Comparison of Hi-C experiments¶
Comparison between replicates¶
Load previous data¶
from pytadbit.mapping.analyze import eig_correlate_matrices, correlate_matrices, get_reproducibility
from pytadbit.parsers.hic_parser import load_hic_data_from_bam
from matplotlib import pyplot as plt
base_path = 'results/fragment/{0}_{1}/03_filtering/valid_reads12_{0}_{1}.bam'
bias_path = 'results/fragment/{0}_{1}/03_filtering/valid_reads12_{0}_{1}_Vanilla_{2}kb.biases'
Mouse B cell¶
We load the hic_data object from the BAM file
reso = 100000
cel1 = 'mouse_B'
cel2 = 'mouse_PSC'
rep1 = 'rep1'
rep2 = 'rep2'
hic_data1 = load_hic_data_from_bam(base_path.format(cel1, rep1),
resolution=reso,
biases=bias_path.format(cel1, rep1, reso // 1000),
ncpus=8)
hic_data2 = load_hic_data_from_bam(base_path.format(cel1, rep2),
resolution=reso,
biases=bias_path.format(cel2, rep2, reso // 1000),
ncpus=8)
(Matrix size 27269x27269) [2020-02-06 13:29:05] - Parsing BAM (122 chunks) [2020-02-06 13:29:05] .......... .......... .......... .......... .......... 50/122 .......... .......... .......... .......... .......... 100/122 .......... .......... .. 122/122 - Getting matrices [2020-02-06 13:31:43] .......... .......... .......... .......... .......... 50/122 .......... .......... .......... .......... .......... 100/122 .......... .......... .. 122/122 (Matrix size 27269x27269) [2020-02-06 13:33:21] - Parsing BAM (122 chunks) [2020-02-06 13:33:21] .......... .......... .......... .......... .......... 50/122 .......... .......... .......... .......... .......... 100/122 .......... .......... .. 122/122 - Getting matrices [2020-02-06 13:35:47] .......... .......... .......... .......... .......... 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.
## 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)
## 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
print('SCC score: %.4f (+- %.7f)' % (scc, std))
SCC score: 0.5482 (+- 0.0075563)
reso = 1000000
hic_data1 = hic_data2 = None
hic_data1 = load_hic_data_from_bam(base_path.format(cel1, rep1),
resolution=reso,
biases=bias_path.format(cel1, rep1, reso // 1000),
ncpus=8)
hic_data2 = load_hic_data_from_bam(base_path.format(cel1, rep2),
resolution=reso,
biases=bias_path.format(cel1, rep2, reso // 1000),
ncpus=8)
(Matrix size 2738x2738) [2020-02-06 14:19:49] - Parsing BAM (117 chunks) [2020-02-06 14:19:50] .......... .......... .......... .......... .......... 50/117 .......... .......... .......... .......... .......... 100/117 .......... ....... 117/117 - Getting matrices [2020-02-06 14:20:30] .......... .......... .......... .......... .......... 50/117 .......... .......... .......... .......... .......... 100/117 .......... ....... 117/117 (Matrix size 2738x2738) [2020-02-06 14:20:45] - Parsing BAM (117 chunks) [2020-02-06 14:20:45] .......... .......... .......... .......... .......... 50/117 .......... .......... .......... .......... .......... 100/117 .......... ....... 117/117 - Getting matrices [2020-02-06 14:21:20] .......... .......... .......... .......... .......... 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
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')
0.976 0.191 0.032 0.018 0.021 0.003 0.192 0.956 0.194 0.003 0.011 0.013 0.006 0.197 0.975 0.015 0.011 0.003 0.016 0.002 0.015 0.993 0.031 0.016 0.019 0.017 0.006 0.034 0.989 0.039 0.000 0.010 0.007 0.018 0.045 0.931
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).
reprod = get_reproducibility(hic_data1, hic_data2, num_evec=20, normalized=True, verbose=False)
print('Reproducibility score: %.4f' % (reprod))
Reproducibility score: 0.8629
Mouse iPS cell¶
We load the hic_data object from the BAM file
reso = 100000
hic_data1 = hic_data2 = None
hic_data1 = load_hic_data_from_bam(base_path.format(cel2, rep1),
resolution=reso,
biases=bias_path.format(cel2, rep1, reso // 1000),
ncpus=8)
hic_data2 = load_hic_data_from_bam(base_path.format(cel2, rep2),
resolution=reso,
biases=bias_path.format(cel2, rep2, reso // 1000),
ncpus=8)
(Matrix size 27269x27269) [2020-02-06 14:23:03] - Parsing BAM (122 chunks) [2020-02-06 14:23:03] .......... .......... .......... .......... .......... 50/122 .......... .......... .......... .......... .......... 100/122 .......... .......... .. 122/122 - Getting matrices [2020-02-06 14:23:47] .......... .......... .......... .......... .......... 50/122 .......... .......... .......... .......... .......... 100/122 .......... .......... .. 122/122 (Matrix size 27269x27269) [2020-02-06 14:24:57] - Parsing BAM (122 chunks) [2020-02-06 14:24:57] .......... .......... .......... .......... .......... 50/122 .......... .......... .......... .......... .......... 100/122 .......... .......... .. 122/122 - Getting matrices [2020-02-06 14:25:53] .......... .......... .......... .......... .......... 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.
## 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
print('SCC score: %.4f (+- %.7f)' % (scc, std))
SCC score: 0.6448 (+- 0.0277123)
reso = 1000000
hic_data1 = hic_data2 = None
hic_data1 = load_hic_data_from_bam(base_path.format(cel2, rep1),
resolution=reso,
biases=bias_path.format(cel2, rep1, reso // 1000),
ncpus=8)
hic_data2 = load_hic_data_from_bam(base_path.format(cel2, rep2),
resolution=reso,
biases=bias_path.format(cel2, rep2, reso // 1000),
ncpus=8)
(Matrix size 2738x2738) [2020-02-06 14:27:10] - Parsing BAM (117 chunks) [2020-02-06 14:27:10] .......... .......... .......... .......... .......... 50/117 .......... .......... .......... .......... .......... 100/117 .......... ....... 117/117 - Getting matrices [2020-02-06 14:27:44] .......... .......... .......... .......... .......... 50/117 .......... .......... .......... .......... .......... 100/117 .......... ....... 117/117 (Matrix size 2738x2738) [2020-02-06 14:27:57] - Parsing BAM (117 chunks) [2020-02-06 14:27:57] .......... .......... .......... .......... .......... 50/117 .......... .......... .......... .......... .......... 100/117 .......... ....... 117/117 - Getting matrices [2020-02-06 14:29:00] .......... .......... .......... .......... .......... 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
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).
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¶
reso = 100000
hic_data1 = hic_data2 = None
hic_data1 = load_hic_data_from_bam(base_path.format(cel1, rep1),
resolution=reso,
biases=bias_path.format(cel1, rep1, reso // 1000),
ncpus=8)
hic_data2 = load_hic_data_from_bam(base_path.format(cel2, rep1),
resolution=reso,
biases=bias_path.format(cel2, rep1, reso // 1000),
ncpus=8)
(Matrix size 27269x27269) [2020-02-06 14:30:21] - Parsing BAM (122 chunks) [2020-02-06 14:30:21] .......... .......... .......... .......... .......... 50/122 .......... .......... .......... .......... .......... 100/122 .......... .......... .. 122/122 - Getting matrices [2020-02-06 14:31:06] .......... .......... .......... .......... .......... 50/122 .......... .......... .......... .......... .......... 100/122 .......... .......... .. 122/122 (Matrix size 27269x27269) [2020-02-06 14:32:51] - Parsing BAM (122 chunks) [2020-02-06 14:32:51] .......... .......... .......... .......... .......... 50/122 .......... .......... .......... .......... .......... 100/122 .......... .......... .. 122/122 - Getting matrices [2020-02-06 14:33:27] .......... .......... .......... .......... .......... 50/122 .......... .......... .......... .......... .......... 100/122 .......... .......... .. 122/122
## 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
print('SCC score: %.4f (+- %.7f)' % (scc, std))
SCC score: 0.4770 (+- 0.0197731)
reso = 1000000
hic_data1 = load_hic_data_from_bam(base_path.format(cel1, rep1),
resolution=reso,
biases=bias_path.format(cel1, rep1, reso // 1000),
ncpus=8)
hic_data2 = load_hic_data_from_bam(base_path.format(cel2, rep1),
resolution=reso,
biases=bias_path.format(cel2, rep1, reso // 1000),
ncpus=8)
(Matrix size 2738x2738) [2020-02-06 14:34:57] - Parsing BAM (117 chunks) [2020-02-06 14:34:58] .......... .......... .......... .......... .......... 50/117 .......... .......... .......... .......... .......... 100/117 .......... ....... 117/117 - Getting matrices [2020-02-06 14:35:25] .......... .......... .......... .......... .......... 50/117 .......... .......... .......... .......... .......... 100/117 .......... ....... 117/117 (Matrix size 2738x2738) [2020-02-06 14:36:09] - Parsing BAM (117 chunks) [2020-02-06 14:36:09] .......... .......... .......... .......... .......... 50/117 .......... .......... .......... .......... .......... 100/117 .......... ....... 117/117 - Getting matrices [2020-02-06 14:36:41] .......... .......... .......... .......... .......... 50/117 .......... .......... .......... .......... .......... 100/117 .......... ....... 117/117
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')
0.875 0.171 0.011 0.048 0.066 0.023 0.251 0.787 0.481 0.072 0.070 0.032 0.117 0.484 0.838 0.063 0.045 0.032 0.013 0.027 0.118 0.970 0.048 0.034 0.003 0.052 0.020 0.059 0.961 0.009 0.013 0.004 0.016 0.016 0.012 0.859
reprod = get_reproducibility(hic_data1, hic_data2, num_evec=20, normalized=True, verbose=False)
print('Reproducibility score: %.4f' % (reprod))
Reproducibility score: 0.2864
Replicate 2¶
reso = 100000
hic_data1 = hic_data2 = None
hic_data1 = load_hic_data_from_bam(base_path.format(cel1, rep2),
resolution=reso,
biases=bias_path.format(cel1, rep2, reso // 1000),
ncpus=8)
hic_data2 = load_hic_data_from_bam(base_path.format(cel2, rep2),
resolution=reso,
biases=bias_path.format(cel2, rep2, reso // 1000),
ncpus=8)
(Matrix size 27269x27269) [2020-02-06 14:38:14] - Parsing BAM (122 chunks) [2020-02-06 14:38:14] .......... .......... .......... .......... .......... 50/122 .......... .......... .......... .......... .......... 100/122 .......... .......... .. 122/122 - Getting matrices [2020-02-06 14:39:02] .......... .......... .......... .......... .......... 50/122 .......... .......... .......... .......... .......... 100/122 .......... .......... .. 122/122 (Matrix size 27269x27269) [2020-02-06 14:40:54] - Parsing BAM (122 chunks) [2020-02-06 14:40:55] .......... .......... .......... .......... .......... 50/122 .......... .......... .......... .......... .......... 100/122 .......... .......... .. 122/122 - Getting matrices [2020-02-06 14:41:44] .......... .......... .......... .......... .......... 50/122 .......... .......... .......... .......... .......... 100/122 .......... .......... .. 122/122
## 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)
print('SCC score: %.4f (+- %.7f)' % (scc, std))
SCC score: 0.4696 (+- 0.0185008)
reso = 1000000
hic_data1 = load_hic_data_from_bam(base_path.format(cel1, rep2),
resolution=reso,
biases=bias_path.format(cel1, rep2, reso // 1000),
ncpus=8)
hic_data2 = load_hic_data_from_bam(base_path.format(cel2, rep2),
resolution=reso,
biases=bias_path.format(cel2, rep2, reso // 1000),
ncpus=8)
(Matrix size 2738x2738) [2020-02-06 14:43:12] - Parsing BAM (117 chunks) [2020-02-06 14:43:13] .......... .......... .......... .......... .......... 50/117 .......... .......... .......... .......... .......... 100/117 .......... ....... 117/117 - Getting matrices [2020-02-06 14:43:43] .......... .......... .......... .......... .......... 50/117 .......... .......... .......... .......... .......... 100/117 .......... ....... 117/117 (Matrix size 2738x2738) [2020-02-06 14:44:30] - Parsing BAM (117 chunks) [2020-02-06 14:44:31] .......... .......... .......... .......... .......... 50/117 .......... .......... .......... .......... .......... 100/117 .......... ....... 117/117 - Getting matrices [2020-02-06 14:45:16] .......... .......... .......... .......... .......... 50/117 .......... .......... .......... .......... .......... 100/117 .......... ....... 117/117
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
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.
from pytadbit.mapping import merge_bams
! 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/
cell = 'mouse_B'
rep1 = 'rep1'
rep2 = 'rep2'
hic_data1 = 'results/fragment/{0}_{1}/03_filtering/valid_reads12_{0}_{1}.bam'.format(cell, rep1)
hic_data2 = 'results/fragment/{0}_{1}/03_filtering/valid_reads12_{0}_{1}.bam'.format(cell, rep2)
hic_data = 'results/fragment/{0}_both/03_filtering/valid_reads12_{0}.bam'.format(cell)
merge_bams(hic_data1, hic_data2, hic_data)
- Mergeing experiments - Indexing new BAM file
cell = 'mouse_PSC'
rep1 = 'rep1'
rep2 = 'rep2'
hic_data1 = 'results/fragment/{0}_{1}/03_filtering/valid_reads12_{0}_{1}.bam'.format(cell, rep1)
hic_data2 = 'results/fragment/{0}_{1}/03_filtering/valid_reads12_{0}_{1}.bam'.format(cell, rep2)
hic_data = 'results/fragment/{0}_both/03_filtering/valid_reads12_{0}.bam'.format(cell)
merge_bams(hic_data1, hic_data2, hic_data)
- Mergeing experiments - Indexing new BAM file
Normalizing merged data¶
from pytadbit.mapping.analyze import hic_map
! mkdir -p results/fragment/mouse_B_both/04_normalizing
! mkdir -p results/fragment/mouse_PSC_both/04_normalizing
All in one loop to: - filter - normalize - generate intra-chromosome and genomic matrices
All datasets are analysed at various resolutions.
for cell in ['mouse_B','mouse_PSC']:
print(' -', cell)
for reso in [1000000, 200000, 100000]:
print(' *', reso)
# load hic_data
hic_data = load_hic_data_from_bam(
'results/fragment/{0}_both/03_filtering/valid_reads12_{0}.bam'.format(cell),
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_B
* 1000000
(Matrix size 2738x2738) [2020-02-06 15:09:37]
- Parsing BAM (117 chunks) [2020-02-06 15:09:37]
.......... .......... .......... .......... .......... 50/117
.......... .......... .......... .......... .......... 100/117
.......... ....... 117/117
- Getting matrices [2020-02-06 15:10:54]
.......... .......... .......... .......... .......... 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 2698 2707 2708 2709 2712 2737
WARNING: removing columns having less than (11301.146+0j) counts:
1 2 3 67 68 69 197 198 199 372 373 379 380 381 382 540 541 542 543 601
602 671 698 699 700 792 850 851 852 999 1000 1001 1002 1008 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 1902 1939 2022 2023 2024 2126 2127 2128 2129 2145 2222
2225 2226 2227 2228 2254 2260 2261 2262 2263 2321 2322 2323 2412 2413 2414 2474 2475 2476 2478 2501
2502 2503 2504 2508 2598 2599 2622 2623 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655
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
=> 2606.178
- rescaling biases
* 200000
(Matrix size 13641x13641) [2020-02-06 15:13:33]
- Parsing BAM (122 chunks) [2020-02-06 15:13:33]
.......... .......... .......... .......... .......... 50/122
.......... .......... .......... .......... .......... 100/122
.......... .......... .. 122/122
- Getting matrices [2020-02-06 15:14:51]
.......... .......... .......... .......... .......... 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 15 112 113 114 978 979
980 981 982 983 984 985 986 987 988 989 990 991 992 993 1856 1857 1861 1890 1891 1892
1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 2093 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 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 4131 4132 4133 4134 4135 4233 4234 4235 4236 4237 4238 4239 4240 4241 4242 4243 4244
4245 4246 4247 4248 4982 4983 4984 4985 4986 4987 4988 4989 4990 4991 4992 4993 4994 4995 4996 4997
4998 5030 5031 5091 5092 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 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
10687 10688 10689 11077 11088 11089 11090 11091 11092 11093 11094 11095 11096 11097 11098 11099 11100 11101 11102 11103
11126 11226 11227 11228 11259 11261 11263 11264 11275 11276 11277 11278 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 12471 12472 12541 12628 12882 12948 12949 12950 12953
12954 13030 13031 13177 13178 13181 13184 13197 13199 13218 13219 13220 13221 13240 13244 13246 13247 13248 13251 13254
13255 13256 13258 13259 13264 13271 13272 13274 13275 13276 13277 13278 13302 13303 13304 13305 13306 13307 13308 13309
13310 13311 13312 13313 13314 13315 13316 13317 13318 13319 13320 13335 13365 13366 13367 13368 13406 13411 13412 13413
13420 13421 13422 13423 13424 13425 13426 13427 13428 13429 13430 13431 13432 13433 13434 13435 13437 13438 13439 13440
13441 13442 13443 13444 13445 13446 13447 13448 13449 13450 13452 13453 13455 13457 13459 13460 13461 13462 13463 13465
13466 13467 13468 13472 13484 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 13529 13530 13531 13532
13533 13546 13551 13552 13564 13565 13566 13580 13582 13584 13609 13610 13637 13638 13639 13640
WARNING: removing columns having less than 2249.148 counts:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 112 113 114 318 329
330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 653 960 978 979
980 981 982 983 984 985 986 987 988 989 990 991 992 993 1494 1495 1855 1856 1857 1858
1859 1860 1861 1862 1863 1867 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902
1903 1904 1968 2075 2093 2393 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 3000 3001
3190 3299 3300 3301 3302 3341 3342 3343 3344 3345 3347 3348 3401 3402 3403 3407 3408 3420 3421 3422
3443 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 3952 4131 4132 4133 4134 4135 4233 4234 4235 4236 4237
4238 4239 4240 4241 4242 4243 4244 4245 4246 4247 4248 4648 4836 4851 4952 4953 4954 4957 4958 4962
4964 4965 4973 4974 4975 4977 4979 4980 4981 4982 4983 4984 4985 4986 4987 4988 4989 4990 4991 4992
4993 4994 4995 4996 4997 4998 4999 5020 5021 5022 5023 5024 5025 5026 5027 5028 5029 5030 5031 5032
5084 5085 5086 5087 5088 5089 5090 5091 5092 5093 5094 5095 5096 5097 5098 5145 5146 5147 5148 5176
5177 5178 5179 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 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
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 8344
8347 8358 8359 8360 8361 8364 8366 8367 8685 8794 8848 8849 8850 8851 8852 8853 8854 8855 8856 8857
8858 8859 8860 8861 8862 9177 9178 9450 9451 9452 9453 9454 9455 9456 9457 9458 9459 9460 9461 9462
9463 9464 9465 9467 9468 9469 9470 9474 9475 9476 9477 9478 9479 9486 9581 9662 9663 9664 9665 9715
9716 9717 9798 10075 10076 10077 10078 10079 10080 10081 10082 10083 10084 10085 10086 10087 10088 10089 10090 10091
10121 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 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 11232 11234 11258
11259 11260 11261 11262 11263 11264 11265 11266 11267 11268 11270 11271 11272 11273 11274 11275 11276 11277 11278 11279
11280 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 12038 12049 12050 12051 12204 12325 12326 12327 12328 12329 12330 12331 12332 12333 12334 12335 12336 12337 12338
12339 12340 12343 12344 12345 12347 12349 12350 12351 12360 12450 12451 12461 12462 12463 12464 12465 12466 12467 12468
12469 12470 12471 12472 12473 12475 12476 12477 12478 12479 12480 12483 12485 12486 12487 12490 12492 12493 12494 12496
12498 12499 12500 12501 12502 12515 12541 12598 12599 12600 12601 12628 12691 12844 12882 12942 12943 12945 12947 12948
12949 12950 12951 12952 12953 12954 12955 13030 13031 13065 13066 13067 13068 13069 13071 13072 13073 13075 13176 13177
13178 13179 13180 13181 13182 13183 13184 13185 13186 13187 13188 13189 13190 13191 13192 13193 13194 13195 13196 13197
13198 13199 13200 13201 13202 13203 13204 13205 13206 13207 13208 13209 13210 13211 13212 13213 13214 13215 13216 13217
13218 13219 13220 13221 13222 13223 13224 13225 13226 13227 13228 13229 13230 13231 13232 13233 13234 13235 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 1141 of 13641 columns with poor signal
iterative correction
- copying matrix
- computing biases
rescaling to factor 1
- getting the sum of the matrix
=> 12819.011
- rescaling biases
* 100000
(Matrix size 27269x27269) [2020-02-06 15:22:32]
- Parsing BAM (122 chunks) [2020-02-06 15:22:33]
.......... .......... .......... .......... .......... 50/122
.......... .......... .......... .......... .......... 100/122
.......... .......... .. 122/122
- Getting matrices [2020-02-06 15:24:23]
.......... .......... .......... .......... .......... 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 1783 1919 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 3709 3710 3711 3712 3713 3716 3719 3720
3721 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 4184 4185 4186 4285 4286 4912
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 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
8259 8260 8261 8262 8263 8264 8265 8266 8267 8268 8269 8454 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 9901 9902 9903 9950 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 10056 10057 10058 10059 10165 10168 10177 10178 10179 10180 10181 10182 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 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 16095 16134 16135 16136 16137 16145 16146 16147 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 19153 19154 19160 19161 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 21363 21364 21365 21366
21367 21368 21369 21372 22138 22140 22143 22144 22145 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 22507 22508 22510 22511 22512 22513 22514 22515 22516 22517 22518
22519 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
24708 24709 24889 24891 24910 24911 24913 24915 24930 24931 24932 24933 24934 24945 24947 24950 24954 24958 24987 24989
25013 25017 25018 25069 25070 25071 25175 25183 25189 25190 25243 25244 25245 25246 25752 25753 25754 25884 25885 25886
25887 25888 25889 25892 25894 25895 25896 25897 25898 26048 26049 26050 26051 26052 26123 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 26430 26459 26460 26461 26463 26464 26466 26467 26468 26470 26473 26474 26475 26476 26479 26480 26481 26482
26483 26484 26486 26487 26488 26489 26490 26493 26494 26495 26496 26497 26498 26499 26500 26502 26503 26504 26505 26506
26509 26510 26511 26512 26513 26514 26515 26516 26519 26520 26521 26522 26523 26526 26528 26529 26530 26531 26532 26533
26534 26535 26536 26537 26538 26539 26540 26541 26542 26543 26544 26545 26547 26548 26549 26550 26551 26568 26574 26575
26576 26577 26579 26580 26581 26582 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 26634 26635 26636 26650 26651 26653 26655 26657 26658 26659 26661
26662 26664 26665 26666 26667 26669 26671 26673 26679 26681 26682 26683 26684 26686 26689 26691 26692 26693 26695 26714
26716 26717 26718 26719 26720 26721 26722 26723 26724 26778 26779 26780 26799 26800 26801 26802 26804 26806 26807 26808
26809 26810 26811 26812 26813 26814 26815 26816 26817 26820 26821 26822 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 26891 26892 26893 26894 26896
26897 26898 26899 26900 26901 26902 26904 26905 26906 26907 26908 26909 26910 26911 26912 26913 26914 26915 26916 26917
26918 26919 26920 26921 26922 26923 26924 26925 26927 26930 26931 26932 26937 26941 26942 26949 26953 26954 26955 26956
26957 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 27032 27033 27034 27035 27036 27037 27038 27040 27042 27045 27046
27047 27048 27049 27050 27051 27052 27053 27054 27055 27057 27062 27063 27064 27065 27066 27067 27068 27069 27070 27071
27073 27074 27075 27076 27079 27080 27082 27088 27089 27090 27091 27092 27097 27115 27116 27117 27118 27119 27120 27121
27122 27123 27125 27126 27127 27128 27130 27131 27133 27135 27136 27140 27143 27144 27145 27146 27147 27148 27150 27151
27152 27153 27154 27155 27156 27158 27204 27205 27206 27207 27208 27260 27261 27262 27263 27264 27265 27266 27267 27268
WARNING: removing columns having less than 2417.695 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 30 223 224 225 226 227 228 229 608 609 610
611 618 619 635 636 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671
672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 854
855 1304 1305 1306 1334 1335 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 2180 2859 2865 2961 2962 2963 2986 2987 2988 2989 3182 3708 3709 3710 3711 3712 3713 3714 3715
3716 3717 3718 3719 3720 3721 3722 3723 3724 3725 3727 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 4700 4717 4763 4782 4783 4784 4785 4787 4788 4789 4790 4814 4815 4816 4817 4869 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 6115 6376 6377 6378 6594 6595 6596 6597 6598
6599 6600 6601 6602 6603 6663 6664 6665 6666 6667 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 6836 6837 6838 6839 6840 6841 6842 6845 6847 6849 6851
6855 6870 6871 6872 6874 6875 6877 6878 6879 6880 6881 6882 6883 6884 6885 6886 6887 6888 6889 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 7056 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 7991 8259 8260 8261 8262 8263 8264 8265 8266 8267
8268 8269 8454 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 9187 9292 9293 9365 9366
9609 9668 9669 9670 9671 9672 9673 9698 9699 9763 9900 9901 9902 9903 9904 9905 9906 9908 9909 9910
9911 9912 9913 9918 9919 9920 9921 9923 9924 9925 9926 9927 9939 9941 9942 9943 9944 9945 9946 9947
9949 9950 9951 9952 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 10036 10037 10038 10039 10040 10041 10042 10043 10044 10045 10046
10047 10048 10049 10050 10051 10052 10053 10054 10055 10056 10057 10058 10059 10060 10061 10070 10071 10075 10076 10077
10078 10112 10113 10114 10116 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 10279 10280
10282 10283 10284 10286 10287 10288 10289 10290 10291 10292 10293 10347 10348 10349 10350 10351 10352 10353 10354 10355
10557 10560 11021 11022 11023 11352 11353 11354 11379 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 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 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 15343 15611 15753 15754 15755 15846 15847 15851 15852 15964 15965 15966
15967 15968 15969 15970 15971 15972 15973 15974 15975 15976 15994 15995 15996 15997 15998 15999 16000 16001 16002 16003
16004 16005 16006 16007 16095 16096 16097 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 16673 16674 16675 16676 16679 16680 16681 16682
16683 16685 16686 16687 16691 16692 16693 16695 16703 16704 16705 16706 16707 16708 16709 16710 16711 16712 16713 16714
16715 16716 16718 16719 16720 16721 16722 16723 16724 16725 16726 16727 16728 16729 17037 17362 17363 17365 17580 17581
17582 17613 17620 17621 17622 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 17917 18342 18344 18345
18346 18347 18348 18349 18350 18351 18352 18689 18756 18887 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 18957 18958 18959 18960 18961 18963
18964 18965 18966 18967 18968 18969 19088 19152 19153 19154 19155 19160 19161 19307 19308 19309 19310 19311 19313 19314
19315 19316 19317 19318 19319 19320 19321 19322 19327 19328 19336 19337 19417 19418 19420 19421 19422 19423 19424 19425
19426 19427 19428 19429 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 20233 20234 20529 20530 20531 20532 20533 20723 20724 20725 20726 20727 21180 21181
21182 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 21374 22126 22127 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
22338 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 22638 22639 22640 22646 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 24065 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 24686 24687 24688 24689 24690 24691
24692 24708 24709 24887 24888 24889 24890 24891 24892 24895 24896 24898 24899 24901 24902 24903 24904 24906 24907 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 24953 24954 24955 24956 24958 24959 24960 24961 24962 24963 24964 24966 24967 24968 24969 24971 24972 24973 24974
24975 24976 24977 24978 24980 24981 24982 24983 24984 24985 24986 24987 24988 24989 24990 24991 24992 24993 24994 25013
25014 25015 25017 25018 25019 25069 25070 25071 25072 25175 25176 25183 25184 25185 25186 25187 25188 25189 25190 25191
25192 25197 25243 25244 25245 25246 25362 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 25991 25994 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 26358 26359 26360 26361
26362 26363 26364 26365 26366 26367 26368 26369 26370 26371 26372 26373 26374 26375 26376 26377 26378 26379 26380 26381
26382 26383 26384 26385 26386 26387 26388 26389 26390 26391 26392 26393 26394 26395 26396 26397 26398 26399 26400 26401
26402 26403 26404 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 2665 of 27269 columns with poor signal
iterative correction
- copying matrix
- computing biases
rescaling to factor 1
- getting the sum of the matrix
=> 25100.692
- rescaling biases
- mouse_PSC
* 1000000
(Matrix size 2738x2738) [2020-02-06 15:41:12]
- Parsing BAM (117 chunks) [2020-02-06 15:41:14]
.......... .......... .......... .......... .......... 50/117
.......... .......... .......... .......... .......... 100/117
.......... ....... 117/117
- Getting matrices [2020-02-06 15:42:22]
.......... .......... .......... .......... .......... 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
WARNING: removing columns having less than (6950.013+0j) 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
* 200000
(Matrix size 13641x13641) [2020-02-06 15:45:25]
- Parsing BAM (122 chunks) [2020-02-06 15:45:26]
.......... .......... .......... .......... .......... 50/122
.......... .......... .......... .......... .......... 100/122
.......... .......... .. 122/122
- Getting matrices [2020-02-06 15:46:31]
.......... .......... .......... .......... .......... 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 (1599.084+0j) 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) [2020-02-06 15:50:54]
- Parsing BAM (122 chunks) [2020-02-06 15:50:55]
.......... .......... .......... .......... .......... 50/122
.......... .......... .......... .......... .......... 100/122
.......... .......... .. 122/122
- Getting matrices [2020-02-06 15:52:03]
.......... .......... .......... .......... .......... 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+0j) 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
