Commit c4163391 authored by Antonio Politi's avatar Antonio Politi

add EM data

parent 8283ebee
% HeLa-H2B-4D-24h-sum.xlsx
0.166666667 407.7144316 68.85010145
0.333333333 528.4113285 38.25902195
0.5 545.8308869 37.45184456
0.666666667 566.5263546 52.52273458
0.833333333 581.5070816 65.54394039
1 582.5058843 62.63906516
1.166666667 604.5654448 60.71830203
1.333333333 617.3827407 66.2741207
1.5 619.6808723 66.82646074
1.666666667 625.1862621 62.88596307
1.833333333 634.3958589 65.13751258
2 642.106259 69.17118087
2.166666667 656.5171993 71.65065241
2.333333333 660.0799594 71.91933172
2.5 656.2462918 77.53766849
2.666666667 655.5874139 72.25112485
2.833333333 656.7088401 69.64814133
3 664.4699204 74.3172357
3.166666667 675.2280497 71.76894453
3.333333333 678.8230408 73.92448303
3.5 684.540175 74.18811414
3.666666667 686.7455663 75.25470832
3.833333333 685.8019606 77.60541631
4 687.8487958 84.13560819
4.166666667 686.7053397 85.33575256
4.333333333 691.8175678 95.05015858
4.5 690.8676407 93.2083874
4.666666667 675.6651272 99.6046057
4.833333333 680.6653258 97.76276105
5 686.7962376 100.7288929
5.166666667 698.3352553 92.94793142
5.333333333 699.3531523 89.89040212
5.5 704.8510608 90.2644098
5.666666667 712.663784 96.68997729
5.833333333 713.7696197 97.21666425
6 709.6485889 98.66180491
6.166666667 726.8645754 81.5100884
6.333333333 697.1337 97.70396424
6.5 719.0778916 101.5471063
6.666666667 724.5033796 107.7766503
6.833333333 728.8552715 107.1235523
7 741.2041322 103.3625303
7.166666667 723.6089733 101.830181
7.333333333 729.5717198 101.3490376
7.5 741.0288466 87.5519332
7.666666667 735.4173313 98.82362049
7.833333333 735.804374 92.08571443
8 742.1082278 103.2631415
8.166666667 749.6779651 105.8888703
8.333333333 772.2092872 116.6889221
8.5 767.1836298 113.5345878
8.666666667 771.676499 122.7810422
8.833333333 774.7363934 111.6929589
9 765.5832981 105.5832699
9.166666667 788.2002017 108.7681552
9.333333333 801.3870648 101.541717
9.5 806.4704243 91.73872975
9.666666667 809.486501 89.2928061
9.833333333 804.725441 93.93698987
10 803.9762704 92.47081315
10.16666667 816.3071108 90.3767804
10.33333333 828.5606667 91.64612485
10.5 830.1950568 95.69194556
10.66666667 829.1207291 97.64948323
10.83333333 832.5410124 93.89990516
11 836.5115798 94.55123807
11.16666667 838.6542803 91.10523398
11.33333333 844.6244084 92.40885311
11.5 848.1822925 95.13916851
11.66666667 849.7889162 93.8101261
11.83333333 857.2343264 99.79408381
12 848.8323435 95.79405019
12.16666667 888.8665893 78.98697011
12.33333333 881.9794527 70.16420906
12.5 888.7881254 69.81761684
12.66666667 892.8860785 66.26757948
12.83333333 893.481709 65.05242681
13 898.2263393 61.76823175
13.16666667 898.9409388 64.80344582
13.33333333 906.6585017 64.47106199
13.5 909.2013434 62.99799924
13.66666667 908.7496716 63.19854914
13.83333333 910.7796349 68.21651583
14 913.1220926 72.06217686
14.16666667 918.9025253 67.92030505
14.33333333 923.1186273 64.06706783
14.5 929.5855477 60.42886332
14.66666667 932.0538751 68.46223329
14.83333333 946.7439048 62.08649691
15 937.2746433 57.11402787
15.16666667 941.3275375 67.23009102
15.33333333 946.4619229 60.61670292
15.5 946.8497839 54.77022402
15.66666667 953.7501052 53.80442957
15.83333333 954.6076405 54.87660513
16 971.8928978 44.41959715
16.16666667 969.5251684 43.39848963
16.33333333 964.6742582 40.19194121
16.5 963.4836261 39.30978362
16.66666667 966.5766002 49.98893935
16.83333333 993.8755697 35.01785295
17 997.5395703 38.42017762
HeLa4D.xls contains the analyzed innercore and non-core intensities for dividing cells
2xZFN mEGFP-Nup107 26, 31
and
CRISPR mEGFP-Nup358/RanBP2 118
from file HeLaNup358-2h-background-individually-average-intensity-forAntonio.xls
from file HeLaNup107-2h-160121-background-individual-average-intensity-forAntonio.xls
data acquired by Shotaro Otsuka
HeLa4D.xls contains the analyzed innercore and non-core intensities for dividing cells
2xZFN mEGFP-Nup107 26, 31
and
CRISPR mEGFP-Nup358/RanBP2 118
from file HeLaNup358-2h-background-individually-average-intensity-forAntonio.xls
from file HeLaNup107-2h-160121-background-individual-average-intensity-forAntonio.xls
data acquired by Shotaro Otsuka
Pom121_siRNA.xls contains the analyzed innercore and non-core intensities for dividing cells
CRISPR mEGFP-Nup358/RanBP2 118
data acquired by Joe Padget
Pom121 has been knocked-down for 48h (?)
Pom121_siRNA.xls contains the analyzed innercore and non-core intensities for dividing cells
CRISPR mEGFP-Nup358/RanBP2 118
data acquired by Joe Padget
Pom121 has been knocked-down for 48h (?)
function [ threshold ] = Otsu_2D(image)
[xSize, ySize] = size(image);
maxVal = max(max(image));
histN = round(maxVal)+1;
histogram = zeros(1,histN);
threshold=0;
w0=0.0;
w1=0.0;
m0=0.0;
m1=0.0;
N=0;
p=0;
sum=0.0;
mean=0.0;
var_bet_class=0.0;
var_max=0.0;
mu_k=0.0;
for i = 1:xSize
for j = 1:ySize
hIndex = round(image(i,j))+1;
histogram(hIndex) = histogram(hIndex)+1;
end
end
% histogram
% pause;
for i=1:histN
N=N+histogram(i);
sum=sum+histogram(i)*i;
end
mean=sum/N;
for i=1:histN
p=histogram(i)/N;
%cumulative for class 1 and class 2
w0=w0+p;
w1=1-w0;
%mean for class 1 and class 2
mu_k=mu_k+i*p;
if(w0==0)
m0=0;
else
m0=(mu_k/w0);
end
if(1-w0==0)
m1=0;
else
m1=(mean-mu_k)/(1-w0);
end
var_bet_class = w0*(m0-mean)*(m0-mean)+w1*(m1-mean)*(m1-mean);
if var_bet_class>var_max
var_max=var_bet_class;
threshold=i;
end
end
end
function [ threshold ] = Otsu_2D(image)
[xSize, ySize] = size(image);
maxVal = max(max(image));
histN = round(maxVal)+1;
histogram = zeros(1,histN);
threshold=0;
w0=0.0;
w1=0.0;
m0=0.0;
m1=0.0;
N=0;
p=0;
sum=0.0;
mean=0.0;
var_bet_class=0.0;
var_max=0.0;
mu_k=0.0;
for i = 1:xSize
for j = 1:ySize
hIndex = round(image(i,j))+1;
histogram(hIndex) = histogram(hIndex)+1;
end
end
% histogram
% pause;
for i=1:histN
N=N+histogram(i);
sum=sum+histogram(i)*i;
end
mean=sum/N;
for i=1:histN
p=histogram(i)/N;
%cumulative for class 1 and class 2
w0=w0+p;
w1=1-w0;
%mean for class 1 and class 2
mu_k=mu_k+i*p;
if(w0==0)
m0=0;
else
m0=(mu_k/w0);
end
if(1-w0==0)
m1=0;
else
m1=(mean-mu_k)/(1-w0);
end
var_bet_class = w0*(m0-mean)*(m0-mean)+w1*(m1-mean)*(m1-mean);
if var_bet_class>var_max
var_max=var_bet_class;
threshold=i;
end
end
end
function [ threshold ] = Otsu_2D_Img(image, masked)
[xSize, ySize] = size(image);
maxVal = max(max(image));
histN = round(maxVal)+1;
histogram = zeros(1,histN);
threshold=0;
w0=0.0;
w1=0.0;
m0=0.0;
m1=0.0;
N=0;
p=0;
sum=0.0;
mean=0.0;
var_bet_class=0.0;
var_max=0.0;
mu_k=0.0;
for i = 1:xSize
for j = 1:ySize
hIndex = round(image(i,j))+1;
histogram(hIndex) = histogram(hIndex)+1;
end
end
if masked == 1
histogram(1) = 0;
end
for i=1:histN
N=N+histogram(i);
sum=sum+histogram(i)*i;
end
mean=sum/N;
for i=1:histN
p=histogram(i)/N;
%cumulative for class 1 and class 2
w0=w0+p;
w1=1-w0;
%mean for class 1 and class 2
mu_k=mu_k+i*p;
if(w0==0)
m0=0;
else
m0=(mu_k/w0);
end
if(1-w0==0)
m1=0;
else
m1=(mean-mu_k)/(1-w0);
end
var_bet_class = w0*(m0-mean)*(m0-mean)+w1*(m1-mean)*(m1-mean);
if var_bet_class>var_max
var_max=var_bet_class;
threshold=i;
end
end
end
function [ threshold ] = Otsu_2D_Img(image, masked)
[xSize, ySize] = size(image);
maxVal = max(max(image));
histN = round(maxVal)+1;
histogram = zeros(1,histN);
threshold=0;
w0=0.0;
w1=0.0;
m0=0.0;
m1=0.0;
N=0;
p=0;
sum=0.0;
mean=0.0;
var_bet_class=0.0;
var_max=0.0;
mu_k=0.0;
for i = 1:xSize
for j = 1:ySize
hIndex = round(image(i,j))+1;
histogram(hIndex) = histogram(hIndex)+1;
end
end
if masked == 1
histogram(1) = 0;
end
for i=1:histN
N=N+histogram(i);
sum=sum+histogram(i)*i;
end
mean=sum/N;
for i=1:histN
p=histogram(i)/N;
%cumulative for class 1 and class 2
w0=w0+p;
w1=1-w0;
%mean for class 1 and class 2
mu_k=mu_k+i*p;
if(w0==0)
m0=0;
else
m0=(mu_k/w0);
end
if(1-w0==0)
m1=0;
else
m1=(mean-mu_k)/(1-w0);
end
var_bet_class = w0*(m0-mean)*(m0-mean)+w1*(m1-mean)*(m1-mean);
if var_bet_class>var_max
var_max=var_bet_class;
threshold=i;
end
end
end
function [threshold] = Otsu_3D_Hist(histogram,cut_thresh, masked)
%This function receives histogram as input
%It computes a threshold without considering the values in the histogram
%greater than cut_threshold
histN = cut_thresh;
threshold=1;
w0=0.0;
w1=0.0;
m0=0.0;
m1=0.0;
N=0;
p=0;
sum=0.0;
mean=0.0;
var_bet_class=0.0;
var_max=0.0;
mu_k=0.0;
if masked ==1
histogram(1) = 0;
end
for i=1:histN
N=N+histogram(i);
sum=sum+histogram(i)*i;
end
mean=sum/N;
for i=1:histN
p=histogram(i)/N;
%cumulative for class 1 and class 2
w0=w0+p;
w1=1-w0;
%mean for class 1 and class 2
mu_k=mu_k+i*p;
if(w0==0)
m0=0;
else
m0=(mu_k/w0);
end
if(1-w0==0)
m1=0;
else
m1=(mean-mu_k)/(1-w0);
end
var_bet_class = w0*(m0-mean)*(m0-mean)+w1*(m1-mean)*(m1-mean);
if var_bet_class>var_max
var_max=var_bet_class;
threshold=i;
end
end
end
function [threshold] = Otsu_3D_Hist(histogram,cut_thresh, masked)
%This function receives histogram as input
%It computes a threshold without considering the values in the histogram
%greater than cut_threshold
histN = cut_thresh;
threshold=1;
w0=0.0;
w1=0.0;
m0=0.0;
m1=0.0;
N=0;
p=0;
sum=0.0;
mean=0.0;
var_bet_class=0.0;
var_max=0.0;
mu_k=0.0;
if masked ==1
histogram(1) = 0;
end
for i=1:histN
N=N+histogram(i);
sum=sum+histogram(i)*i;
end
mean=sum/N;
for i=1:histN
p=histogram(i)/N;
%cumulative for class 1 and class 2
w0=w0+p;
w1=1-w0;
%mean for class 1 and class 2
mu_k=mu_k+i*p;
if(w0==0)
m0=0;
else
m0=(mu_k/w0);
end
if(1-w0==0)
m1=0;
else
m1=(mean-mu_k)/(1-w0);
end
var_bet_class = w0*(m0-mean)*(m0-mean)+w1*(m1-mean)*(m1-mean);
if var_bet_class>var_max
var_max=var_bet_class;
threshold=i;
end
end
end
function [threshold, histogram] = Otsu_3D_Img(image, masked)
[xSize, ySize, zSize] = size(image);
maxVal = max(max(max(image)));
histN = round(maxVal)+1;
histogram = zeros(1,histN);
threshold=1;
w0=0.0;
w1=0.0;
m0=0.0;
m1=0.0;
N=0;
p=0;
sum=0.0;
mean=0.0;
var_bet_class=0.0;
var_max=0.0;
mu_k=0.0;
for i = 1:xSize
for j = 1:ySize
for k = 1:zSize
hIndex = round(image(i,j,k))+1;
histogram(hIndex) = histogram(hIndex)+1;
end
end
end
if masked == 1
histogram(1) = 0;
end
for i=1:histN
N=N+histogram(i);
sum=sum+histogram(i)*i;
end
mean=sum/N;
for i=1:histN
p=histogram(i)/N;
%cumulative for class 1 and class 2
w0=w0+p;
w1=1-w0;
%mean for class 1 and class 2
mu_k=mu_k+i*p;
if(w0==0)
m0=0;
else
m0=(mu_k/w0);
end
if(1-w0==0)
m1=0;
else
m1=(mean-mu_k)/(1-w0);
end
var_bet_class = w0*(m0-mean)*(m0-mean)+w1*(m1-mean)*(m1-mean);
if var_bet_class>var_max
var_max=var_bet_class;
threshold=i;
end
end
end
function [threshold, histogram] = Otsu_3D_Img(image, masked)
[xSize, ySize, zSize] = size(image);
maxVal = max(max(max(image)));
histN = round(maxVal)+1;
histogram = zeros(1,histN);
threshold=1;
w0=0.0;
w1=0.0;
m0=0.0;
m1=0.0;
N=0;
p=0;
sum=0.0;
mean=0.0;
var_bet_class=0.0;
var_max=0.0;
mu_k=0.0;
for i = 1:xSize
for j = 1:ySize
for k = 1:zSize
hIndex = round(image(i,j,k))+1;
histogram(hIndex) = histogram(hIndex)+1;
end
end
end
if masked == 1
histogram(1) = 0;
end
for i=1:histN
N=N+histogram(i);
sum=sum+histogram(i)*i;
end
mean=sum/N;
for i=1:histN
p=histogram(i)/N;
%cumulative for class 1 and class 2
w0=w0+p;
w1=1-w0;
%mean for class 1 and class 2
mu_k=mu_k+i*p;
if(w0==0)
m0=0;
else
m0=(mu_k/w0);