# Comparison of Hi-C experiments

## Comparison between replicates

``````

In [1]:

from pytadbit.mapping.analyze import eig_correlate_matrices, correlate_matrices, get_reproducibility
from matplotlib import pyplot as plt

``````
``````

In [2]:

``````

### Mouse B cell

We load the hic_data object from the BAM file

``````

In [3]:

reso = 100000
cel1 = 'mouse_B'
cel2 = 'mouse_PSC'
rep1  = 'rep1'
rep2  = 'rep2'

``````
``````

In [4]:

resolution=reso,
biases=bias_path.format(cel1, rep1, reso / 1000),
ncpus=8)
resolution=reso,
biases=bias_path.format(cel2, rep2, reso / 1000),
ncpus=8)

``````
``````

(Matrix size 27269x27269)                                                    [2018-10-20 12:28:54]

- Parsing BAM (122 chunks)                                                   [2018-10-20 12:28:54]
.......... .......... .......... .......... ..........     50/122
.......... .......... .......... .......... ..........    100/122
.......... .......... ..                                  122/122

- Getting matrices                                                           [2018-10-20 12:31:36]
.......... .......... .......... .......... ..........     50/122
.......... .......... .......... .......... ..........    100/122
.......... .......... ..                                  122/122

(Matrix size 27269x27269)                                                    [2018-10-20 12:37:40]

- Parsing BAM (122 chunks)                                                   [2018-10-20 12:37:41]
.......... .......... .......... .......... ..........     50/122
.......... .......... .......... .......... ..........    100/122
.......... .......... ..                                  122/122

- Getting matrices                                                           [2018-10-20 12:40:28]
.......... .......... .......... .......... ..........     50/122
.......... .......... .......... .......... ..........    100/122
.......... .......... ..                                  122/122

``````

We compare the interactions of the two Hi-C matrices at a given distance.

#### The Spearman rank correlation of the matrix diagonals

In the plot we represent the Spearman rank correlation of the diagonals of the matrices starting from the main diagonal until the diagonal at 10Mbp.

``````

In [5]:

## this part is to "tune" the plot ##
plt.figure(figsize=(9, 6))
axe = plt.subplot()
axe.grid()
axe.set_xticks(range(0, 55, 5))
axe.set_xticklabels(['%d Mb' % int(i * 0.2) if i else '' for i in range(0, 55, 5)], rotation=-45)
#####################################

spearmans, dists, scc, std = correlate_matrices(hic_data1, hic_data2, max_dist=50, show=True, axe=axe)

``````
``````

``````
``````

In [5]:

## this part is to "tune" the plot ##
plt.figure(figsize=(9, 6))
axe = plt.subplot()
axe.grid()
axe.set_xticks(range(0, 55, 5))
axe.set_xticklabels(['%d Mb' % int(i * 0.2) if i else '' for i in range(0, 55, 5)], rotation=-45)
#####################################

spearmans, dists, scc, std = correlate_matrices(hic_data1, hic_data2, max_dist=50, show=True, axe=axe, normalized=True)

``````
``````

``````

The SCC score as in HiCrep (see https://doi.org/10.1101/gr.220640.117) is also computed. The value of SCC ranges from −1 to 1 and can be interpreted in a way similar to the standard correlation

``````

In [6]:

print 'SCC score: %.4f (+- %.7f)' % (scc, std)

``````
``````

SCC score: 0.5482 (+- 0.0075563)

``````
``````

In [23]:

reso = 1000000
hic_data1 = hic_data2 = None
resolution=reso,
biases=bias_path.format(cel1, rep1, reso / 1000),
ncpus=8)
resolution=reso,
biases=bias_path.format(cel1, rep2, reso / 1000),
ncpus=8)

``````
``````

(Matrix size 2738x2738)                                                      [2018-10-04 12:00:32]

- Parsing BAM (117 chunks)                                                   [2018-10-04 12:00:32]
.......... .......... .......... .......... ..........     50/117
.......... .......... .......... .......... ..........    100/117
.......... .......                                        117/117

- Getting matrices                                                           [2018-10-04 12:01:16]
.......... .......... .......... .......... ..........     50/117
.......... .......... .......... .......... ..........    100/117
.......... .......                                        117/117

(Matrix size 2738x2738)                                                      [2018-10-04 12:01:57]

- Parsing BAM (117 chunks)                                                   [2018-10-04 12:01:57]
.......... .......... .......... .......... ..........     50/117
.......... .......... .......... .......... ..........    100/117
.......... .......                                        117/117

- Getting matrices                                                           [2018-10-04 12:02:43]
.......... .......... .......... .......... ..........     50/117
.......... .......... .......... .......... ..........    100/117
.......... .......                                        117/117

``````

#### The correlation of the eigenvectors

Since the eigenvectors of a matrix capture its internal correlations [26], two matrices with highly correlation of eigenvectors are considered to have similar structure.

In this case we limit the computation to the first 6 eigenvectors

``````

In [24]:

corrs = eig_correlate_matrices(hic_data1, hic_data2, show=True, aspect='auto', normalized=True)

for cor in corrs:
print ' '.join(['%5.3f' % (c) for c in cor]) + '\n'

``````
``````

0.980 0.110 0.135 0.022 0.038 0.001

0.089 0.353 0.927 0.002 0.006 0.016

0.150 0.924 0.338 0.008 0.004 0.005

0.018 0.010 0.003 0.994 0.027 0.019

0.036 0.007 0.014 0.028 0.991 0.004

0.002 0.001 0.017 0.019 0.024 0.805

``````

#### The reproducibility score (Q)

Computed as in HiC-spector (https://doi.org/10.1093/bioinformatics/btx152), it is also based on comparing eigenvectors. The reproducibility score ranges from 0 (low similarity) to 1 (identity).

``````

In [25]:

reprod = get_reproducibility(hic_data1, hic_data2, num_evec=20, normalized=True, verbose=False)
print 'Reproducibility score: %.4f' % (reprod)

``````
``````

Reproducibility score: 0.8905

``````

### Mouse iPS cell

We load the hic_data object from the BAM file

``````

In [4]:

reso = 100000
hic_data1 = hic_data2 = None

``````
``````

In [5]:

resolution=reso,
biases=bias_path.format(cel2, rep1, reso / 1000),
ncpus=8)
resolution=reso,
biases=bias_path.format(cel2, rep2, reso / 1000),
ncpus=8)

``````
``````

(Matrix size 27269x27269)                                                    [2018-10-04 12:28:39]

- Parsing BAM (122 chunks)                                                   [2018-10-04 12:28:39]
.......... .......... .......... .......... ..........     50/122
.......... .......... .......... .......... ..........    100/122
.......... .......... ..                                  122/122

- Getting matrices                                                           [2018-10-04 12:29:45]
.......... .......... .......... .......... ..........     50/122
.......... .......... .......... .......... ..........    100/122
.......... .......... ..                                  122/122

(Matrix size 27269x27269)                                                    [2018-10-04 12:32:01]

- Parsing BAM (122 chunks)                                                   [2018-10-04 12:32:01]
.......... .......... .......... .......... ..........     50/122
.......... .......... .......... .......... ..........    100/122
.......... .......... ..                                  122/122

- Getting matrices                                                           [2018-10-04 12:33:11]
.......... .......... .......... .......... ..........     50/122
.......... .......... .......... .......... ..........    100/122
.......... .......... ..                                  122/122

``````

We compare the interactions of the two Hi-C matrices at a given distance.

#### The Spearman rank correlation of the matrix diagonals

In the plot we represent the Spearman rank correlation of the diagonals of the matrices starting from the main diagonal until the diagonal at 10Mbp.

``````

In [6]:

## this part is to "tune" the plot ##
plt.figure(figsize=(9, 6))
axe = plt.subplot()
axe.grid()
axe.set_xticks(range(0, 55, 5))
axe.set_xticklabels(['%d Mb' % int(i * 0.2) if i else '' for i in range(0, 55, 5)], rotation=-45)
#####################################

spearmans, dists, scc, std = correlate_matrices(hic_data1, hic_data2, max_dist=50, show=True, axe=axe)

``````
``````

``````

The SCC score as in HiCrep (see https://doi.org/10.1101/gr.220640.117) is also computed. The value of SCC ranges from −1 to 1 and can be interpreted in a way similar to the standard correlation

``````

In [7]:

print 'SCC score: %.4f (+- %.7f)' % (scc, std)

``````
``````

SCC score: 0.6448 (+- 0.0277123)

``````
``````

In [8]:

reso = 1000000
hic_data1 = hic_data2 = None
resolution=reso,
biases=bias_path.format(cel2, rep1, reso / 1000),
ncpus=8)
resolution=reso,
biases=bias_path.format(cel2, rep2, reso / 1000),
ncpus=8)

``````
``````

(Matrix size 2738x2738)                                                      [2018-10-04 12:37:58]

- Parsing BAM (117 chunks)                                                   [2018-10-04 12:37:58]
.......... .......... .......... .......... ..........     50/117
.......... .......... .......... .......... ..........    100/117
.......... .......                                        117/117

- Getting matrices                                                           [2018-10-04 12:38:53]
.......... .......... .......... .......... ..........     50/117
.......... .......... .......... .......... ..........    100/117
.......... .......                                        117/117

(Matrix size 2738x2738)                                                      [2018-10-04 12:39:36]

- Parsing BAM (117 chunks)                                                   [2018-10-04 12:39:37]
.......... .......... .......... .......... ..........     50/117
.......... .......... .......... .......... ..........    100/117
.......... .......                                        117/117

- Getting matrices                                                           [2018-10-04 12:40:20]
.......... .......... .......... .......... ..........     50/117
.......... .......... .......... .......... ..........    100/117
.......... .......                                        117/117

``````

#### The correlation of the eigenvectors

Since the eigenvectors of a matrix capture its internal correlations [26], two matrices with highly correlation of eigenvectors are considered to have similar structure.

In this case we limit the computation to the first 6 eigenvectors

``````

In [9]:

corrs = eig_correlate_matrices(hic_data1, hic_data2, show=True, aspect='auto', normalized=True)

for cor in corrs:
print ' '.join(['%5.3f' % (c) for c in cor]) + '\n'

``````
``````

0.989 0.088 0.002 0.005 0.005 0.002

0.094 0.983 0.073 0.010 0.006 0.008

0.009 0.071 0.987 0.056 0.034 0.028

0.006 0.014 0.053 0.988 0.025 0.001

0.006 0.007 0.031 0.028 0.985 0.079

0.002 0.008 0.021 0.015 0.081 0.954

``````

#### The reproducibility score (Q)

Computed as in HiC-spector (https://doi.org/10.1093/bioinformatics/btx152), it is also based on comparing eigenvectors. The reproducibility score ranges from 0 (low similarity) to 1 (identity).

``````

In [10]:

reprod = get_reproducibility(hic_data1, hic_data2, num_evec=20, normalized=True, verbose=False)
print 'Reproducibility score: %.4f' % (reprod)

``````
``````

Reproducibility score: 0.5979

``````

## Comparison between cell types

### Replicate 1

``````

In [4]:

reso = 100000
hic_data1 = hic_data2 = None
resolution=reso,
biases=bias_path.format(cel1, rep1, reso / 1000),
ncpus=8)
resolution=reso,
biases=bias_path.format(cel2, rep1, reso / 1000),
ncpus=8)

``````
``````

(Matrix size 27269x27269)                                                    [2018-10-04 13:10:53]

- Parsing BAM (122 chunks)                                                   [2018-10-04 13:10:53]
.......... .......... .......... .......... ..........     50/122
.......... .......... .......... .......... ..........    100/122
.......... .......... ..                                  122/122

- Getting matrices                                                           [2018-10-04 13:12:37]
.......... .......... .......... .......... ..........     50/122
.......... .......... .......... .......... ..........    100/122
.......... .......... ..                                  122/122

(Matrix size 27269x27269)                                                    [2018-10-04 13:15:39]

- Parsing BAM (122 chunks)                                                   [2018-10-04 13:15:40]
.......... .......... .......... .......... ..........     50/122
.......... .......... .......... .......... ..........    100/122
.......... .......... ..                                  122/122

- Getting matrices                                                           [2018-10-04 13:17:36]
.......... .......... .......... .......... ..........     50/122
.......... .......... .......... .......... ..........    100/122
.......... .......... ..                                  122/122

``````
``````

In [5]:

## this part is to "tune" the plot ##
plt.figure(figsize=(9, 6))
axe = plt.subplot()
axe.grid()
axe.set_xticks(range(0, 55, 5))
axe.set_xticklabels(['%d Mb' % int(i * 0.2) if i else '' for i in range(0, 55, 5)], rotation=-45)
#####################################

spearmans, dists, scc, std = correlate_matrices(hic_data1, hic_data2, max_dist=50, show=True, axe=axe)

``````
``````

``````

We expect a lower SCC score between different cell types

``````

In [6]:

print 'SCC score: %.4f (+- %.7f)' % (scc, std)

``````
``````

SCC score: 0.4770 (+- 0.0197731)

``````
``````

In [5]:

reso = 1000000
resolution=reso,
biases=bias_path.format(cel1, rep1, reso / 1000),
ncpus=8)
resolution=reso,
biases=bias_path.format(cel2, rep1, reso / 1000),
ncpus=8)

``````
``````

(Matrix size 2738x2738)                                                      [2018-10-04 14:27:44]

- Parsing BAM (117 chunks)                                                   [2018-10-04 14:27:44]
.......... .......... .......... .......... ..........     50/117
.......... .......... .......... .......... ..........    100/117
.......... .......                                        117/117

- Getting matrices                                                           [2018-10-04 14:28:52]
.......... .......... .......... .......... ..........     50/117
.......... .......... .......... .......... ..........    100/117
.......... .......                                        117/117

(Matrix size 2738x2738)                                                      [2018-10-04 14:29:17]

- Parsing BAM (117 chunks)                                                   [2018-10-04 14:29:17]
.......... .......... .......... .......... ..........     50/117
.......... .......... .......... .......... ..........    100/117
.......... .......                                        117/117

- Getting matrices                                                           [2018-10-04 14:30:30]
.......... .......... .......... .......... ..........     50/117
.......... .......... .......... .......... ..........    100/117
.......... .......                                        117/117

``````
``````

In [6]:

corrs = eig_correlate_matrices(hic_data1, hic_data2, show=True, aspect='auto', normalized=True)

for cor in corrs:
print ' '.join(['%5.3f' % (c) for c in cor]) + '\n'

``````
``````

0.877 0.173 0.002 0.046 0.070 0.028

0.084 0.216 0.935 0.107 0.018 0.057

0.259 0.902 0.250 0.001 0.089 0.001

0.013 0.022 0.127 0.970 0.054 0.043

0.006 0.066 0.019 0.061 0.966 0.034

0.013 0.036 0.038 0.027 0.035 0.247

``````
``````

In [7]:

reprod = get_reproducibility(hic_data1, hic_data2, num_evec=20, normalized=True, verbose=False)
print 'Reproducibility score: %.4f' % (reprod)

``````
``````

Reproducibility score: 0.2641

``````

### Replicate 2

``````

In [8]:

reso = 100000
hic_data1 = hic_data2 = None
resolution=reso,
biases=bias_path.format(cel1, rep2, reso / 1000),
ncpus=8)
resolution=reso,
biases=bias_path.format(cel2, rep2, reso / 1000),
ncpus=8)

``````
``````

(Matrix size 27269x27269)                                                    [2018-10-04 14:33:28]

- Parsing BAM (122 chunks)                                                   [2018-10-04 14:33:28]
.......... .......... .......... .......... ..........     50/122
.......... .......... .......... .......... ..........    100/122
.......... .......... ..                                  122/122

- Getting matrices                                                           [2018-10-04 14:35:15]
.......... .......... .......... .......... ..........     50/122
.......... .......... .......... .......... ..........    100/122
.......... .......... ..                                  122/122

(Matrix size 27269x27269)                                                    [2018-10-04 14:38:29]

- Parsing BAM (122 chunks)                                                   [2018-10-04 14:38:29]
.......... .......... .......... .......... ..........     50/122
.......... .......... .......... .......... ..........    100/122
.......... .......... ..                                  122/122

- Getting matrices                                                           [2018-10-04 14:40:07]
.......... .......... .......... .......... ..........     50/122
.......... .......... .......... .......... ..........    100/122
.......... .......... ..                                  122/122

``````
``````

In [9]:

## this part is to "tune" the plot ##
plt.figure(figsize=(9, 6))
axe = plt.subplot()
axe.grid()
axe.set_xticks(range(0, 55, 5))
axe.set_xticklabels(['%d Mb' % int(i * 0.2) if i else '' for i in range(0, 55, 5)], rotation=-45)
#####################################

spearmans, dists, scc, std = correlate_matrices(hic_data1, hic_data2, max_dist=50, show=True, axe=axe)

``````
``````

``````
``````

In [10]:

print 'SCC score: %.4f (+- %.7f)' % (scc, std)

``````
``````

SCC score: 0.4696 (+- 0.0185008)

``````
``````

In [11]:

reso = 1000000
resolution=reso,
biases=bias_path.format(cel1, rep2, reso / 1000),
ncpus=8)
resolution=reso,
biases=bias_path.format(cel2, rep2, reso / 1000),
ncpus=8)

``````
``````

(Matrix size 2738x2738)                                                      [2018-10-04 14:42:54]

- Parsing BAM (117 chunks)                                                   [2018-10-04 14:42:55]
.......... .......... .......... .......... ..........     50/117
.......... .......... .......... .......... ..........    100/117
.......... .......                                        117/117

- Getting matrices                                                           [2018-10-04 14:43:40]
.......... .......... .......... .......... ..........     50/117
.......... .......... .......... .......... ..........    100/117
.......... .......                                        117/117

(Matrix size 2738x2738)                                                      [2018-10-04 14:44:29]

- Parsing BAM (117 chunks)                                                   [2018-10-04 14:44:30]
.......... .......... .......... .......... ..........     50/117
.......... .......... .......... .......... ..........    100/117
.......... .......                                        117/117

- Getting matrices                                                           [2018-10-04 14:45:17]
.......... .......... .......... .......... ..........     50/117
.......... .......... .......... .......... ..........    100/117
.......... .......                                        117/117

``````
``````

In [12]:

corrs = eig_correlate_matrices(hic_data1, hic_data2, show=True, aspect='auto', normalized=True)

for cor in corrs:
print ' '.join(['%5.3f' % (c) for c in cor]) + '\n'

``````
``````

0.904 0.086 0.018 0.057 0.080 0.028

0.031 0.846 0.445 0.015 0.087 0.033

0.004 0.407 0.855 0.034 0.073 0.089

0.029 0.022 0.058 0.971 0.039 0.030

0.037 0.052 0.015 0.057 0.948 0.086

0.011 0.015 0.022 0.020 0.005 0.776

``````
``````

In [13]:

reprod = get_reproducibility(hic_data1, hic_data2, num_evec=20, normalized=True, verbose=False)
print 'Reproducibility score: %.4f' % (reprod)

``````
``````

Reproducibility score: 0.3852

``````

## Merge Hi-C experiments

Once agreed that experiments are similar, they can be merged.

Here is a simple way to merge valid pairs. Arguably we may want to merge unfiltered data but the difference would be minimal specially with non-replicates.

``````

In [ ]:

``````
``````

In [ ]:

! mkdir -p results/fragment/mouse_B_both/
! mkdir -p results/fragment/mouse_PSC_both/
! mkdir -p results/fragment/mouse_B_both/03_filtering/
! mkdir -p results/fragment/mouse_PSC_both/03_filtering/

``````
``````

In [ ]:

cell = 'mouse_B'
rep1 = 'rep1'
rep2 = 'rep2'

merge_bams(hic_data1, hic_data2, hic_data)

``````
``````

In [ ]:

cell = 'mouse_PSC'
rep1 = 'rep1'
rep2 = 'rep2'

merge_bams(hic_data1, hic_data2, hic_data)

``````

## Normalizing merged data

``````

In [4]:

``````
``````

In [27]:

! mkdir -p results/fragment/both_mouse_B/04_normalizing
! mkdir -p results/fragment/both_mouse_PSC/04_normalizing

``````

All in one loop to:

• filter
• normalize
• generate intra-chromosome and genomic matrices

All datasets are analysed at various resolutions.

``````

In [5]:

for cell in ['mouse_B','mouse_PSC']:
print ' -', cell
for reso in [1000000, 200000, 100000]:
print '   *', reso
reso)
# filter columns
hic_data.filter_columns(draw_hist=False, min_count=10, by_mean=True)
# normalize
hic_data.normalize_hic(iterations=0)
# save biases to reconstruct normalization
hic_data.save_biases('results/fragment/{0}_both/04_normalizing/biases_{0}_both_{1}kb.biases'.format(cell, reso / 1000))
# save data as raw matrix per chromsome
hic_map(hic_data, by_chrom='intra', normalized=False,
savedata='results/fragment/{1}_both/04_normalizing/{0}_raw'.format(reso, cell))
# save data as normalized matrix per chromosome
hic_map(hic_data, by_chrom='intra', normalized=True,
savedata='results/fragment/{1}_both/04_normalizing/{0}_norm'.format(reso, cell))
# if the resolution is low save the full genomic matrix
if reso > 500000:
hic_map(hic_data, by_chrom=False, normalized=False,
savefig ='results/fragment/{1}_both/04_normalizing/{0}_raw.png'.format(reso, cell),
savedata='results/fragment/{1}_both/04_normalizing/{0}_raw.mat'.format(reso, cell))

hic_map(hic_data, by_chrom=False, normalized=True,
savefig ='results/fragment/{1}_both/04_normalizing/{0}_norm.png'.format(reso, cell) ,
savedata='results/fragment/{1}_both/04_normalizing/{0}_norm.mat'.format(reso, cell))

``````
``````

- mouse_PSC
* 1000000

(Matrix size 2738x2738)                                                      [2018-10-04 16:50:56]

- Parsing BAM (117 chunks)                                                   [2018-10-04 16:50:56]
.......... .......... .......... .......... ..........     50/117
.......... .......... .......... .......... ..........    100/117
.......... .......                                        117/117

- Getting matrices                                                           [2018-10-04 16:53:28]
.......... .......... .......... .......... ..........     50/117
.......... .......... .......... .......... ..........    100/117
.......... .......                                        117/117

WARNING: Using twice min_count as the matrix was symmetricized and contains twice as many interactions as the original

WARNING: removing columns having less than 20 counts:
1     2     3   197   198   199   380   381   382   540   541   542   543   698   699   700   850   851   852  1000
1001  1002  1146  1147  1148  1276  1277  1278  1401  1402  1403  1532  1533  1534  1654  1655  1656  1657  1776  1777
1778  1897  1898  1899  2022  2023  2024  2126  2127  2128  2129  2226  2227  2228  2321  2322  2323  2412  2413  2414
2474  2475  2476  2645  2694  2695  2697  2698  2708  2709  2711  2712  2737
round(root, 3), ' '.join(

WARNING: removing columns having less than 6950.013 counts:
1     2     3    67    68    69   197   198   199   372   373   379   380   381   382   540   541   542   543   601
602   670   671   689   690   691   698   699   700   792   850   851   852   994   996   997   999  1000  1001  1002
1009  1020  1021  1022  1032  1146  1147  1148  1276  1277  1278  1401  1402  1403  1532  1533  1534  1602  1605  1619
1620  1621  1654  1655  1656  1657  1677  1775  1776  1777  1778  1897  1898  1899  1900  1950  2022  2023  2024  2126
2127  2128  2129  2145  2226  2227  2228  2254  2260  2261  2262  2263  2321  2322  2323  2412  2413  2414  2474  2475
2476  2477  2478  2501  2502  2503  2504  2506  2507  2508  2597  2598  2599  2622  2623  2644  2645  2649  2653  2654
2656  2657  2658  2659  2660  2661  2662  2663  2664  2665  2666  2667  2668  2669  2670  2671  2672  2673  2674  2675
2676  2677  2678  2679  2680  2681  2682  2683  2684  2685  2686  2687  2688  2689  2690  2691  2692  2693  2694  2695
2696  2697  2698  2699  2700  2701  2702  2703  2704  2705  2706  2707  2708  2709  2710  2711  2712  2713  2714  2715
2716  2717  2718  2719  2720  2721  2722  2723  2724  2725  2726  2727  2728  2729  2730  2731  2732  2733  2734  2735
2736  2737

Found 202 of 2738 columns with poor signal
iterative correction
- copying matrix
- computing biases
rescaling to factor 1
- getting the sum of the matrix
=> 2808.308
- rescaling biases

/home/dcastillo/miniconda2/lib/python2.7/site-packages/matplotlib/axes/_base.py:3477: UserWarning: Attempted to set non-positive ylimits for log-scale axis; invalid limits will be ignored.
'Attempted to set non-positive ylimits for log-scale axis; '

* 200000

(Matrix size 13641x13641)                                                    [2018-10-04 16:56:48]

- Parsing BAM (122 chunks)                                                   [2018-10-04 16:56:49]
.......... .......... .......... .......... ..........     50/122
.......... .......... .......... .......... ..........    100/122
.......... .......... ..                                  122/122

- Getting matrices                                                           [2018-10-04 16:58:37]
.......... .......... .......... .......... ..........     50/122
.......... .......... .......... .......... ..........    100/122
.......... .......... ..                                  122/122

WARNING: Using twice min_count as the matrix was symmetricized and contains twice as many interactions as the original

WARNING: removing columns having less than 20 counts:
1     2     3     4     5     6     7     8     9    10    11    12    13    14    15   112   113   114   960   978
979   980   981   982   983   984   985   986   987   988   989   990   991   992   993  1855  1856  1857  1859  1861
1890  1891  1892  1893  1894  1895  1896  1897  1898  1899  1900  1901  1902  1903  1904  2093  2437  2690  2691  2692
2693  2694  2695  2696  2697  2698  2699  2700  2701  2702  2703  2704  2705  2901  2902  2903  2904  2996  2997  3190
3299  3300  3302  3344  3345  3347  3401  3402  3403  3422  3473  3474  3475  3476  3477  3478  3479  3480  3481  3482
3483  3484  3485  3486  3487  3488  3550  3551  3949  4131  4132  4133  4134  4135  4233  4234  4235  4236  4237  4238
4239  4240  4241  4242  4243  4244  4245  4246  4247  4248  4851  4953  4954  4977  4980  4982  4983  4984  4985  4986
4987  4988  4989  4990  4991  4992  4993  4994  4995  4996  4997  4998  5030  5031  5091  5092  5093  5147  5678  5710
5711  5712  5713  5714  5715  5716  5717  5718  5719  5720  5721  5722  5723  5724  5725  6358  6359  6360  6361  6362
6363  6364  6365  6366  6367  6368  6369  6370  6371  6372  6373  6982  6983  6984  6985  6986  6987  6988  6989  6990
6991  6992  6993  6994  6995  6996  7019  7272  7319  7635  7636  7637  7638  7639  7640  7641  7642  7643  7644  7645
7646  7647  7648  7649  7650  7674  7987  7988  7989  8001  8004  8005  8006  8076  8246  8247  8248  8249  8250  8251
8252  8253  8254  8255  8256  8257  8258  8259  8260  8261  8794  8848  8849  8850  8851  8852  8853  8854  8855  8856
8857  8858  8859  8860  8861  8862  9450  9451  9452  9453  9454  9455  9456  9457  9458  9459  9460  9461  9462  9463
9464  9465  9581  9798 10076 10077 10078 10079 10080 10081 10082 10083 10084 10085 10086 10087 10088 10089 10090 10269
10270 10366 10367 10596 10597 10598 10599 10600 10601 10602 10603 10604 10605 10606 10607 10608 10609 10610 10611 10686
10687 10688 10689 11074 11077 11088 11089 11090 11091 11092 11093 11094 11095 11096 11097 11098 11099 11100 11101 11102
11103 11126 11226 11227 11228 11258 11259 11260 11261 11262 11263 11264 11273 11275 11276 11277 11278 11279 11280 11325
11329 11330 11363 11364 11365 11366 11564 11565 11566 11567 11568 11569 11570 11571 11572 11573 11574 11575 11576 11577
11578 11953 12018 12019 12020 12021 12022 12023 12024 12025 12026 12027 12028 12029 12030 12031 12032 12049 12050 12051
12325 12326 12327 12328 12329 12330 12331 12332 12333 12334 12335 12336 12337 12338 12339 12340 12360 12461 12463 12471
12472 12479 12500 12541 12628 12882 12948 12949 12950 12952 12953 12954 13030 13031 13177 13178 13181 13184 13197 13199
13220 13236 13238 13246 13247 13248 13250 13251 13253 13254 13255 13256 13258 13259 13261 13262 13266 13267 13270 13271
13272 13274 13275 13276 13277 13278 13279 13281 13303 13305 13306 13307 13308 13309 13312 13314 13315 13316 13317 13318
13319 13320 13339 13366 13367 13368 13408 13411 13412 13413 13414 13416 13420 13421 13422 13423 13424 13425 13426 13427
13428 13429 13430 13431 13432 13433 13434 13435 13436 13437 13438 13439 13440 13441 13442 13443 13444 13445 13446 13447
13448 13449 13450 13452 13453 13455 13458 13459 13460 13461 13465 13466 13467 13468 13472 13484 13485 13486 13487 13488
13489 13490 13491 13492 13493 13494 13495 13496 13497 13498 13499 13500 13501 13502 13503 13505 13506 13507 13508 13509
13510 13511 13512 13513 13514 13515 13516 13517 13520 13529 13530 13531 13532 13551 13552 13568 13569 13581 13637 13638
13639 13640

WARNING: removing columns having less than 1599.085 counts:
1     2     3     4     5     6     7     8     9    10    11    12    13    14    15   112   113   114   318   329
330   331   332   333   334   335   336   337   338   339   340   341   342   343   344   345   346   870   960   978
979   980   981   982   983   984   985   986   987   988   989   990   991   992   993  1494  1495  1855  1856  1857
1858  1859  1860  1861  1863  1889  1890  1891  1892  1893  1894  1895  1896  1897  1898  1899  1900  1901  1902  1903
1904  1968  2074  2075  2076  2093  2393  2409  2436  2437  2438  2690  2691  2692  2693  2694  2695  2696  2697  2698
2699  2700  2701  2702  2703  2704  2705  2900  2901  2902  2903  2904  2991  2992  2993  2994  2995  2996  2997  2998
2999  3000  3001  3190  3252  3253  3255  3256  3257  3299  3300  3301  3302  3333  3334  3335  3336  3337  3338  3339
3340  3341  3342  3343  3344  3345  3347  3348  3401  3402  3403  3408  3420  3421  3422  3430  3431  3432  3433  3434
3435  3436  3437  3438  3439  3440  3441  3442  3443  3444  3445  3446  3473  3474  3475  3476  3477  3478  3479  3480
3481  3482  3483  3484  3485  3486  3487  3488  3531  3532  3550  3551  3943  3944  3945  3946  3947  3948  3949  3950
3951  3952  4131  4132  4133  4134  4135  4233  4234  4235  4236  4237  4238  4239  4240  4241  4242  4243  4244  4245
4246  4247  4248  4648  4836  4837  4838  4851  4883  4885  4886  4952  4953  4954  4956  4957  4958  4959  4960  4961
4962  4964  4965  4966  4967  4968  4969  4970  4971  4972  4973  4974  4975  4977  4979  4980  4982  4983  4984  4985
4986  4987  4988  4989  4990  4991  4992  4993  4994  4995  4996  4997  4998  4999  5021  5022  5023  5026  5027  5028
5029  5030  5031  5084  5085  5086  5087  5088  5089  5090  5091  5092  5093  5094  5095  5096  5097  5098  5113  5144
5145  5146  5147  5148  5280  5513  5678  5710  5711  5712  5713  5714  5715  5716  5717  5718  5719  5720  5721  5722
5723  5724  5725  5814  5818  6358  6359  6360  6361  6362  6363  6364  6365  6366  6367  6368  6369  6370  6371  6372
6373  6980  6981  6982  6983  6984  6985  6986  6987  6988  6989  6990  6991  6992  6993  6994  6995  6996  7019  7272
7319  7391  7635  7636  7637  7638  7639  7640  7641  7642  7643  7644  7645  7646  7647  7648  7649  7650  7674  7986
7987  7988  7989  7990  7991  8001  8002  8003  8004  8005  8006  8051  8071  8072  8073  8074  8075  8076  8077  8078
8079  8080  8081  8082  8083  8084  8246  8247  8248  8249  8250  8251  8252  8253  8254  8255  8256  8257  8258  8259
8260  8261  8339  8358  8359  8360  8361  8364  8366  8367  8685  8794  8813  8814  8815  8816  8817  8818  8848  8849
8850  8851  8852  8853  8854  8855  8856  8857  8858  8859  8860  8861  8862  9157  9450  9451  9452  9453  9454  9455
9456  9457  9458  9459  9460  9461  9462  9463  9464  9465  9467  9468  9469  9470  9473  9474  9475  9476  9477  9478
9479  9482  9484  9488  9581  9660  9662  9664  9665  9713  9714  9715  9716  9717  9718  9719  9720  9721  9798 10075
10076 10077 10078 10079 10080 10081 10082 10083 10084 10085 10086 10087 10088 10089 10090 10091 10269 10270 10366 10367
10596 10597 10598 10599 10600 10601 10602 10603 10604 10605 10606 10607 10608 10609 10610 10611 10686 10687 10688 10689
10690 10691 11068 11069 11070 11071 11072 11073 11074 11077 11088 11089 11090 11091 11092 11093 11094 11095 11096 11097
11098 11099 11100 11101 11102 11103 11126 11226 11227 11228 11229 11230 11231 11234 11258 11259 11260 11261 11262 11263
11264 11265 11266 11267 11268 11270 11271 11272 11273 11274 11275 11276 11277 11278 11279 11280 11289 11325 11329 11330
11331 11363 11364 11365 11366 11564 11565 11566 11567 11568 11569 11570 11571 11572 11573 11574 11575 11576 11577 11578
11953 12017 12018 12019 12020 12021 12022 12023 12024 12025 12026 12027 12028 12029 12030 12031 12032 12049 12050 12051
12204 12325 12326 12327 12328 12329 12330 12331 12332 12333 12334 12335 12336 12337 12338 12339 12340 12341 12343 12344
12345 12346 12347 12349 12350 12351 12360 12450 12451 12452 12457 12460 12461 12462 12463 12464 12465 12466 12467 12468
12469 12470 12471 12472 12473 12474 12475 12476 12477 12478 12479 12480 12483 12485 12486 12487 12488 12489 12490 12492
12493 12494 12495 12496 12497 12498 12499 12500 12501 12502 12515 12541 12597 12598 12599 12600 12601 12628 12691 12844
12882 12942 12943 12944 12945 12947 12948 12949 12950 12951 12952 12953 12954 12955 13030 13031 13065 13066 13067 13068
13069 13071 13072 13073 13176 13177 13178 13179 13180 13181 13182 13183 13184 13195 13196 13197 13198 13199 13200 13205
13210 13211 13212 13217 13218 13219 13220 13221 13222 13223 13224 13225 13226 13227 13228 13232 13233 13236 13237 13238
13239 13240 13241 13242 13243 13244 13245 13246 13247 13248 13249 13250 13251 13252 13253 13254 13255 13256 13257 13258
13259 13260 13261 13262 13263 13264 13265 13266 13267 13268 13269 13270 13271 13272 13273 13274 13275 13276 13277 13278
13279 13280 13281 13282 13283 13284 13285 13286 13287 13288 13289 13290 13291 13292 13293 13294 13295 13296 13297 13298
13299 13300 13301 13302 13303 13304 13305 13306 13307 13308 13309 13310 13311 13312 13313 13314 13315 13316 13317 13318
13319 13320 13321 13322 13323 13324 13325 13326 13327 13328 13329 13330 13331 13332 13333 13334 13335 13336 13337 13338
13339 13340 13341 13342 13343 13344 13345 13346 13347 13348 13349 13350 13351 13352 13353 13354 13355 13356 13357 13358
13359 13360 13361 13362 13363 13364 13365 13366 13367 13368 13369 13370 13371 13372 13373 13374 13375 13376 13377 13378
13379 13380 13381 13382 13383 13384 13385 13386 13387 13388 13389 13390 13391 13392 13393 13394 13395 13396 13397 13398
13399 13400 13401 13402 13403 13404 13405 13406 13407 13408 13409 13410 13411 13412 13413 13414 13415 13416 13417 13418
13419 13420 13421 13422 13423 13424 13425 13426 13427 13428 13429 13430 13431 13432 13433 13434 13435 13436 13437 13438
13439 13440 13441 13442 13443 13444 13445 13446 13447 13448 13449 13450 13451 13452 13453 13454 13455 13456 13457 13458
13459 13460 13461 13462 13463 13464 13465 13466 13467 13468 13469 13470 13471 13472 13473 13474 13475 13476 13477 13478
13479 13480 13481 13482 13483 13484 13485 13486 13487 13488 13489 13490 13491 13492 13493 13494 13495 13496 13497 13498
13499 13500 13501 13502 13503 13504 13505 13506 13507 13508 13509 13510 13511 13512 13513 13514 13515 13516 13517 13518
13519 13520 13521 13522 13523 13524 13525 13526 13527 13528 13529 13530 13531 13532 13533 13534 13535 13536 13537 13538
13539 13540 13541 13542 13543 13544 13545 13546 13547 13548 13549 13550 13551 13552 13553 13554 13555 13556 13557 13558
13559 13560 13561 13562 13563 13564 13565 13566 13567 13568 13569 13570 13571 13572 13573 13574 13575 13576 13577 13578
13579 13580 13581 13582 13583 13584 13585 13586 13587 13588 13589 13590 13591 13592 13593 13594 13595 13596 13597 13598
13599 13600 13601 13602 13603 13604 13605 13606 13607 13608 13609 13610 13611 13612 13613 13614 13615 13616 13617 13618
13619 13620 13621 13622 13623 13624 13625 13626 13627 13628 13629 13630 13631 13632 13633 13634 13637 13638 13639 13640

Found 1180 of 13641 columns with poor signal
iterative correction
- copying matrix
- computing biases
rescaling to factor 1
- getting the sum of the matrix
=> 13587.267
- rescaling biases
* 100000

(Matrix size 27269x27269)                                                    [2018-10-04 17:04:51]

- Parsing BAM (122 chunks)                                                   [2018-10-04 17:04:52]
.......... .......... .......... .......... ..........     50/122
.......... .......... .......... .......... ..........    100/122
.......... .......... ..                                  122/122

- Getting matrices                                                           [2018-10-04 17:07:01]
.......... .......... .......... .......... ..........     50/122
.......... .......... .......... .......... ..........    100/122
.......... .......... ..                                  122/122

WARNING: Using twice min_count as the matrix was symmetricized and contains twice as many interactions as the original

WARNING: removing columns having less than 20 counts:
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   223   224   225   226   227   228   635  1783  1919  1920
1921  1955  1956  1957  1958  1959  1960  1961  1962  1963  1964  1965  1966  1967  1968  1969  1970  1971  1972  1973
1974  1975  1976  1977  1978  1979  1980  1981  1982  1983  1984  1985  2865  3182  3708  3709  3710  3711  3712  3713
3715  3716  3717  3719  3720  3721  3725  3777  3778  3779  3780  3781  3782  3783  3784  3785  3786  3787  3788  3789
3790  3791  3792  3793  3794  3795  3796  3797  3798  3799  3800  3801  3802  3803  3804  3805  3806  3807  3935  3936
4148  4149  4152  4184  4185  4186  4285  4286  4870  4871  4872  4873  4875  4912  5091  5092  5378  5379  5380  5381
5382  5383  5384  5385  5386  5387  5388  5389  5390  5391  5392  5393  5394  5395  5396  5397  5398  5399  5400  5401
5402  5403  5404  5405  5406  5407  5408  5798  5799  5800  5801  5802  5803  5804  5805  5806  5978  5982  5988  5989
5990  5991  5992  5993  6376  6377  6378  6594  6595  6596  6597  6598  6601  6602  6680  6681  6683  6685  6686  6687
6688  6690  6691  6692  6693  6694  6730  6799  6800  6801  6802  6803  6804  6812  6813  6838  6840  6841  6842  6943
6944  6945  6946  6947  6948  6949  6950  6951  6952  6953  6954  6955  6956  6957  6958  6959  6960  6961  6962  6963
6964  6965  6966  6967  6968  6969  6970  6971  6972  6973  6974  7096  7097  7098  7099  7100  7889  7891  7892  7894
7895  7896  7902  8259  8260  8261  8262  8263  8264  8265  8266  8267  8268  8269  8454  8455  8463  8464  8465  8466
8467  8468  8469  8470  8471  8472  8473  8474  8475  8476  8477  8478  8479  8480  8481  8482  8483  8484  8485  8486
8487  8488  8489  8490  8491  8492  8493  9187  9293  9365  9668  9671  9673  9698  9699  9901  9902  9903  9904  9905
9906  9939  9946  9949  9950  9951  9955  9956  9957  9958  9960  9961  9962  9963  9964  9965  9966  9967  9968  9969
9970  9971  9972  9973  9974  9975  9976  9977  9978  9979  9980  9981  9982  9983  9984  9985  9986  9987  9988  9989
9990  9991  9992  9993  9995 10019 10056 10057 10058 10059 10164 10165 10168 10177 10178 10179 10180 10181 10182 10183
10184 10185 10187 10188 10191 10192 10289 10290 10291 10352 11352 11353 11354 11416 11417 11418 11419 11420 11421 11422
11423 11424 11425 11426 11427 11428 11429 11430 11431 11432 11433 11434 11435 11436 11437 11438 11439 11440 11441 11442
11443 11444 11445 11446 11969 12124 12711 12712 12713 12714 12715 12716 12717 12718 12719 12720 12721 12722 12723 12724
12725 12726 12727 12728 12729 12730 12731 12732 12733 12734 12735 12736 12737 12738 12739 12740 12741 13800 13955 13957
13958 13959 13960 13961 13962 13963 13964 13965 13966 13967 13968 13969 13970 13971 13972 13973 13974 13975 13976 13977
13978 13979 13980 13981 13982 13983 13984 13985 13986 13987 13988 14032 14033 14034 14537 14538 14539 14540 14631 14632
14633 15264 15265 15266 15267 15268 15269 15270 15271 15272 15273 15274 15275 15276 15277 15278 15279 15280 15281 15282
15283 15284 15285 15286 15287 15288 15289 15290 15291 15292 15293 15294 15295 15341 15342 15964 15967 15968 15969 15970
15971 15972 15995 15996 15997 15998 16000 16001 16002 16003 16004 16005 16006 16007 16095 16134 16135 16136 16137 16142
16145 16146 16147 16151 16485 16486 16487 16488 16489 16490 16491 16492 16493 16494 16495 16496 16497 16498 16499 16500
16501 16502 16503 16504 16505 16506 16507 16508 16509 16510 16511 16512 16513 16514 16515 17037 17580 17581 17687 17688
17689 17690 17691 17692 17693 17694 17695 17696 17697 17698 17699 17700 17701 17702 17703 17704 17705 17706 17707 17708
17709 17710 17711 17712 17713 17714 17715 17716 17717 18892 18893 18894 18895 18896 18897 18898 18899 18900 18901 18902
18903 18904 18905 18906 18907 18908 18909 18910 18911 18912 18913 18914 18915 18916 18917 18918 18919 18920 18921 18922
18927 18929 19152 19153 19154 19160 19161 19422 19425 19586 19587 19588 20142 20143 20144 20145 20146 20147 20148 20149
20150 20151 20152 20153 20154 20155 20156 20157 20158 20159 20160 20161 20162 20163 20164 20165 20166 20167 20168 20169
20170 20171 20172 20173 20234 20529 20530 20531 20532 20533 20723 20724 20725 20726 20727 21180 21181 21183 21184 21185
21186 21187 21188 21189 21190 21191 21192 21193 21194 21195 21196 21197 21198 21199 21200 21201 21202 21203 21204 21205
21206 21207 21208 21209 21210 21211 21212 21213 21362 21363 21364 21365 21366 21367 21368 21369 21372 21373 22136 22137
22138 22139 22140 22143 22144 22145 22155 22166 22167 22168 22169 22170 22171 22172 22173 22174 22175 22176 22177 22178
22179 22180 22181 22182 22183 22184 22185 22186 22187 22188 22189 22190 22191 22192 22193 22194 22195 22196 22241 22242
22441 22442 22443 22444 22445 22446 22505 22506 22507 22508 22509 22510 22511 22512 22513 22514 22515 22516 22517 22518
22519 22521 22522 22532 22533 22535 22536 22538 22539 22540 22541 22542 22543 22544 22545 22546 22547 22548 22549 22550
22551 22638 22639 22640 22647 22648 22649 22650 22651 22714 22715 22716 22717 22718 22719 22720 22721 22722 22723 23116
23117 23118 23119 23120 23121 23122 23123 23124 23125 23126 23127 23128 23129 23130 23131 23132 23133 23134 23135 23136
23137 23138 23139 23140 23141 23142 23143 23144 23145 23146 23230 23882 23883 23894 23895 23896 23897 24024 24025 24026
24027 24028 24029 24030 24031 24032 24033 24034 24035 24036 24037 24038 24039 24040 24041 24042 24043 24044 24045 24046
24047 24048 24049 24050 24051 24052 24053 24054 24066 24087 24088 24089 24090 24091 24092 24398 24639 24640 24641 24642
24643 24644 24645 24646 24647 24648 24649 24650 24651 24652 24653 24654 24655 24656 24657 24658 24659 24660 24661 24662
24663 24664 24665 24666 24667 24668 24669 24687 24708 24709 24889 24891 24910 24911 24913 24914 24915 24916 24924 24925
24930 24931 24932 24933 24934 24945 24946 24947 24948 24950 24954 24958 24987 24988 24989 25013 25017 25018 25069 25070
25071 25175 25183 25186 25189 25190 25243 25244 25245 25246 25752 25753 25754 25883 25884 25885 25886 25887 25888 25889
25890 25892 25893 25894 25895 25896 25897 25898 26048 26049 26050 26051 26052 26341 26342 26343 26344 26345 26349 26350
26351 26355 26356 26366 26367 26380 26381 26382 26383 26385 26386 26387 26422 26423 26424 26425 26426 26427 26428 26429
26459 26460 26461 26463 26464 26465 26466 26467 26468 26470 26471 26472 26473 26474 26475 26476 26477 26479 26480 26481
26482 26483 26484 26486 26487 26488 26489 26490 26493 26494 26495 26496 26497 26498 26499 26500 26501 26502 26503 26504
26505 26506 26509 26510 26511 26512 26513 26514 26515 26516 26518 26519 26520 26521 26522 26523 26526 26527 26528 26529
26530 26531 26532 26533 26534 26535 26536 26537 26538 26539 26540 26541 26542 26543 26544 26545 26546 26547 26549 26550
26551 26574 26576 26591 26592 26593 26594 26595 26596 26597 26598 26599 26600 26601 26602 26603 26604 26605 26606 26607
26608 26609 26610 26611 26612 26614 26615 26616 26617 26618 26619 26620 26621 26622 26623 26624 26625 26626 26627 26628
26629 26631 26635 26650 26651 26653 26654 26657 26658 26659 26660 26661 26662 26664 26665 26666 26667 26668 26673 26679
26680 26683 26685 26716 26717 26718 26719 26720 26721 26722 26723 26724 26746 26778 26780 26782 26799 26800 26801 26802
26803 26804 26805 26806 26807 26809 26810 26811 26812 26813 26814 26815 26816 26817 26819 26820 26821 26825 26827 26828
26829 26830 26831 26832 26833 26834 26835 26836 26837 26838 26839 26840 26841 26842 26843 26844 26845 26846 26847 26848
26849 26850 26851 26852 26853 26854 26855 26856 26857 26858 26859 26860 26861 26862 26863 26864 26865 26866 26867 26868
26869 26870 26871 26872 26873 26874 26875 26876 26877 26878 26879 26880 26881 26882 26883 26884 26885 26886 26887 26888
26889 26890 26891 26892 26893 26894 26895 26897 26898 26899 26901 26902 26903 26904 26905 26906 26907 26908 26909 26910
26911 26912 26913 26914 26916 26917 26918 26919 26920 26921 26922 26923 26924 26925 26927 26930 26931 26932 26933 26935
26936 26937 26939 26941 26942 26946 26949 26953 26954 26955 26956 26957 26958 26959 26960 26961 26962 26963 26964 26965
26966 26967 26968 26969 26970 26971 26972 26973 26974 26975 26976 26977 26978 26979 26980 26981 26982 26983 26984 26985
26986 26987 26988 26989 26990 26991 26992 26993 26994 26995 26997 26998 26999 27000 27001 27002 27003 27004 27005 27006
27007 27008 27009 27010 27011 27012 27013 27014 27015 27016 27017 27018 27019 27020 27021 27022 27023 27024 27025 27026
27027 27028 27033 27034 27036 27037 27038 27040 27041 27042 27045 27046 27047 27048 27049 27050 27051 27052 27053 27054
27055 27057 27059 27062 27063 27067 27069 27070 27071 27073 27074 27075 27076 27079 27080 27088 27089 27090 27091 27092
27115 27116 27117 27118 27119 27120 27121 27122 27123 27124 27125 27126 27127 27128 27148 27149 27150 27152 27156 27158
27204 27205 27206 27207 27208 27260 27261 27262 27263 27264 27265 27266 27267 27268

WARNING: removing columns having less than 1863.967 counts:
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   223   224   225   226   227   228   608   610   611   618
619   635   636   657   658   659   660   661   662   663   664   665   666   667   668   669   670   671   672   673
674   675   676   677   678   679   680   681   682   683   684   685   686   687   688   689   690   691   692   785
786   787   854   855  1306  1334  1335  1738  1739  1740  1783  1919  1920  1921  1955  1956  1957  1958  1959  1960
1961  1962  1963  1964  1965  1966  1967  1968  1969  1970  1971  1972  1973  1974  1975  1976  1977  1978  1979  1980
1981  1982  1983  1984  1985  2859  2865  2961  2962  2986  2987  2988  2989  3182  3708  3709  3710  3711  3712  3713
3714  3715  3716  3717  3718  3719  3720  3721  3722  3723  3724  3725  3732  3733  3776  3777  3778  3779  3780  3781
3782  3783  3784  3785  3786  3787  3788  3789  3790  3791  3792  3793  3794  3795  3796  3797  3798  3799  3800  3801
3802  3803  3804  3805  3806  3807  3934  3935  3936  4146  4147  4148  4149  4150  4151  4152  4183  4184  4185  4186
4285  4286  4714  4717  4782  4783  4784  4785  4788  4789  4790  4814  4815  4816  4817  4870  4871  4872  4873  4874
4875  4911  4912  5091  5092  5378  5379  5380  5381  5382  5383  5384  5385  5386  5387  5388  5389  5390  5391  5392
5393  5394  5395  5396  5397  5398  5399  5400  5401  5402  5403  5404  5405  5406  5407  5408  5797  5798  5799  5800
5801  5802  5803  5804  5805  5806  5978  5979  5980  5981  5982  5983  5984  5985  5986  5987  5988  5989  5990  5991
5992  5993  5994  5995  5996  5997  5998  5999  6000  6050  6376  6377  6378  6500  6501  6502  6503  6504  6507  6508
6509  6510  6511  6512  6514  6515  6594  6595  6596  6597  6598  6599  6600  6601  6602  6603  6663  6664  6665  6666
6667  6668  6669  6670  6671  6672  6673  6674  6675  6676  6677  6678  6679  6680  6681  6682  6683  6684  6685  6686
6687  6688  6690  6691  6692  6693  6694  6695  6730  6787  6799  6800  6801  6802  6803  6804  6805  6811  6812  6813
6814  6821  6836  6837  6838  6839  6840  6841  6842  6845  6847  6848  6851  6852  6855  6857  6858  6859  6860  6861
6862  6863  6864  6865  6866  6867  6868  6869  6870  6871  6872  6873  6874  6875  6876  6877  6878  6879  6880  6881
6882  6883  6884  6885  6886  6887  6888  6889  6890  6891  6943  6944  6945  6946  6947  6948  6949  6950  6951  6952
6953  6954  6955  6956  6957  6958  6959  6960  6961  6962  6963  6964  6965  6966  6967  6968  6969  6970  6971  6972
6973  6974  7054  7055  7057  7058  7059  7060  7061  7062  7063  7096  7097  7098  7099  7100  7852  7880  7881  7883
7884  7885  7886  7887  7888  7889  7890  7891  7892  7893  7894  7895  7896  7897  7898  7899  7900  7901  7902  8259
8260  8261  8262  8263  8264  8265  8266  8267  8268  8269  8454  8455  8463  8464  8465  8466  8467  8468  8469  8470
8471  8472  8473  8474  8475  8476  8477  8478  8479  8480  8481  8482  8483  8484  8485  8486  8487  8488  8489  8490
8491  8492  8493  8494  8972  9187  9292  9293  9365  9366  9609  9668  9669  9670  9671  9672  9673  9698  9699  9761
9762  9763  9764  9765  9766  9767  9768  9769  9900  9901  9902  9903  9904  9905  9906  9908  9909  9910  9911  9912
9913  9914  9915  9916  9917  9918  9919  9920  9921  9923  9924  9925  9926  9927  9928  9929  9930  9931  9932  9933
9934  9935  9936  9937  9938  9939  9940  9941  9942  9943  9944  9945  9946  9947  9949  9950  9951  9954  9955  9956
9957  9958  9959  9960  9961  9962  9963  9964  9965  9966  9967  9968  9969  9970  9971  9972  9973  9974  9975  9976
9977  9978  9979  9980  9981  9982  9983  9984  9985  9986  9987  9988  9989  9990  9991  9992  9993  9994  9995  9999
10000 10019 10037 10038 10039 10040 10041 10042 10043 10044 10047 10048 10049 10050 10051 10052 10053 10054 10055 10056
10057 10058 10059 10060 10061 10071 10075 10077 10078 10113 10114 10116 10136 10137 10163 10164 10165 10166 10167 10168
10169 10170 10171 10172 10173 10174 10175 10176 10177 10178 10179 10180 10181 10182 10183 10184 10185 10186 10187 10188
10189 10190 10191 10192 10193 10194 10222 10223 10279 10280 10281 10282 10283 10284 10285 10286 10287 10288 10289 10290
10291 10292 10293 10348 10351 10352 10355 10436 10437 10438 10440 10556 10557 10559 10560 10564 10567 10568 11021 11022
11023 11352 11353 11354 11416 11417 11418 11419 11420 11421 11422 11423 11424 11425 11426 11427 11428 11429 11430 11431
11432 11433 11434 11435 11436 11437 11438 11439 11440 11441 11442 11443 11444 11445 11446 11615 11623 11624 11628 11629
11630 11631 11632 11633 11830 11831 11968 11969 12124 12148 12149 12710 12711 12712 12713 12714 12715 12716 12717 12718
12719 12720 12721 12722 12723 12724 12725 12726 12727 12728 12729 12730 12731 12732 12733 12734 12735 12736 12737 12738
12739 12740 12741 13800 13954 13955 13956 13957 13958 13959 13960 13961 13962 13963 13964 13965 13966 13967 13968 13969
13970 13971 13972 13973 13974 13975 13976 13977 13978 13979 13980 13981 13982 13983 13984 13985 13986 13987 13988 14032
14033 14034 14537 14538 14539 14540 14631 14632 14633 14775 14776 14777 15264 15265 15266 15267 15268 15269 15270 15271
15272 15273 15274 15275 15276 15277 15278 15279 15280 15281 15282 15283 15284 15285 15286 15287 15288 15289 15290 15291
15292 15293 15294 15295 15341 15342 15754 15755 15847 15851 15852 15964 15965 15966 15967 15968 15969 15970 15971 15972
15973 15974 15975 15976 15977 15994 15995 15996 15997 15998 15999 16000 16001 16002 16003 16004 16005 16006 16007 16095
16096 16134 16135 16136 16137 16138 16139 16140 16141 16142 16143 16144 16145 16146 16147 16148 16149 16150 16151 16152
16153 16154 16155 16156 16157 16158 16159 16160 16161 16162 16163 16307 16485 16486 16487 16488 16489 16490 16491 16492
16493 16494 16495 16496 16497 16498 16499 16500 16501 16502 16503 16504 16505 16506 16507 16508 16509 16510 16511 16512
16513 16514 16515 16670 16671 16672 16675 16679 16680 16681 16682 16683 16685 16686 16687 16691 16703 16705 16707 16708
16709 16710 16711 16712 16713 16714 16715 16716 16718 16719 16720 16721 16722 16723 16724 16725 16726 16727 16728 17037
17362 17363 17580 17581 17582 17613 17617 17618 17619 17620 17621 17622 17623 17624 17625 17626 17627 17628 17629 17687
17688 17689 17690 17691 17692 17693 17694 17695 17696 17697 17698 17699 17700 17701 17702 17703 17704 17705 17706 17707
17708 17709 17710 17711 17712 17713 17714 17715 17716 17717 18305 18306 18307 18342 18345 18346 18348 18349 18351 18353
18689 18756 18883 18888 18892 18893 18894 18895 18896 18897 18898 18899 18900 18901 18902 18903 18904 18905 18906 18907
18908 18909 18910 18911 18912 18913 18914 18915 18916 18917 18918 18919 18920 18921 18922 18923 18924 18925 18926 18927
18928 18929 18930 18931 18932 18933 18934 18935 18936 18937 18938 18939 18940 18941 18942 18943 18944 18945 18946 18947
18948 18949 18950 18951 18953 18954 18955 18956 18958 18959 18960 18961 18963 18964 18965 18966 18967 18968 18969 19088
19152 19153 19154 19155 19160 19161 19307 19308 19309 19310 19311 19312 19313 19314 19315 19316 19317 19318 19319 19320
19321 19322 19323 19327 19417 19418 19419 19420 19421 19422 19423 19424 19425 19426 19427 19428 19429 19430 19431 19432
19433 19434 19586 19587 19588 19589 20141 20142 20143 20144 20145 20146 20147 20148 20149 20150 20151 20152 20153 20154
20155 20156 20157 20158 20159 20160 20161 20162 20163 20164 20165 20166 20167 20168 20169 20170 20171 20172 20173 20174
20234 20529 20530 20531 20532 20533 20723 20724 20725 20726 20727 20942 21180 21181 21183 21184 21185 21186 21187 21188
21189 21190 21191 21192 21193 21194 21195 21196 21197 21198 21199 21200 21201 21202 21203 21204 21205 21206 21207 21208
21209 21210 21211 21212 21213 21214 21362 21363 21364 21365 21366 21367 21368 21369 21370 21371 21372 21373 22125 22126
22127 22128 22129 22130 22131 22132 22133 22134 22135 22136 22137 22138 22139 22140 22143 22144 22145 22155 22165 22166
22167 22168 22169 22170 22171 22172 22173 22174 22175 22176 22177 22178 22179 22180 22181 22182 22183 22184 22185 22186
22187 22188 22189 22190 22191 22192 22193 22194 22195 22196 22230 22241 22242 22243 22247 22397 22441 22442 22443 22444
22445 22446 22447 22448 22449 22450 22451 22452 22453 22454 22455 22456 22457 22458 22504 22505 22506 22507 22508 22509
22510 22511 22512 22513 22514 22515 22516 22517 22518 22519 22520 22521 22522 22523 22524 22525 22526 22528 22529 22530
22531 22532 22533 22534 22535 22536 22537 22538 22539 22540 22541 22542 22543 22544 22545 22546 22547 22548 22549 22550
22551 22566 22567 22568 22638 22639 22640 22647 22648 22649 22650 22651 22652 22714 22715 22716 22717 22718 22719 22720
22721 22722 22723 23116 23117 23118 23119 23120 23121 23122 23123 23124 23125 23126 23127 23128 23129 23130 23131 23132
23133 23134 23135 23136 23137 23138 23139 23140 23141 23142 23143 23144 23145 23146 23230 23882 23883 23894 23895 23896
23897 24023 24024 24025 24026 24027 24028 24029 24030 24031 24032 24033 24034 24035 24036 24037 24038 24039 24040 24041
24042 24043 24044 24045 24046 24047 24048 24049 24050 24051 24052 24053 24054 24055 24066 24087 24088 24089 24090 24091
24092 24397 24398 24399 24639 24640 24641 24642 24643 24644 24645 24646 24647 24648 24649 24650 24651 24652 24653 24654
24655 24656 24657 24658 24659 24660 24661 24662 24663 24664 24665 24666 24667 24668 24669 24670 24671 24673 24674 24675
24676 24677 24678 24679 24680 24681 24682 24683 24684 24685 24686 24687 24688 24689 24690 24691 24692 24693 24708 24709
24884 24885 24886 24887 24888 24889 24890 24891 24892 24893 24894 24895 24896 24897 24898 24899 24900 24901 24902 24903
24904 24905 24906 24907 24908 24909 24910 24911 24912 24913 24914 24915 24916 24917 24918 24919 24920 24921 24922 24923
24924 24925 24926 24927 24928 24929 24930 24931 24932 24933 24934 24935 24936 24937 24938 24939 24940 24941 24942 24943
24944 24945 24946 24947 24948 24949 24950 24951 24953 24954 24955 24956 24958 24959 24960 24961 24962 24963 24964 24965
24966 24967 24968 24969 24970 24971 24972 24973 24974 24975 24976 24977 24978 24979 24980 24981 24982 24983 24984 24985
24986 24987 24988 24989 24990 24991 24992 24993 24994 25013 25014 25015 25016 25017 25018 25019 25069 25070 25071 25072
25175 25176 25182 25183 25184 25185 25186 25187 25188 25189 25190 25191 25192 25193 25194 25197 25243 25244 25245 25246
25362 25363 25370 25371 25372 25388 25589 25676 25677 25751 25752 25753 25754 25871 25872 25873 25874 25875 25876 25877
25878 25879 25880 25882 25883 25884 25885 25886 25887 25888 25889 25890 25891 25892 25893 25894 25895 25896 25897 25898
25899 25900 25907 25991 25993 25994 26003 26048 26049 26050 26051 26052 26118 26119 26120 26121 26122 26123 26124 26125
26126 26127 26128 26129 26130 26131 26132 26133 26134 26135 26136 26137 26138 26139 26140 26180 26340 26341 26342 26343
26344 26345 26346 26347 26348 26349 26350 26351 26352 26353 26354 26355 26356 26357 26366 26367 26376 26377 26378 26379
26380 26381 26382 26383 26384 26385 26386 26387 26388 26391 26392 26395 26396 26397 26398 26399 26400 26401 26402 26403
26405 26406 26407 26408 26409 26410 26411 26412 26413 26414 26415 26416 26417 26418 26419 26420 26421 26422 26423 26424
26425 26426 26427 26428 26429 26430 26431 26432 26433 26434 26435 26436 26437 26438 26439 26440 26441 26442 26443 26444
26445 26446 26447 26448 26449 26450 26451 26452 26453 26454 26455 26456 26457 26458 26459 26460 26461 26462 26463 26464
26465 26466 26467 26468 26469 26470 26471 26472 26473 26474 26475 26476 26477 26478 26479 26480 26481 26482 26483 26484
26485 26486 26487 26488 26489 26490 26491 26492 26493 26494 26495 26496 26497 26498 26499 26500 26501 26502 26503 26504
26505 26506 26507 26508 26509 26510 26511 26512 26513 26514 26515 26516 26517 26518 26519 26520 26521 26522 26523 26524
26525 26526 26527 26528 26529 26530 26531 26532 26533 26534 26535 26536 26537 26538 26539 26540 26541 26542 26543 26544
26545 26546 26547 26548 26549 26550 26551 26552 26553 26554 26555 26556 26557 26558 26559 26560 26561 26562 26563 26564
26565 26566 26567 26568 26569 26570 26571 26572 26573 26574 26575 26576 26577 26578 26579 26580 26581 26582 26583 26584
26585 26586 26587 26588 26589 26590 26591 26592 26593 26594 26595 26596 26597 26598 26599 26600 26601 26602 26603 26604
26605 26606 26607 26608 26609 26610 26611 26612 26613 26614 26615 26616 26617 26618 26619 26620 26621 26622 26623 26624
26625 26626 26627 26628 26629 26630 26631 26632 26633 26634 26635 26636 26637 26638 26639 26640 26641 26642 26643 26644
26645 26646 26647 26648 26649 26650 26651 26652 26653 26654 26655 26656 26657 26658 26659 26660 26661 26662 26663 26664
26665 26666 26667 26668 26669 26670 26671 26672 26673 26674 26675 26676 26677 26678 26679 26680 26681 26682 26683 26684
26685 26686 26687 26688 26689 26690 26691 26692 26693 26694 26695 26696 26697 26698 26699 26700 26701 26702 26703 26704
26705 26706 26707 26708 26709 26710 26711 26712 26713 26714 26715 26716 26717 26718 26719 26720 26721 26722 26723 26724
26725 26726 26727 26728 26729 26730 26731 26732 26733 26734 26735 26736 26737 26738 26739 26740 26741 26742 26743 26744
26745 26746 26747 26748 26749 26750 26751 26752 26753 26754 26755 26756 26757 26758 26759 26760 26761 26762 26763 26764
26765 26766 26767 26768 26769 26770 26771 26772 26773 26774 26775 26776 26777 26778 26779 26780 26781 26782 26783 26784
26785 26786 26787 26788 26789 26790 26791 26792 26793 26794 26795 26796 26797 26798 26799 26800 26801 26802 26803 26804
26805 26806 26807 26808 26809 26810 26811 26812 26813 26814 26815 26816 26817 26818 26819 26820 26821 26822 26823 26824
26825 26826 26827 26828 26829 26830 26831 26832 26833 26834 26835 26836 26837 26838 26839 26840 26841 26842 26843 26844
26845 26846 26847 26848 26849 26850 26851 26852 26853 26854 26855 26856 26857 26858 26859 26860 26861 26862 26863 26864
26865 26866 26867 26868 26869 26870 26871 26872 26873 26874 26875 26876 26877 26878 26879 26880 26881 26882 26883 26884
26885 26886 26887 26888 26889 26890 26891 26892 26893 26894 26895 26896 26897 26898 26899 26900 26901 26902 26903 26904
26905 26906 26907 26908 26909 26910 26911 26912 26913 26914 26915 26916 26917 26918 26919 26920 26921 26922 26923 26924
26925 26926 26927 26928 26929 26930 26931 26932 26933 26934 26935 26936 26937 26938 26939 26940 26941 26942 26943 26944
26945 26946 26947 26948 26949 26950 26951 26952 26953 26954 26955 26956 26957 26958 26959 26960 26961 26962 26963 26964
26965 26966 26967 26968 26969 26970 26971 26972 26973 26974 26975 26976 26977 26978 26979 26980 26981 26982 26983 26984
26985 26986 26987 26988 26989 26990 26991 26992 26993 26994 26995 26996 26997 26998 26999 27000 27001 27002 27003 27004
27005 27006 27007 27008 27009 27010 27011 27012 27013 27014 27015 27016 27017 27018 27019 27020 27021 27022 27023 27024
27025 27026 27027 27028 27029 27030 27031 27032 27033 27034 27035 27036 27037 27038 27039 27040 27041 27042 27043 27044
27045 27046 27047 27048 27049 27050 27051 27052 27053 27054 27055 27056 27057 27058 27059 27060 27061 27062 27063 27064
27065 27066 27067 27068 27069 27070 27071 27072 27073 27074 27075 27076 27077 27078 27079 27080 27081 27082 27083 27084
27085 27086 27087 27088 27089 27090 27091 27092 27093 27094 27095 27096 27097 27098 27099 27100 27101 27102 27103 27104
27105 27106 27107 27108 27109 27110 27111 27112 27113 27114 27115 27116 27117 27118 27119 27120 27121 27122 27123 27124
27125 27126 27127 27128 27129 27130 27131 27132 27133 27134 27135 27136 27137 27138 27139 27140 27141 27142 27143 27144
27145 27146 27147 27148 27149 27150 27151 27152 27153 27154 27155 27156 27157 27158 27159 27160 27161 27162 27163 27164
27165 27166 27167 27168 27169 27170 27171 27172 27173 27174 27175 27176 27177 27178 27179 27180 27181 27182 27183 27184
27185 27186 27187 27188 27189 27190 27191 27192 27193 27194 27195 27196 27197 27198 27199 27200 27201 27202 27203 27204
27205 27206 27207 27208 27209 27210 27211 27212 27213 27214 27215 27216 27217 27218 27219 27220 27221 27222 27223 27224
27225 27226 27227 27228 27229 27230 27231 27232 27233 27234 27235 27236 27237 27238 27239 27240 27241 27242 27243 27244
27245 27246 27247 27248 27249 27250 27251 27252 27253 27254 27255 27256 27257 27260 27261 27262 27263 27264 27265 27266
27267 27268

Found 2722 of 27269 columns with poor signal
iterative correction
- copying matrix
- computing biases
rescaling to factor 1
- getting the sum of the matrix
=> 26205.477
- rescaling biases

``````