(gcf,'color','k');
%
% %% Variability between immidiate neighbors
%
% xlab = 'variability in total transcript among neighbors \sigma/mu';
% % x = linspace(0,1,33);
% % xx = linspace(0,1,100);
% % method = 'pcubic';
% % sigma = .1;
%
% plot_cell_var = cell(1,G);
% for k=1:G; plot_cell_var{k} = cell_var{k}(cc14{k}); end
% plot_cell_var{4} = [plot_cell_var{4}; cell_var{7}(cc14{7})];
%
% figure(3); clf;
% % colordef black; set(gcf,'color','k');
% colordef white; set(gcf,'color','w');
% BoxDist(plot_cell_var,names,xlab);
% set(gcf,'color','k');
% % CompDist(plot_cell_var,x,xx,method,sigma,names,xlab);