In [1]:
%matplotlib inline

In [2]:
#
# Remember to 
#
#

In [3]:
from ipython_memwatcher import MemWatcher
mw = MemWatcher()
mw.start_watching_memory()


In [3] used 0.008 MiB RAM in 0.001s, peaked 0.000 MiB above current, total RAM usage 60.938 MiB

In [4]:
import glob
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib
pd.set_option('display.max_columns', 50) # print all rows


import os
os.chdir('/Users/evanbiederstedt/Downloads/RRBS_data_files')

import statsmodels.api as sm


In [4] used 42.066 MiB RAM in 0.943s, peaked 0.000 MiB above current, total RAM usage 103.004 MiB

In [5]:
"""

CD19cell_regions.csv
cw154_regions.csv
Normal_B_regions.csv
trito_regions.csv
pcell_regions.csv
mcell_regions.csv

"""


Out[5]:
'\n\nCD19cell_regions.csv\ncw154_regions.csv\nNormal_B_regions.csv\ntrito_regions.csv\npcell_regions.csv\nmcell_regions.csv\n\n'
In [5] used 0.043 MiB RAM in 0.006s, peaked 0.000 MiB above current, total RAM usage 103.047 MiB

In [6]:
trito = pd.read_csv("trito_regions.csv")
normal = pd.read_csv("Normal_B_regions.csv")
pcell = pd.read_csv("pcell_regions.csv")
mcell = pd.read_csv("mcell_regions.csv")
cw154 = pd.read_csv("cw154_regions.csv")
cd19cell = pd.read_csv("CD19cell_regions.csv")


In [6] used 0.840 MiB RAM in 0.048s, peaked 0.016 MiB above current, total RAM usage 103.887 MiB

In [7]:
print(trito.shape)
print(normal.shape)   # remove 2cell files
print(pcell.shape)
print(mcell.shape)
print(cw154.shape)
print(cd19cell.shape)


(44, 39)
(136, 39)
(90, 39)
(88, 39)
(66, 39)
(89, 39)
In [7] used 0.043 MiB RAM in 0.003s, peaked 0.000 MiB above current, total RAM usage 103.930 MiB

In [8]:
trito["filename"] = trito["filename"].str[:33]


In [8] used 0.086 MiB RAM in 0.003s, peaked 0.000 MiB above current, total RAM usage 104.016 MiB

In [9]:
trito.head()


Out[9]:
filename methylation_tssDistance methylation_genesDistance methylation_exonsDistance methylation_intronsDistance methylation_promoterDistance methylation_cgiDistance methylation_ctcfDistance methylation_ctcfUpDistance methylation_ctcfDownDistance methylation_geneDistalRegulatoryModulesDistance methylation_vistaEnhancersDistance methylation_3PrimeUTRDistance methylation_5PrimeUTRDistance methylation_firstExonDistance methylation_geneDistalRegulatoryModulesK562Distance methylation_hypoInHues64Distance methylation_intergenic methylation_shore methylation_shelf PDR_tssDistance PDR_genesDistance PDR_exonsDistance PDR_intronsDistance PDR_promoterDistance PDR_cgiDistance PDR_ctcfDistance PDR_ctcfUpDistance PDR_ctcfDownDistance PDR_geneDistalRegulatoryModulesDistance PDR_vistaEnhancersDistance PDR_3PrimeUTRDistance PDR_5PrimeUTRDistance PDR_firstExonDistance PDR_geneDistalRegulatoryModulesK562Distance PDR_hypoInHues64Distance PDR_intergenic PDR_shore PDR_shelf
0 RRBS_trito_pool_1_TAAGGCGA.ACAACC 0.0 0.570721 0.404589 0.594704 0.146371 0.169386 0.199815 0.0 0.199815 0.342986 0.444522 0.680480 0.305206 0.145730 0.254209 0.877142 0.820339 0.607671 0.803716 0.0 0.343014 0.371890 0.339596 0.347748 0.388709 0.385297 0.0 0.385297 0.401254 0.509555 0.386285 0.324236 0.367271 0.354046 0.240359 0.386809 0.377907 0.359143
1 RRBS_trito_pool_1_TAAGGCGA.ACGTGG 0.0 0.545781 0.383371 0.568638 0.141545 0.161519 0.191404 0.0 0.191404 0.326140 0.589834 0.670559 0.290196 0.140779 0.240221 0.809942 0.816166 0.573089 0.795932 0.0 0.348110 0.381251 0.341950 0.349891 0.398898 0.415058 0.0 0.415058 0.408417 0.548192 0.382172 0.332749 0.373615 0.359217 0.364148 0.391925 0.386808 0.354120
2 RRBS_trito_pool_1_TAAGGCGA.ACTCAC 0.0 0.564547 0.401760 0.588136 0.148529 0.174413 0.209041 0.0 0.209041 0.346473 0.553062 0.696068 0.296809 0.148360 0.255392 0.795883 0.832812 0.609544 0.812564 0.0 0.338412 0.371890 0.332321 0.351391 0.393829 0.392313 0.0 0.392313 0.412311 0.471703 0.378630 0.327488 0.370494 0.338321 0.334783 0.378580 0.378799 0.353949
3 RRBS_trito_pool_1_TAAGGCGA.AGGATG 0.0 0.567309 0.399934 0.592890 0.143897 0.168936 0.200661 0.0 0.200661 0.342257 0.665920 0.661426 0.308680 0.141673 0.242236 0.787966 0.824659 0.602995 0.799836 0.0 0.342724 0.374419 0.337654 0.346109 0.389718 0.399153 0.0 0.399153 0.405627 0.359189 0.391002 0.324431 0.360431 0.343730 0.304035 0.380413 0.373345 0.347372
4 RRBS_trito_pool_1_TAAGGCGA.ATAGCG 0.0 0.529224 0.367743 0.555131 0.136090 0.156827 0.175426 0.0 0.175426 0.307402 0.479145 0.644411 0.273473 0.134137 0.220729 0.815944 0.808981 0.575050 0.788587 0.0 0.349254 0.376307 0.342617 0.343348 0.388623 0.403861 0.0 0.403861 0.390288 0.471324 0.392438 0.332882 0.358450 0.319824 0.401641 0.398275 0.373236 0.363320
In [9] used 0.219 MiB RAM in 0.108s, peaked 0.000 MiB above current, total RAM usage 104.234 MiB

In [10]:
normal["filename"] = normal["filename"].str[:40]


In [10] used -0.016 MiB RAM in 0.003s, peaked 0.016 MiB above current, total RAM usage 104.219 MiB

In [11]:
normal.tail()


Out[11]:
filename methylation_tssDistance methylation_genesDistance methylation_exonsDistance methylation_intronsDistance methylation_promoterDistance methylation_cgiDistance methylation_ctcfDistance methylation_ctcfUpDistance methylation_ctcfDownDistance methylation_geneDistalRegulatoryModulesDistance methylation_vistaEnhancersDistance methylation_3PrimeUTRDistance methylation_5PrimeUTRDistance methylation_firstExonDistance methylation_geneDistalRegulatoryModulesK562Distance methylation_hypoInHues64Distance methylation_intergenic methylation_shore methylation_shelf PDR_tssDistance PDR_genesDistance PDR_exonsDistance PDR_intronsDistance PDR_promoterDistance PDR_cgiDistance PDR_ctcfDistance PDR_ctcfUpDistance PDR_ctcfDownDistance PDR_geneDistalRegulatoryModulesDistance PDR_vistaEnhancersDistance PDR_3PrimeUTRDistance PDR_5PrimeUTRDistance PDR_firstExonDistance PDR_geneDistalRegulatoryModulesK562Distance PDR_hypoInHues64Distance PDR_intergenic PDR_shore PDR_shelf
131 RRBS_normal_B_cell_H1_22_TAGGCATG.GTGAGG 0.0 0.476010 0.319477 0.501080 0.112156 0.125920 0.140172 0.0 0.140172 0.266191 0.520634 0.568097 0.250866 0.108529 0.199670 0.743346 0.709026 0.512304 0.723009 0.0 0.413186 0.417787 0.411922 0.371247 0.397676 0.361732 0.0 0.361732 0.449309 0.538398 0.431550 0.378186 0.390782 0.390614 0.433456 0.526395 0.438160 0.468283
132 RRBS_normal_B_cell_H1_22_TAGGCATG.GTTGAG 0.0 0.561826 0.421027 0.581238 0.194894 0.241158 0.227289 0.0 0.227289 0.355498 0.652030 0.675360 0.320042 0.195983 0.239473 0.848333 0.775009 0.603093 0.765059 0.0 0.378380 0.397910 0.375394 0.389046 0.412999 0.390730 0.0 0.390730 0.430454 0.628598 0.406830 0.371516 0.395340 0.389081 0.268990 0.455415 0.396795 0.385031
133 RRBS_normal_B_cell_H1_22_TAGGCATG.TAGCGG 0.0 0.403834 0.204255 0.435666 0.094118 0.093855 0.072948 0.0 0.072948 0.253112 0.333333 0.591837 0.223590 0.096000 0.223404 0.818182 0.820471 0.605528 0.801020 0.0 0.320128 0.375319 0.314522 0.375959 0.383838 0.258359 0.0 0.258359 0.396266 1.000000 0.346939 0.411282 0.409333 0.386525 0.818182 0.311942 0.381910 0.306122
134 RRBS_normal_B_cell_H1_22_TAGGCATG.TATCTC 0.0 0.601704 0.464233 0.621206 0.137723 0.148328 0.140719 0.0 0.140719 0.266187 0.400000 0.706186 0.334928 0.135086 0.218656 1.000000 0.878793 0.656104 0.840000 0.0 0.286919 0.323835 0.287354 0.384461 0.407314 0.347305 0.0 0.347305 0.350719 0.000000 0.368557 0.362440 0.391198 0.270812 0.000000 0.243974 0.306151 0.357500
135 RRBS_normal_B_cell_H1_22_TAGGCATG.TCTCTG 0.0 0.560233 0.366567 0.591090 0.120401 0.128137 0.182218 0.0 0.182218 0.329330 0.558873 0.703213 0.278496 0.111726 0.241188 0.930269 0.887614 0.628778 0.865630 0.0 0.276382 0.338157 0.268336 0.360595 0.380319 0.401676 0.0 0.401676 0.369472 0.293523 0.278300 0.314054 0.366833 0.346789 0.255155 0.244022 0.313503 0.248764
In [11] used 0.023 MiB RAM in 0.107s, peaked 0.000 MiB above current, total RAM usage 104.242 MiB

In [12]:
pcell["protocol"] = pcell["filename"].str[:31]


In [12] used 0.004 MiB RAM in 0.003s, peaked 0.000 MiB above current, total RAM usage 104.246 MiB

In [13]:
pcell["filename"][pcell["protocol"]=='RRBS_NormalBCD19pCD27pcell1_22_'] = pcell["filename"].str[:46]
pcell["filename"][pcell["protocol"]=='RRBS_NormalBCD19pCD27pcell23_44'] = pcell["filename"].str[:47]
pcell["filename"][pcell["protocol"]=='RRBS_NormalBCD19pCD27pcell45_66'] = pcell["filename"].str[:47]
pcell["filename"][pcell["protocol"]=='RRBS_NormalBCD19pCD27pcell67_88'] = pcell["filename"].str[:47]


/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':
/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  from ipykernel import kernelapp as app
/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  app.launch_new_instance()
/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
In [13] used 0.109 MiB RAM in 0.249s, peaked 0.000 MiB above current, total RAM usage 104.355 MiB

In [14]:
pcell.tail()


Out[14]:
filename methylation_tssDistance methylation_genesDistance methylation_exonsDistance methylation_intronsDistance methylation_promoterDistance methylation_cgiDistance methylation_ctcfDistance methylation_ctcfUpDistance methylation_ctcfDownDistance methylation_geneDistalRegulatoryModulesDistance methylation_vistaEnhancersDistance methylation_3PrimeUTRDistance methylation_5PrimeUTRDistance methylation_firstExonDistance methylation_geneDistalRegulatoryModulesK562Distance methylation_hypoInHues64Distance methylation_intergenic methylation_shore methylation_shelf PDR_tssDistance PDR_genesDistance PDR_exonsDistance PDR_intronsDistance PDR_promoterDistance PDR_cgiDistance PDR_ctcfDistance PDR_ctcfUpDistance PDR_ctcfDownDistance PDR_geneDistalRegulatoryModulesDistance PDR_vistaEnhancersDistance PDR_3PrimeUTRDistance PDR_5PrimeUTRDistance PDR_firstExonDistance PDR_geneDistalRegulatoryModulesK562Distance PDR_hypoInHues64Distance PDR_intergenic PDR_shore PDR_shelf protocol
85 RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.GTTGAG 0.0 0.488591 0.320965 0.515461 0.090361 0.105259 0.121025 0.0 0.121025 0.263410 0.516635 0.618186 0.232372 0.087691 0.179349 0.855401 0.786055 0.541282 0.773497 0.0 0.281265 0.250894 0.285832 0.152622 0.193308 0.192678 0.0 0.192678 0.276641 0.645913 0.369431 0.183830 0.159538 0.183626 0.488850 0.429191 0.356704 0.371891 RRBS_NormalBCD19pCD27pcell67_88
86 RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.TAGCGG 0.0 0.482284 0.313186 0.509034 0.085526 0.094721 0.126048 0.0 0.126048 0.258379 0.331160 0.603295 0.229917 0.074569 0.173552 0.891344 0.781317 0.519032 0.765147 0.0 0.275113 0.244301 0.281934 0.159508 0.186889 0.213556 0.0 0.213556 0.284529 0.364407 0.374666 0.194492 0.150189 0.202991 0.220994 0.416715 0.372722 0.386118 RRBS_NormalBCD19pCD27pcell67_88
87 RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.TATCTC 0.0 0.486434 0.337771 0.509877 0.104169 0.131287 0.145222 0.0 0.145222 0.275499 0.528938 0.594743 0.239506 0.108890 0.177779 0.812648 0.734134 0.536591 0.741314 0.0 0.317291 0.273234 0.322569 0.168070 0.214958 0.197274 0.0 0.197274 0.298599 0.433302 0.385105 0.203708 0.181556 0.193979 0.399823 0.492227 0.388837 0.420926 RRBS_NormalBCD19pCD27pcell67_88
88 RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.TCTCTG 0.0 0.504985 0.354574 0.527785 0.126590 0.162859 0.148021 0.0 0.148021 0.298728 0.675342 0.654135 0.247424 0.123843 0.198148 0.793358 0.753129 0.541053 0.740954 0.0 0.308032 0.268731 0.311831 0.174724 0.213951 0.206692 0.0 0.206692 0.310745 0.335160 0.390648 0.196147 0.168175 0.209597 0.402829 0.469717 0.383694 0.375906 RRBS_NormalBCD19pCD27pcell67_88
89 RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.TGCTGC 0.0 0.461103 0.285016 0.490647 0.061652 0.073687 0.091623 0.0 0.091623 0.224748 0.583333 0.622024 0.196912 0.058647 0.151447 1.000000 0.882502 0.575498 0.874932 0.0 0.166791 0.159804 0.165718 0.099319 0.130943 0.126309 0.0 0.126309 0.179091 0.083333 0.245536 0.094161 0.100645 0.120579 0.000000 0.205599 0.257229 0.231186 RRBS_NormalBCD19pCD27pcell67_88
In [14] used 0.035 MiB RAM in 0.074s, peaked 0.000 MiB above current, total RAM usage 104.391 MiB

In [15]:
mcell["protocol"] = mcell["filename"].str[:31]


In [15] used 0.016 MiB RAM in 0.002s, peaked 0.000 MiB above current, total RAM usage 104.406 MiB

In [16]:
mcell["filename"][mcell["protocol"]=='RRBS_NormalBCD19pCD27mcell1_22_'] = mcell["filename"].str[:46]
mcell["filename"][mcell["protocol"]=='RRBS_NormalBCD19pCD27mcell23_44'] = mcell["filename"].str[:47]
mcell["filename"][mcell["protocol"]=='RRBS_NormalBCD19pCD27mcell45_66'] = mcell["filename"].str[:47]
mcell["filename"][mcell["protocol"]=='RRBS_NormalBCD19pCD27mcell67_88'] = mcell["filename"].str[:47]


/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':
/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  from ipykernel import kernelapp as app
/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  app.launch_new_instance()
/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
In [16] used 0.035 MiB RAM in 0.244s, peaked 0.000 MiB above current, total RAM usage 104.441 MiB

In [17]:
mcell.tail()


Out[17]:
filename methylation_tssDistance methylation_genesDistance methylation_exonsDistance methylation_intronsDistance methylation_promoterDistance methylation_cgiDistance methylation_ctcfDistance methylation_ctcfUpDistance methylation_ctcfDownDistance methylation_geneDistalRegulatoryModulesDistance methylation_vistaEnhancersDistance methylation_3PrimeUTRDistance methylation_5PrimeUTRDistance methylation_firstExonDistance methylation_geneDistalRegulatoryModulesK562Distance methylation_hypoInHues64Distance methylation_intergenic methylation_shore methylation_shelf PDR_tssDistance PDR_genesDistance PDR_exonsDistance PDR_intronsDistance PDR_promoterDistance PDR_cgiDistance PDR_ctcfDistance PDR_ctcfUpDistance PDR_ctcfDownDistance PDR_geneDistalRegulatoryModulesDistance PDR_vistaEnhancersDistance PDR_3PrimeUTRDistance PDR_5PrimeUTRDistance PDR_firstExonDistance PDR_geneDistalRegulatoryModulesK562Distance PDR_hypoInHues64Distance PDR_intergenic PDR_shore PDR_shelf protocol
83 RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.GTGAGG 0.0 0.472029 0.279894 0.507985 0.062184 0.068979 0.099081 0.0 0.099081 0.254673 0.414678 0.646722 0.202133 0.052964 0.180330 0.825413 0.880407 0.567798 0.868663 0.0 0.162036 0.157993 0.162275 0.105846 0.134560 0.144832 0.0 0.144832 0.210529 0.343079 0.229286 0.102900 0.115445 0.160677 0.270661 0.208397 0.252791 0.195733 RRBS_NormalBCD19pCD27mcell67_88
84 RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.GTTGAG 0.0 0.475509 0.284108 0.507053 0.067481 0.074098 0.107121 0.0 0.107121 0.252745 0.376858 0.661143 0.200471 0.058478 0.178968 0.852344 0.884411 0.593522 0.879363 0.0 0.157373 0.152483 0.156989 0.105841 0.133327 0.140077 0.0 0.140077 0.195673 0.159236 0.214858 0.102166 0.103687 0.136311 0.139063 0.208999 0.275148 0.201310 RRBS_NormalBCD19pCD27mcell67_88
85 RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.TAGCGG 0.0 0.465384 0.270854 0.497950 0.070138 0.075311 0.113918 0.0 0.113918 0.265460 0.515006 0.626513 0.197863 0.054605 0.191544 0.873469 0.874056 0.568923 0.844443 0.0 0.165017 0.151922 0.166105 0.117741 0.146015 0.161420 0.0 0.161420 0.211532 0.650660 0.257800 0.104910 0.104068 0.157382 0.273469 0.210667 0.270156 0.202981 RRBS_NormalBCD19pCD27mcell67_88
86 RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.TATCTC 0.0 0.475148 0.283056 0.509076 0.067527 0.076951 0.117951 0.0 0.117951 0.258988 0.298264 0.644925 0.206166 0.057636 0.178186 0.948181 0.881155 0.586620 0.867445 0.0 0.160243 0.152014 0.161938 0.115076 0.146481 0.159352 0.0 0.159352 0.204830 0.377937 0.193244 0.110812 0.107856 0.135752 0.152150 0.215250 0.266874 0.199644 RRBS_NormalBCD19pCD27mcell67_88
87 RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.TCTCTG 0.0 0.428225 0.251018 0.461970 0.061715 0.068326 0.092568 0.0 0.092568 0.211492 0.450980 0.630726 0.171360 0.056249 0.140668 0.892283 0.877888 0.557980 0.854488 0.0 0.159956 0.156530 0.159670 0.108323 0.135232 0.156855 0.0 0.156855 0.199040 0.287582 0.263901 0.103231 0.117227 0.144726 0.075563 0.210384 0.254628 0.219492 RRBS_NormalBCD19pCD27mcell67_88
In [17] used -0.090 MiB RAM in 0.070s, peaked 0.090 MiB above current, total RAM usage 104.352 MiB

In [18]:
len("RRBS_NormalBCD19pcell1_22_")


Out[18]:
26
In [18] used 0.000 MiB RAM in 0.002s, peaked 0.000 MiB above current, total RAM usage 104.352 MiB

In [19]:
cd19cell["protocol"] = cd19cell["filename"].str[:26]


In [19] used 0.016 MiB RAM in 0.003s, peaked 0.000 MiB above current, total RAM usage 104.367 MiB

In [20]:
len('RRBS_NormalBCD19pcell1_22_TAAGGCGA.ACAACC')


Out[20]:
41
In [20] used 0.016 MiB RAM in 0.003s, peaked 0.000 MiB above current, total RAM usage 104.383 MiB

In [21]:
cd19cell["filename"][cd19cell["protocol"]=='RRBS_NormalBCD19pcell1_22_'] = cd19cell["filename"].str[:41]
cd19cell["filename"][cd19cell["protocol"]=='RRBS_NormalBCD19pcell23_44'] = cd19cell["filename"].str[:42]
cd19cell["filename"][cd19cell["protocol"]=='RRBS_NormalBCD19pcell45_66'] = cd19cell["filename"].str[:42]
cd19cell["filename"][cd19cell["protocol"]=='RRBS_NormalBCD19pcell67_88'] = cd19cell["filename"].str[:42]


/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':
/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  from ipykernel import kernelapp as app
/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  app.launch_new_instance()
/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
In [21] used 0.023 MiB RAM in 0.252s, peaked 0.000 MiB above current, total RAM usage 104.406 MiB

In [22]:
cd19cell.tail()


Out[22]:
filename methylation_tssDistance methylation_genesDistance methylation_exonsDistance methylation_intronsDistance methylation_promoterDistance methylation_cgiDistance methylation_ctcfDistance methylation_ctcfUpDistance methylation_ctcfDownDistance methylation_geneDistalRegulatoryModulesDistance methylation_vistaEnhancersDistance methylation_3PrimeUTRDistance methylation_5PrimeUTRDistance methylation_firstExonDistance methylation_geneDistalRegulatoryModulesK562Distance methylation_hypoInHues64Distance methylation_intergenic methylation_shore methylation_shelf PDR_tssDistance PDR_genesDistance PDR_exonsDistance PDR_intronsDistance PDR_promoterDistance PDR_cgiDistance PDR_ctcfDistance PDR_ctcfUpDistance PDR_ctcfDownDistance PDR_geneDistalRegulatoryModulesDistance PDR_vistaEnhancersDistance PDR_3PrimeUTRDistance PDR_5PrimeUTRDistance PDR_firstExonDistance PDR_geneDistalRegulatoryModulesK562Distance PDR_hypoInHues64Distance PDR_intergenic PDR_shore PDR_shelf protocol
84 RRBS_NormalBCD19pcell67_88_TCCTGAGC.GTTGAG 0.0 0.365003 0.198154 0.403464 0.045692 0.049036 0.067145 0.0 0.067145 0.175783 0.170213 0.603492 0.133213 0.039203 0.124610 0.990164 0.857586 0.529461 0.830424 0.0 0.151630 0.135355 0.155304 0.086338 0.115382 0.124663 0.0 0.124663 0.173466 0.045593 0.233981 0.088479 0.087266 0.128772 0.000000 0.238726 0.295577 0.208543 RRBS_NormalBCD19pcell67_88
85 RRBS_NormalBCD19pcell67_88_TCCTGAGC.TAGCGG 0.0 0.427826 0.249504 0.457398 0.062149 0.072737 0.087771 0.0 0.087771 0.207407 0.428877 0.564334 0.176146 0.050720 0.137381 0.662519 0.804080 0.516266 0.779332 0.0 0.224285 0.181686 0.230307 0.109675 0.134092 0.141826 0.0 0.141826 0.200233 0.200000 0.300089 0.122736 0.101102 0.129729 0.395023 0.371977 0.332454 0.371777 RRBS_NormalBCD19pcell67_88
86 RRBS_NormalBCD19pcell67_88_TCCTGAGC.TATCTC 0.0 0.424818 0.246150 0.456708 0.058898 0.070105 0.105839 0.0 0.105839 0.221580 0.363380 0.632963 0.176894 0.050543 0.149377 0.885320 0.825866 0.539768 0.819517 0.0 0.195755 0.165491 0.200067 0.101588 0.130197 0.151039 0.0 0.151039 0.215342 0.302535 0.281348 0.112925 0.096018 0.148490 0.410017 0.335954 0.310619 0.307484 RRBS_NormalBCD19pcell67_88
87 RRBS_NormalBCD19pcell67_88_TCCTGAGC.TCTCTG 0.0 0.472135 0.286358 0.503827 0.066952 0.077275 0.107918 0.0 0.107918 0.244037 0.413347 0.619841 0.199015 0.058952 0.168218 0.894972 0.879557 0.591964 0.864421 0.0 0.157952 0.150639 0.158441 0.108958 0.132628 0.156623 0.0 0.156623 0.199528 0.254980 0.243718 0.096941 0.102134 0.130035 0.138547 0.206872 0.276050 0.196075 RRBS_NormalBCD19pcell67_88
88 RRBS_NormalBCD19pcell67_88_TCCTGAGC.TGCTGC 0.0 0.386120 0.188577 0.424230 0.057304 0.069063 0.110272 0.0 0.110272 0.226848 0.000000 0.601695 0.166264 0.051560 0.174515 0.933333 0.866931 0.560803 0.818182 0.0 0.167532 0.149592 0.180484 0.103296 0.120248 0.193353 0.0 0.193353 0.230246 0.000000 0.152542 0.106332 0.105665 0.193906 0.466667 0.299404 0.284534 0.258182 RRBS_NormalBCD19pcell67_88
In [22] used 0.020 MiB RAM in 0.076s, peaked 0.000 MiB above current, total RAM usage 104.426 MiB

In [23]:
len("RRBS_cw154_Tris_protease_GR")


Out[23]:
27
In [23] used 0.016 MiB RAM in 0.004s, peaked 0.000 MiB above current, total RAM usage 104.441 MiB

In [24]:
cw154["protocol"] = cw154["filename"].str[:27]


In [24] used 0.016 MiB RAM in 0.003s, peaked 0.000 MiB above current, total RAM usage 104.457 MiB

In [25]:
cw154.head()  # RRBS_cw154_CutSmart_protein   # RRBS_cw154_Tris_protease_CT   # RRBS_cw154_Tris_protease_GR


Out[25]:
filename methylation_tssDistance methylation_genesDistance methylation_exonsDistance methylation_intronsDistance methylation_promoterDistance methylation_cgiDistance methylation_ctcfDistance methylation_ctcfUpDistance methylation_ctcfDownDistance methylation_geneDistalRegulatoryModulesDistance methylation_vistaEnhancersDistance methylation_3PrimeUTRDistance methylation_5PrimeUTRDistance methylation_firstExonDistance methylation_geneDistalRegulatoryModulesK562Distance methylation_hypoInHues64Distance methylation_intergenic methylation_shore methylation_shelf PDR_tssDistance PDR_genesDistance PDR_exonsDistance PDR_intronsDistance PDR_promoterDistance PDR_cgiDistance PDR_ctcfDistance PDR_ctcfUpDistance PDR_ctcfDownDistance PDR_geneDistalRegulatoryModulesDistance PDR_vistaEnhancersDistance PDR_3PrimeUTRDistance PDR_5PrimeUTRDistance PDR_firstExonDistance PDR_geneDistalRegulatoryModulesK562Distance PDR_hypoInHues64Distance PDR_intergenic PDR_shore PDR_shelf protocol
0 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACAA... 0.0 0.557445 0.387638 0.583685 0.148984 0.173551 0.197546 0.0 0.197546 0.330378 0.534917 0.659609 0.300969 0.142909 0.246735 0.805699 0.820512 0.590658 0.806973 0.0 0.370149 0.420504 0.359511 0.408136 0.450911 0.436709 0.0 0.436709 0.446102 0.393463 0.409348 0.376109 0.438725 0.388377 0.397393 0.379858 0.378600 0.345222 RRBS_cw154_CutSmart_protein
1 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACCG... 0.0 0.495537 0.324467 0.521993 0.140566 0.157658 0.175575 0.0 0.175575 0.296219 0.366917 0.587535 0.261936 0.137202 0.237040 0.690698 0.796567 0.530634 0.780923 0.0 0.388093 0.435418 0.383256 0.418755 0.458484 0.442186 0.0 0.442186 0.440632 0.840602 0.376294 0.386659 0.445932 0.394716 0.346512 0.418598 0.404386 0.362456 RRBS_cw154_CutSmart_protein
2 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACGT... 0.0 0.520409 0.357209 0.544602 0.138190 0.161856 0.202110 0.0 0.202110 0.314849 0.463004 0.656101 0.275719 0.138640 0.225783 0.761359 0.792970 0.551861 0.785731 0.0 0.383971 0.430167 0.374988 0.410647 0.455464 0.453277 0.0 0.453277 0.450694 0.324865 0.443011 0.387172 0.439933 0.414011 0.280260 0.411414 0.389000 0.363575 RRBS_cw154_CutSmart_protein
3 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACTC... 0.0 0.569906 0.398614 0.595372 0.158028 0.178889 0.196911 0.0 0.196911 0.340942 0.608100 0.665446 0.310607 0.154927 0.247978 0.867355 0.831256 0.609382 0.812445 0.0 0.363173 0.416487 0.354378 0.421272 0.454977 0.438265 0.0 0.438265 0.452257 0.477332 0.376957 0.382624 0.449764 0.400321 0.171406 0.369736 0.385435 0.345905 RRBS_cw154_CutSmart_protein
4 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.AGGA... 0.0 0.554293 0.390193 0.576205 0.148946 0.173784 0.194668 0.0 0.194668 0.339245 0.494894 0.652036 0.291318 0.145185 0.250083 0.894932 0.825017 0.592558 0.807747 0.0 0.365658 0.414198 0.357211 0.407138 0.447926 0.433596 0.0 0.433596 0.428716 0.406863 0.397655 0.376272 0.425466 0.383864 0.274772 0.382212 0.378352 0.346732 RRBS_cw154_CutSmart_protein
In [25] used -0.004 MiB RAM in 0.067s, peaked 0.004 MiB above current, total RAM usage 104.453 MiB

In [26]:
cw154["filename"][cw154["protocol"] == "RRBS_cw154_CutSmart_protein"] = cw154["filename"].str[:48]
cw154["filename"][cw154["protocol"] == "RRBS_cw154_Tris_protease_CT"] = cw154["filename"].str[:40]
cw154["filename"][cw154["protocol"] == "RRBS_cw154_Tris_protease_GR"] = cw154["filename"].str[:43]


In [26] used 0.012 MiB RAM in 0.172s, peaked 0.000 MiB above current, total RAM usage 104.465 MiB
/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':
/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  from ipykernel import kernelapp as app
/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  app.launch_new_instance()

In [27]:
cw154


Out[27]:
filename methylation_tssDistance methylation_genesDistance methylation_exonsDistance methylation_intronsDistance methylation_promoterDistance methylation_cgiDistance methylation_ctcfDistance methylation_ctcfUpDistance methylation_ctcfDownDistance methylation_geneDistalRegulatoryModulesDistance methylation_vistaEnhancersDistance methylation_3PrimeUTRDistance methylation_5PrimeUTRDistance methylation_firstExonDistance methylation_geneDistalRegulatoryModulesK562Distance methylation_hypoInHues64Distance methylation_intergenic methylation_shore methylation_shelf PDR_tssDistance PDR_genesDistance PDR_exonsDistance PDR_intronsDistance PDR_promoterDistance PDR_cgiDistance PDR_ctcfDistance PDR_ctcfUpDistance PDR_ctcfDownDistance PDR_geneDistalRegulatoryModulesDistance PDR_vistaEnhancersDistance PDR_3PrimeUTRDistance PDR_5PrimeUTRDistance PDR_firstExonDistance PDR_geneDistalRegulatoryModulesK562Distance PDR_hypoInHues64Distance PDR_intergenic PDR_shore PDR_shelf protocol
0 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACAACC 0.0 0.557445 0.387638 0.583685 0.148984 0.173551 0.197546 0.0 0.197546 0.330378 0.534917 0.659609 0.300969 0.142909 0.246735 0.805699 0.820512 0.590658 0.806973 0.0 0.370149 0.420504 0.359511 0.408136 0.450911 0.436709 0.0 0.436709 0.446102 0.393463 0.409348 0.376109 0.438725 0.388377 0.397393 0.379858 0.378600 0.345222 RRBS_cw154_CutSmart_protein
1 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACCGCG 0.0 0.495537 0.324467 0.521993 0.140566 0.157658 0.175575 0.0 0.175575 0.296219 0.366917 0.587535 0.261936 0.137202 0.237040 0.690698 0.796567 0.530634 0.780923 0.0 0.388093 0.435418 0.383256 0.418755 0.458484 0.442186 0.0 0.442186 0.440632 0.840602 0.376294 0.386659 0.445932 0.394716 0.346512 0.418598 0.404386 0.362456 RRBS_cw154_CutSmart_protein
2 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACGTGG 0.0 0.520409 0.357209 0.544602 0.138190 0.161856 0.202110 0.0 0.202110 0.314849 0.463004 0.656101 0.275719 0.138640 0.225783 0.761359 0.792970 0.551861 0.785731 0.0 0.383971 0.430167 0.374988 0.410647 0.455464 0.453277 0.0 0.453277 0.450694 0.324865 0.443011 0.387172 0.439933 0.414011 0.280260 0.411414 0.389000 0.363575 RRBS_cw154_CutSmart_protein
3 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACTCAC 0.0 0.569906 0.398614 0.595372 0.158028 0.178889 0.196911 0.0 0.196911 0.340942 0.608100 0.665446 0.310607 0.154927 0.247978 0.867355 0.831256 0.609382 0.812445 0.0 0.363173 0.416487 0.354378 0.421272 0.454977 0.438265 0.0 0.438265 0.452257 0.477332 0.376957 0.382624 0.449764 0.400321 0.171406 0.369736 0.385435 0.345905 RRBS_cw154_CutSmart_protein
4 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.AGGATG 0.0 0.554293 0.390193 0.576205 0.148946 0.173784 0.194668 0.0 0.194668 0.339245 0.494894 0.652036 0.291318 0.145185 0.250083 0.894932 0.825017 0.592558 0.807747 0.0 0.365658 0.414198 0.357211 0.407138 0.447926 0.433596 0.0 0.433596 0.428716 0.406863 0.397655 0.376272 0.425466 0.383864 0.274772 0.382212 0.378352 0.346732 RRBS_cw154_CutSmart_protein
5 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ATAGCG 0.0 0.526884 0.365643 0.552338 0.136827 0.156219 0.186982 0.0 0.186982 0.301051 0.415511 0.644089 0.271955 0.138062 0.216288 0.857859 0.811758 0.577252 0.782210 0.0 0.367963 0.412391 0.359439 0.403460 0.439426 0.423300 0.0 0.423300 0.429729 0.292245 0.380568 0.375672 0.428114 0.370678 0.322551 0.389826 0.380924 0.350955 RRBS_cw154_CutSmart_protein
6 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ATCGAC 0.0 0.565536 0.398508 0.589354 0.153438 0.180336 0.206914 0.0 0.206914 0.347903 0.522757 0.669365 0.309018 0.148969 0.258009 0.820103 0.819525 0.596228 0.804310 0.0 0.369158 0.418102 0.361217 0.417424 0.458302 0.448756 0.0 0.448756 0.443698 0.440532 0.368417 0.382661 0.443897 0.395646 0.354083 0.387312 0.389256 0.340801 RRBS_cw154_CutSmart_protein
7 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.CAAGAG 0.0 0.547472 0.384373 0.572749 0.152863 0.174421 0.196066 0.0 0.196066 0.323417 0.597436 0.672153 0.293574 0.158459 0.240607 0.824585 0.818860 0.588054 0.798634 0.0 0.376000 0.434308 0.366559 0.423294 0.459171 0.451662 0.0 0.451662 0.447380 0.488112 0.390544 0.391449 0.454109 0.397710 0.296812 0.377959 0.384896 0.350755 RRBS_cw154_CutSmart_protein
8 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.CATGAC 0.0 0.560115 0.399275 0.582994 0.152070 0.177982 0.210642 0.0 0.210642 0.340830 0.435017 0.658850 0.302575 0.158123 0.251308 0.843652 0.824098 0.590612 0.803914 0.0 0.373358 0.424900 0.366055 0.417887 0.459681 0.468793 0.0 0.468793 0.450033 0.352024 0.396828 0.387961 0.445253 0.403604 0.319329 0.384551 0.390295 0.359863 RRBS_cw154_CutSmart_protein
9 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.CCTTCG 0.0 0.532219 0.373563 0.554328 0.142907 0.169012 0.183736 0.0 0.183736 0.302616 0.550507 0.615149 0.286602 0.137620 0.221478 0.836297 0.797139 0.569496 0.783430 0.0 0.385187 0.431739 0.377812 0.418669 0.460034 0.465381 0.0 0.465381 0.453603 0.594966 0.418134 0.380153 0.439284 0.396606 0.339958 0.408284 0.396687 0.369618 RRBS_cw154_CutSmart_protein
10 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.CGGTAG 0.0 0.531700 0.370532 0.555725 0.143834 0.165315 0.196658 0.0 0.196658 0.309425 0.623132 0.622519 0.289789 0.155198 0.227672 0.843004 0.805881 0.560993 0.780727 0.0 0.382102 0.430692 0.375433 0.424467 0.459925 0.446492 0.0 0.446492 0.438186 0.285556 0.395191 0.396617 0.457859 0.385372 0.288365 0.385553 0.395152 0.348968 RRBS_cw154_CutSmart_protein
11 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.CTATTG 0.0 0.000000 0.438795 0.584559 0.167596 0.132262 0.247382 0.0 0.247382 0.367269 0.666667 0.802508 0.266462 0.184211 0.438384 NaN 0.864040 0.753557 0.898721 0.0 0.320416 0.364490 0.306668 0.356688 0.364810 0.492147 0.0 0.492147 0.336617 1.000000 0.278997 0.367534 0.408669 0.282828 0.000000 0.255152 0.306773 0.166311 RRBS_cw154_CutSmart_protein
12 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.CTCAGC 0.0 0.541012 0.377389 0.563507 0.145227 0.168764 0.192585 0.0 0.192585 0.316389 0.499417 0.620360 0.294856 0.143099 0.234895 0.807127 0.815787 0.578767 0.792081 0.0 0.376073 0.421432 0.367262 0.419961 0.462968 0.441023 0.0 0.441023 0.437581 0.588098 0.388470 0.389301 0.439037 0.393979 0.358086 0.389474 0.390579 0.352459 RRBS_cw154_CutSmart_protein
13 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.GACACG 0.0 0.539446 0.379285 0.562106 0.151057 0.169830 0.197479 0.0 0.197479 0.319023 0.526829 0.624394 0.298679 0.152752 0.245067 0.817265 0.808146 0.560909 0.785910 0.0 0.383113 0.442335 0.372629 0.432990 0.474104 0.484110 0.0 0.484110 0.456716 0.394038 0.435484 0.392065 0.465344 0.397636 0.453005 0.394112 0.399016 0.354612 RRBS_cw154_CutSmart_protein
14 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.GCATTC 0.0 0.573906 0.406627 0.598475 0.155752 0.181375 0.219125 0.0 0.219125 0.349142 0.461636 0.692945 0.314043 0.149229 0.265561 0.805184 0.818499 0.600889 0.805211 0.0 0.364838 0.413731 0.355293 0.418106 0.451039 0.460955 0.0 0.460955 0.439120 0.427394 0.374650 0.384259 0.436755 0.386565 0.237381 0.388799 0.388247 0.351250 RRBS_cw154_CutSmart_protein
15 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.GCTGCC 0.0 0.539193 0.372853 0.564383 0.150046 0.168813 0.200850 0.0 0.200850 0.317655 0.268741 0.630294 0.293949 0.147488 0.230698 0.821040 0.793269 0.565109 0.775614 0.0 0.393342 0.452026 0.385192 0.444778 0.484272 0.488233 0.0 0.488233 0.459993 0.214993 0.453466 0.409531 0.469308 0.402897 0.556227 0.403487 0.399105 0.359500 RRBS_cw154_CutSmart_protein
16 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.GGCATC 0.0 0.548961 0.381545 0.572243 0.146518 0.163592 0.183910 0.0 0.183910 0.332055 0.649764 0.659449 0.302050 0.137127 0.242075 0.858011 0.802843 0.573498 0.788575 0.0 0.369289 0.415335 0.362727 0.408150 0.450432 0.434240 0.0 0.434240 0.448769 0.330582 0.427089 0.369247 0.423437 0.403369 0.205525 0.403571 0.402985 0.363866 RRBS_cw154_CutSmart_protein
17 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.GTGAGG 0.0 0.517231 0.360581 0.541139 0.144744 0.169206 0.192751 0.0 0.192751 0.305701 0.480723 0.644707 0.272439 0.143972 0.224896 0.918484 0.809293 0.549385 0.774150 0.0 0.384798 0.440085 0.375126 0.427821 0.470287 0.458102 0.0 0.458102 0.450411 0.491777 0.418309 0.398621 0.454455 0.393129 0.153117 0.377603 0.389681 0.372192 RRBS_cw154_CutSmart_protein
18 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.GTTGAG 0.0 0.445185 0.314814 0.459320 0.101895 0.106513 0.175696 0.0 0.175696 0.255117 0.000000 0.678481 0.205315 0.121112 0.152685 1.000000 0.864037 0.541609 0.803819 0.0 0.289698 0.302033 0.274471 0.305291 0.323511 0.320269 0.0 0.320269 0.332025 0.000000 0.272405 0.287926 0.304364 0.244743 0.000000 0.259456 0.350199 0.261936 RRBS_cw154_CutSmart_protein
19 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.TAGCGG 0.0 0.515064 0.355504 0.538392 0.136692 0.160634 0.179352 0.0 0.179352 0.304709 0.444590 0.584831 0.275469 0.136273 0.232486 0.726700 0.784468 0.534703 0.769952 0.0 0.396907 0.440482 0.389549 0.423470 0.462821 0.449771 0.0 0.449771 0.465285 0.426702 0.407070 0.397690 0.445997 0.414601 0.361041 0.422636 0.409871 0.372959 RRBS_cw154_CutSmart_protein
20 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.TATCTC 0.0 0.576698 0.407457 0.601190 0.156286 0.179898 0.203588 0.0 0.203588 0.341264 0.581638 0.672314 0.322060 0.151705 0.253217 0.870145 0.827566 0.609707 0.808661 0.0 0.363719 0.416227 0.355604 0.418422 0.454966 0.438377 0.0 0.438377 0.442940 0.321610 0.394777 0.380926 0.444623 0.400264 0.253117 0.374934 0.378514 0.347429 RRBS_cw154_CutSmart_protein
21 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.TCTCTG 0.0 0.565269 0.398589 0.589572 0.157359 0.178807 0.211161 0.0 0.211161 0.342742 0.563872 0.681485 0.312209 0.155151 0.256075 0.863981 0.828704 0.602805 0.808628 0.0 0.368633 0.420777 0.360477 0.423342 0.463158 0.464180 0.0 0.464180 0.449781 0.310764 0.373593 0.395798 0.454929 0.396916 0.177725 0.372295 0.384154 0.345767 RRBS_cw154_CutSmart_protein
22 RRBS_cw154_Tris_protease_CTCTCTAC.ACAACC 0.0 0.534341 0.373414 0.559693 0.140429 0.159554 0.178100 0.0 0.178100 0.309936 0.497813 0.645147 0.292520 0.143238 0.226691 0.731040 0.808980 0.580810 0.795186 0.0 0.388448 0.444272 0.377688 0.433265 0.469909 0.469338 0.0 0.469338 0.451973 0.651118 0.429110 0.404743 0.471306 0.398084 0.377739 0.394894 0.395843 0.368690 RRBS_cw154_Tris_protease_CT
23 RRBS_cw154_Tris_protease_CTCTCTAC.ACCGCG 0.0 0.460252 0.301662 0.485655 0.123422 0.144838 0.161997 0.0 0.161997 0.258069 0.137171 0.607551 0.216179 0.125607 0.192525 0.893236 0.784792 0.496794 0.751479 0.0 0.393472 0.439482 0.385331 0.422044 0.459844 0.438432 0.0 0.438432 0.456989 0.173419 0.457588 0.402154 0.453262 0.412374 0.324368 0.422718 0.423725 0.343020 RRBS_cw154_Tris_protease_CT
24 RRBS_cw154_Tris_protease_CTCTCTAC.ACGTGG 0.0 0.458037 0.323757 0.482683 0.127488 0.161868 0.176395 0.0 0.176395 0.285677 0.407810 0.584367 0.216456 0.126353 0.201637 0.975253 0.791770 0.517245 0.752050 0.0 0.410130 0.440868 0.407166 0.426673 0.464655 0.448130 0.0 0.448130 0.451854 0.296589 0.413079 0.408591 0.454207 0.418433 0.066367 0.399408 0.404818 0.339397 RRBS_cw154_Tris_protease_CT
25 RRBS_cw154_Tris_protease_CTCTCTAC.ACTCAC 0.0 0.590028 0.422366 0.612746 0.168838 0.192126 0.204903 0.0 0.204903 0.371221 0.733536 0.676370 0.340330 0.165456 0.282602 0.831377 0.816232 0.594412 0.805338 0.0 0.381890 0.444354 0.372424 0.453939 0.496793 0.476430 0.0 0.476430 0.467415 0.134052 0.405989 0.397287 0.473730 0.425922 0.237761 0.393858 0.396729 0.348192 RRBS_cw154_Tris_protease_CT
26 RRBS_cw154_Tris_protease_CTCTCTAC.AGGATG 0.0 0.526552 0.369634 0.553452 0.143346 0.165112 0.189923 0.0 0.189923 0.319121 0.419560 0.650610 0.281031 0.137878 0.234273 0.826387 0.793863 0.560189 0.785121 0.0 0.389957 0.447900 0.378239 0.447429 0.480496 0.498393 0.0 0.498393 0.459329 0.192210 0.399631 0.408654 0.479657 0.409307 0.294912 0.419935 0.403721 0.382693 RRBS_cw154_Tris_protease_CT
27 RRBS_cw154_Tris_protease_CTCTCTAC.ATAGCG 0.0 0.514725 0.364374 0.536571 0.138552 0.163622 0.209144 0.0 0.209144 0.301816 0.432663 0.641328 0.275702 0.138910 0.225859 0.926267 0.798583 0.542398 0.777647 0.0 0.387464 0.435454 0.380125 0.440076 0.477478 0.453723 0.0 0.453723 0.448844 0.455532 0.378974 0.401406 0.462439 0.404434 0.175444 0.404892 0.414161 0.349822 RRBS_cw154_Tris_protease_CT
28 RRBS_cw154_Tris_protease_CTCTCTAC.ATCGAC 0.0 0.558080 0.398185 0.580534 0.151438 0.175596 0.197664 0.0 0.197664 0.334314 0.433680 0.703718 0.302549 0.152055 0.248275 0.877315 0.819819 0.581308 0.796077 0.0 0.380517 0.431675 0.373339 0.438100 0.476924 0.467834 0.0 0.467834 0.464795 0.732005 0.401747 0.402183 0.468532 0.413423 0.445849 0.379814 0.380670 0.343064 RRBS_cw154_Tris_protease_CT
29 RRBS_cw154_Tris_protease_CTCTCTAC.CAAGAG 0.0 0.486270 0.370296 0.502550 0.152896 0.168602 0.205128 0.0 0.205128 0.170190 0.235294 0.743697 0.231379 0.191316 0.100649 0.500000 0.848130 0.564462 0.827768 0.0 0.427703 0.430160 0.436737 0.494695 0.470844 0.458974 0.0 0.458974 0.465425 1.000000 0.340336 0.543755 0.458727 0.457792 0.000000 0.335998 0.348029 0.298770 RRBS_cw154_Tris_protease_CT
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
36 RRBS_cw154_Tris_protease_CTCTCTAC.GCATTC 0.0 0.550686 0.397458 0.573475 0.155206 0.175344 0.197986 0.0 0.197986 0.317136 0.483328 0.689705 0.302586 0.153650 0.237950 0.865758 0.812120 0.583075 0.787242 0.0 0.383840 0.440115 0.373946 0.430963 0.471428 0.460249 0.0 0.460249 0.444606 0.497993 0.406583 0.406167 0.463870 0.403142 0.234360 0.388152 0.397824 0.364322 RRBS_cw154_Tris_protease_CT
37 RRBS_cw154_Tris_protease_CTCTCTAC.GCTGCC 0.0 0.549865 0.375639 0.572812 0.150089 0.162113 0.164505 0.0 0.164505 0.324149 0.608583 0.611432 0.317604 0.143925 0.240400 0.757844 0.797200 0.527989 0.792693 0.0 0.383353 0.438283 0.376650 0.435875 0.472296 0.471156 0.0 0.471156 0.443709 0.592978 0.387363 0.416812 0.445048 0.423542 0.478172 0.389699 0.403168 0.358137 RRBS_cw154_Tris_protease_CT
38 RRBS_cw154_Tris_protease_CTCTCTAC.GGCATC 0.0 0.534038 0.370099 0.556779 0.145719 0.162697 0.180510 0.0 0.180510 0.319885 0.502717 0.643891 0.294115 0.142610 0.227868 0.782086 0.793476 0.560685 0.779066 0.0 0.393307 0.441777 0.386214 0.433309 0.468830 0.459156 0.0 0.459156 0.464283 0.369318 0.423972 0.391096 0.448848 0.413452 0.402209 0.419728 0.410595 0.385529 RRBS_cw154_Tris_protease_CT
39 RRBS_cw154_Tris_protease_CTCTCTAC.GTGAGG 0.0 0.489653 0.326848 0.515329 0.130821 0.151956 0.181042 0.0 0.181042 0.287429 0.652036 0.607564 0.253067 0.128629 0.208924 0.832283 0.789822 0.519845 0.771301 0.0 0.393518 0.435982 0.385103 0.421115 0.461429 0.432756 0.0 0.432756 0.444819 0.511312 0.470527 0.406976 0.454380 0.377418 0.532563 0.409291 0.411337 0.363646 RRBS_cw154_Tris_protease_CT
40 RRBS_cw154_Tris_protease_CTCTCTAC.GTTGAG 0.0 0.531990 0.370032 0.557751 0.135521 0.158191 0.182368 0.0 0.182368 0.308875 0.524319 0.620437 0.279451 0.135809 0.245574 0.858791 0.796981 0.548003 0.795593 0.0 0.378032 0.429048 0.366821 0.420896 0.457147 0.420198 0.0 0.420198 0.441564 0.596819 0.350647 0.385625 0.446510 0.377840 0.322593 0.416306 0.394795 0.360156 RRBS_cw154_Tris_protease_CT
41 RRBS_cw154_Tris_protease_CTCTCTAC.TAGCGG 0.0 0.471778 0.313727 0.499631 0.125400 0.152194 0.185181 0.0 0.185181 0.275724 0.331111 0.528724 0.243965 0.131895 0.194354 0.889034 0.792664 0.523283 0.743746 0.0 0.400202 0.440841 0.390273 0.438782 0.479660 0.487100 0.0 0.487100 0.453398 0.542222 0.474395 0.402119 0.453015 0.411555 0.259791 0.413971 0.416831 0.362602 RRBS_cw154_Tris_protease_CT
42 RRBS_cw154_Tris_protease_CTCTCTAC.TATCTC 0.0 0.516220 0.350940 0.542691 0.144981 0.164118 0.185249 0.0 0.185249 0.308461 0.439266 0.625922 0.269605 0.140071 0.225836 0.873896 0.797049 0.564834 0.796188 0.0 0.392915 0.435968 0.384976 0.433091 0.471331 0.468878 0.0 0.468878 0.454272 0.560982 0.409778 0.400449 0.448436 0.404954 0.250000 0.417221 0.404421 0.371054 RRBS_cw154_Tris_protease_CT
43 RRBS_cw154_Tris_protease_CTCTCTAC.TCTCTG 0.0 0.553577 0.386507 0.579787 0.155410 0.178985 0.208762 0.0 0.208762 0.336380 0.501952 0.654150 0.298292 0.152577 0.246424 0.827148 0.817611 0.585299 0.788970 0.0 0.377694 0.434611 0.366589 0.438043 0.475137 0.472028 0.0 0.472028 0.459645 0.571333 0.377541 0.397031 0.456302 0.423987 0.213718 0.388219 0.394885 0.358565 RRBS_cw154_Tris_protease_CT
44 RRBS_cw154_Tris_protease_GR_CAGAGAGG.ACAACC 0.0 0.576284 0.406097 0.601754 0.161465 0.185395 0.207583 0.0 0.207583 0.357942 0.547005 0.670954 0.324295 0.157820 0.268130 0.846800 0.817763 0.599877 0.789617 0.0 0.385672 0.442893 0.376316 0.443877 0.485673 0.476193 0.0 0.476193 0.477462 0.557266 0.414924 0.405707 0.472460 0.436088 0.193601 0.390099 0.402147 0.356893 RRBS_cw154_Tris_protease_GR
45 RRBS_cw154_Tris_protease_GR_CAGAGAGG.ACCGCG 0.0 0.511334 0.343362 0.525529 0.146702 0.155815 0.161441 0.0 0.161441 0.300844 0.255682 0.604491 0.270828 0.137620 0.232425 0.460251 0.763500 0.494908 0.734817 0.0 0.421948 0.474217 0.419047 0.445339 0.484756 0.396339 0.0 0.396339 0.472072 0.625000 0.475384 0.430449 0.478865 0.442140 0.246862 0.427111 0.408966 0.424868 RRBS_cw154_Tris_protease_GR
46 RRBS_cw154_Tris_protease_GR_CAGAGAGG.ACGTGG 0.0 0.512682 0.345234 0.536809 0.137729 0.160869 0.176882 0.0 0.176882 0.311430 0.473591 0.616956 0.277755 0.130273 0.230467 0.931588 0.793475 0.532694 0.771031 0.0 0.398524 0.452411 0.391642 0.448729 0.485078 0.475244 0.0 0.475244 0.463947 0.347774 0.436047 0.417028 0.471349 0.425034 0.192148 0.416041 0.417826 0.365604 RRBS_cw154_Tris_protease_GR
47 RRBS_cw154_Tris_protease_GR_CAGAGAGG.ACTCAC 0.0 0.573171 0.401849 0.597199 0.151773 0.171875 0.194561 0.0 0.194561 0.345406 0.517198 0.670404 0.319500 0.153398 0.263630 0.815552 0.823303 0.608822 0.800335 0.0 0.378345 0.434331 0.370594 0.440354 0.477316 0.461536 0.0 0.461536 0.463942 0.533343 0.377821 0.398082 0.465740 0.432103 0.403309 0.383423 0.391929 0.353436 RRBS_cw154_Tris_protease_GR
48 RRBS_cw154_Tris_protease_GR_CAGAGAGG.AGGATG 0.0 0.564190 0.400639 0.587070 0.156148 0.178286 0.200963 0.0 0.200963 0.341886 0.720597 0.682988 0.310596 0.151629 0.253827 0.866745 0.815414 0.594260 0.803257 0.0 0.379929 0.434078 0.371444 0.444880 0.483348 0.468416 0.0 0.468416 0.458227 0.361589 0.389223 0.405786 0.467325 0.408632 0.225639 0.388187 0.390956 0.356615 RRBS_cw154_Tris_protease_GR
49 RRBS_cw154_Tris_protease_GR_CAGAGAGG.ATAGCG 0.0 0.511650 0.347418 0.536770 0.130230 0.152742 0.171285 0.0 0.171285 0.301554 0.600641 0.620994 0.268694 0.129413 0.230489 0.721732 0.801653 0.536829 0.779564 0.0 0.389526 0.437767 0.377810 0.426748 0.469372 0.442436 0.0 0.442436 0.460019 0.354316 0.390483 0.394218 0.452053 0.397870 0.406152 0.393092 0.402037 0.364014 RRBS_cw154_Tris_protease_GR
50 RRBS_cw154_Tris_protease_GR_CAGAGAGG.ATCGAC 0.0 0.581265 0.413731 0.604593 0.159955 0.182839 0.200410 0.0 0.200410 0.348518 0.473692 0.685157 0.329431 0.157349 0.261800 0.883827 0.827906 0.608313 0.808906 0.0 0.378523 0.439229 0.367856 0.442772 0.484959 0.491116 0.0 0.491116 0.471417 0.430501 0.409146 0.406994 0.473154 0.427580 0.355927 0.376216 0.398450 0.348651 RRBS_cw154_Tris_protease_GR
51 RRBS_cw154_Tris_protease_GR_CAGAGAGG.CAAGAG 0.0 0.000000 0.372152 0.546730 0.184593 0.188679 0.283105 0.0 0.283105 0.368590 0.000000 0.694737 0.249365 0.219697 0.267257 NaN 0.805740 0.603448 0.860000 0.0 0.369933 0.427426 0.365895 0.447646 0.463819 0.458143 0.0 0.458143 0.495726 0.000000 0.436842 0.409264 0.415909 0.522124 0.000000 0.374869 0.358012 0.230000 RRBS_cw154_Tris_protease_GR
52 RRBS_cw154_Tris_protease_GR_CAGAGAGG.CATGAC 0.0 0.587509 0.432016 0.608034 0.165995 0.187324 0.214616 0.0 0.214616 0.367932 0.518964 0.674795 0.330463 0.168557 0.271944 0.902915 0.821682 0.598753 0.808898 0.0 0.381658 0.436510 0.374043 0.451143 0.490825 0.466760 0.0 0.466760 0.462817 0.547771 0.413982 0.411164 0.476445 0.416048 0.233673 0.385518 0.398958 0.344039 RRBS_cw154_Tris_protease_GR
53 RRBS_cw154_Tris_protease_GR_CAGAGAGG.CCTTCG 0.0 0.516150 0.346647 0.540346 0.143693 0.154591 0.175251 0.0 0.175251 0.310324 0.413022 0.614101 0.274294 0.137531 0.228403 0.769873 0.783656 0.524627 0.766010 0.0 0.392435 0.446851 0.385694 0.434338 0.475932 0.485631 0.0 0.485631 0.436179 0.792247 0.403187 0.403722 0.464367 0.398929 0.258439 0.416008 0.411363 0.378304 RRBS_cw154_Tris_protease_GR
54 RRBS_cw154_Tris_protease_GR_CAGAGAGG.CGGTAG 0.0 0.534594 0.369322 0.558910 0.146821 0.161236 0.192204 0.0 0.192204 0.310096 0.545982 0.604021 0.284603 0.144641 0.235544 0.837650 0.794706 0.546525 0.773418 0.0 0.386344 0.440891 0.378346 0.439248 0.487651 0.475052 0.0 0.475052 0.463789 0.426339 0.398519 0.407179 0.461797 0.431631 0.290587 0.397254 0.397529 0.366067 RRBS_cw154_Tris_protease_GR
55 RRBS_cw154_Tris_protease_GR_CAGAGAGG.CTATTG 0.0 0.506774 0.372387 0.528822 0.145138 0.174562 0.185376 0.0 0.185376 0.313840 0.351714 0.634783 0.260631 0.147495 0.225961 0.888504 0.800684 0.565772 0.791344 0.0 0.397668 0.443187 0.389245 0.434167 0.479863 0.461500 0.0 0.461500 0.466725 0.299837 0.448787 0.405493 0.460125 0.402327 0.284124 0.403780 0.397252 0.388561 RRBS_cw154_Tris_protease_GR
56 RRBS_cw154_Tris_protease_GR_CAGAGAGG.CTCAGC 0.0 0.585109 0.420451 0.608710 0.164237 0.177026 0.200596 0.0 0.200596 0.354298 0.402174 0.652141 0.332674 0.155343 0.274508 0.897694 0.802184 0.584740 0.811600 0.0 0.380717 0.430851 0.372447 0.446459 0.484725 0.452090 0.0 0.452090 0.470944 0.403339 0.398307 0.408462 0.454746 0.447672 0.241869 0.407099 0.410584 0.357457 RRBS_cw154_Tris_protease_GR
57 RRBS_cw154_Tris_protease_GR_CAGAGAGG.GACACG 0.0 0.515384 0.348097 0.540523 0.136907 0.150941 0.169903 0.0 0.169903 0.302160 0.415330 0.628959 0.278453 0.132860 0.220073 0.945615 0.790033 0.517115 0.755147 0.0 0.397503 0.455544 0.389747 0.437309 0.480080 0.467647 0.0 0.467647 0.456722 0.269456 0.408003 0.418366 0.481040 0.413696 0.220401 0.395705 0.414493 0.385641 RRBS_cw154_Tris_protease_GR
58 RRBS_cw154_Tris_protease_GR_CAGAGAGG.GCATTC 0.0 0.608538 0.428746 0.629168 0.173777 0.186020 0.225286 0.0 0.225286 0.365004 0.607946 0.682409 0.362460 0.161610 0.266919 0.943119 0.820956 0.601619 0.811971 0.0 0.373311 0.429765 0.366590 0.449237 0.490146 0.514217 0.0 0.514217 0.463287 0.525091 0.398041 0.400434 0.468651 0.427292 0.251376 0.386496 0.409402 0.348546 RRBS_cw154_Tris_protease_GR
59 RRBS_cw154_Tris_protease_GR_CAGAGAGG.GCTGCC 0.0 0.576122 0.404285 0.598434 0.162122 0.181122 0.220391 0.0 0.220391 0.353660 0.488789 0.695825 0.342339 0.158025 0.267716 0.868908 0.802257 0.574030 0.802627 0.0 0.392724 0.459133 0.382072 0.448174 0.497532 0.473646 0.0 0.473646 0.442382 0.715994 0.419597 0.426912 0.490002 0.393181 0.174790 0.404133 0.416121 0.340402 RRBS_cw154_Tris_protease_GR
60 RRBS_cw154_Tris_protease_GR_CAGAGAGG.GGCATC 0.0 0.567852 0.405896 0.589092 0.159208 0.173028 0.194514 0.0 0.194514 0.339230 0.400992 0.625731 0.327633 0.153576 0.241934 0.824254 0.797284 0.567597 0.778227 0.0 0.393760 0.455299 0.386012 0.451880 0.500343 0.508443 0.0 0.508443 0.480389 0.404090 0.447343 0.421022 0.480479 0.440682 0.414732 0.411504 0.411222 0.373854 RRBS_cw154_Tris_protease_GR
61 RRBS_cw154_Tris_protease_GR_CAGAGAGG.GTGAGG 0.0 0.540579 0.378239 0.563490 0.148783 0.168545 0.180514 0.0 0.180514 0.310731 0.521963 0.667501 0.303415 0.143335 0.227983 0.820113 0.793085 0.542785 0.767958 0.0 0.401265 0.458648 0.394131 0.454753 0.489479 0.463048 0.0 0.463048 0.465206 0.518934 0.391409 0.415603 0.493483 0.414381 0.373337 0.403627 0.402933 0.389869 RRBS_cw154_Tris_protease_GR
62 RRBS_cw154_Tris_protease_GR_CAGAGAGG.GTTGAG 0.0 0.590227 0.420827 0.615305 0.167125 0.186078 0.203812 0.0 0.203812 0.365675 0.464368 0.654491 0.343965 0.176519 0.283145 0.911492 0.808049 0.596742 0.792297 0.0 0.384102 0.433679 0.374276 0.441195 0.482594 0.457740 0.0 0.457740 0.471821 0.675575 0.414101 0.400324 0.461227 0.430853 0.246331 0.398943 0.401963 0.361181 RRBS_cw154_Tris_protease_GR
63 RRBS_cw154_Tris_protease_GR_CAGAGAGG.TAGCGG 0.0 0.551284 0.385638 0.574912 0.152819 0.166841 0.182777 0.0 0.182777 0.317631 0.606152 0.628592 0.322355 0.152436 0.238888 0.879544 0.784437 0.541362 0.761603 0.0 0.389318 0.439817 0.380264 0.431510 0.483114 0.470635 0.0 0.470635 0.455824 0.668417 0.378515 0.399306 0.451436 0.432468 0.149803 0.405760 0.397798 0.373977 RRBS_cw154_Tris_protease_GR
64 RRBS_cw154_Tris_protease_GR_CAGAGAGG.TATCTC 0.0 0.587895 0.412116 0.613305 0.165356 0.185364 0.209044 0.0 0.209044 0.353929 0.580729 0.665446 0.343067 0.159167 0.268219 0.780298 0.816863 0.589350 0.788591 0.0 0.376174 0.435518 0.367302 0.448862 0.487347 0.475819 0.0 0.475819 0.458763 0.523438 0.370440 0.413176 0.476628 0.412619 0.439182 0.387418 0.387567 0.355424 RRBS_cw154_Tris_protease_GR
65 RRBS_cw154_Tris_protease_GR_CAGAGAGG.TCTCTG 0.0 0.563683 0.396403 0.586718 0.154660 0.175256 0.209778 0.0 0.209778 0.336147 0.540323 0.668507 0.315953 0.154404 0.243405 0.791259 0.803866 0.580085 0.803588 0.0 0.383811 0.442727 0.373669 0.431037 0.478234 0.464356 0.0 0.464356 0.461964 0.353719 0.412310 0.396713 0.462184 0.405112 0.475546 0.405538 0.402137 0.342846 RRBS_cw154_Tris_protease_GR

66 rows × 40 columns

In [27] used 0.074 MiB RAM in 0.246s, peaked 0.000 MiB above current, total RAM usage 104.539 MiB

In [28]:
files = [trito, normal, pcell, mcell, cw154, cd19cell]


In [28] used 0.059 MiB RAM in 0.002s, peaked 0.016 MiB above current, total RAM usage 104.598 MiB

In [29]:
total_region_files = pd.concat([trito, normal, pcell, mcell, cw154, cd19cell])


In [29] used 0.188 MiB RAM in 0.011s, peaked 0.000 MiB above current, total RAM usage 104.785 MiB

In [30]:
total_region_files.shape


Out[30]:
(513, 40)
In [30] used 0.016 MiB RAM in 0.003s, peaked 0.000 MiB above current, total RAM usage 104.801 MiB

In [31]:
total_region_files = total_region_files[["filename", "methylation_tssDistance","methylation_genesDistance","methylation_exonsDistance",
                 "methylation_intronsDistance", "methylation_promoterDistance","methylation_cgiDistance",
                 "methylation_ctcfDistance","methylation_ctcfUpDistance","methylation_ctcfDownDistance",
                 "methylation_geneDistalRegulatoryModulesDistance","methylation_vistaEnhancersDistance",
                 "methylation_3PrimeUTRDistance","methylation_5PrimeUTRDistance",
                 "methylation_firstExonDistance","methylation_geneDistalRegulatoryModulesK562Distance",
                 "methylation_hypoInHues64Distance","methylation_intergenic",
                 "methylation_shore","methylation_shelf","PDR_tssDistance",
                 "PDR_genesDistance","PDR_exonsDistance","PDR_intronsDistance", "PDR_promoterDistance",
                 "PDR_cgiDistance","PDR_ctcfDistance","PDR_ctcfUpDistance","PDR_ctcfDownDistance",
                 "PDR_geneDistalRegulatoryModulesDistance","PDR_vistaEnhancersDistance","PDR_3PrimeUTRDistance",
                 "PDR_5PrimeUTRDistance","PDR_firstExonDistance","PDR_geneDistalRegulatoryModulesK562Distance",
                 "PDR_hypoInHues64Distance","PDR_intergenic","PDR_shore","PDR_shelf"]]


In [31] used 0.074 MiB RAM in 0.012s, peaked 0.000 MiB above current, total RAM usage 104.875 MiB

In [35]:
total_region_files = total_region_files.reset_index(drop=True)


In [35] used 0.000 MiB RAM in 0.002s, peaked 0.000 MiB above current, total RAM usage 105.898 MiB

In [37]:
total_region_files[:40]


Out[37]:
filename methylation_tssDistance methylation_genesDistance methylation_exonsDistance methylation_intronsDistance methylation_promoterDistance methylation_cgiDistance methylation_ctcfDistance methylation_ctcfUpDistance methylation_ctcfDownDistance methylation_geneDistalRegulatoryModulesDistance methylation_vistaEnhancersDistance methylation_3PrimeUTRDistance methylation_5PrimeUTRDistance methylation_firstExonDistance methylation_geneDistalRegulatoryModulesK562Distance methylation_hypoInHues64Distance methylation_intergenic methylation_shore methylation_shelf PDR_tssDistance PDR_genesDistance PDR_exonsDistance PDR_intronsDistance PDR_promoterDistance PDR_cgiDistance PDR_ctcfDistance PDR_ctcfUpDistance PDR_ctcfDownDistance PDR_geneDistalRegulatoryModulesDistance PDR_vistaEnhancersDistance PDR_3PrimeUTRDistance PDR_5PrimeUTRDistance PDR_firstExonDistance PDR_geneDistalRegulatoryModulesK562Distance PDR_hypoInHues64Distance PDR_intergenic PDR_shore PDR_shelf
0 RRBS_trito_pool_1_TAAGGCGA.ACAACC 0.0 0.570721 0.404589 0.594704 0.146371 0.169386 0.199815 0.0 0.199815 0.342986 0.444522 0.680480 0.305206 0.145730 0.254209 0.877142 0.820339 0.607671 0.803716 0.0 0.343014 0.371890 0.339596 0.347748 0.388709 0.385297 0.0 0.385297 0.401254 0.509555 0.386285 0.324236 0.367271 0.354046 0.240359 0.386809 0.377907 0.359143
1 RRBS_trito_pool_1_TAAGGCGA.ACGTGG 0.0 0.545781 0.383371 0.568638 0.141545 0.161519 0.191404 0.0 0.191404 0.326140 0.589834 0.670559 0.290196 0.140779 0.240221 0.809942 0.816166 0.573089 0.795932 0.0 0.348110 0.381251 0.341950 0.349891 0.398898 0.415058 0.0 0.415058 0.408417 0.548192 0.382172 0.332749 0.373615 0.359217 0.364148 0.391925 0.386808 0.354120
2 RRBS_trito_pool_1_TAAGGCGA.ACTCAC 0.0 0.564547 0.401760 0.588136 0.148529 0.174413 0.209041 0.0 0.209041 0.346473 0.553062 0.696068 0.296809 0.148360 0.255392 0.795883 0.832812 0.609544 0.812564 0.0 0.338412 0.371890 0.332321 0.351391 0.393829 0.392313 0.0 0.392313 0.412311 0.471703 0.378630 0.327488 0.370494 0.338321 0.334783 0.378580 0.378799 0.353949
3 RRBS_trito_pool_1_TAAGGCGA.AGGATG 0.0 0.567309 0.399934 0.592890 0.143897 0.168936 0.200661 0.0 0.200661 0.342257 0.665920 0.661426 0.308680 0.141673 0.242236 0.787966 0.824659 0.602995 0.799836 0.0 0.342724 0.374419 0.337654 0.346109 0.389718 0.399153 0.0 0.399153 0.405627 0.359189 0.391002 0.324431 0.360431 0.343730 0.304035 0.380413 0.373345 0.347372
4 RRBS_trito_pool_1_TAAGGCGA.ATAGCG 0.0 0.529224 0.367743 0.555131 0.136090 0.156827 0.175426 0.0 0.175426 0.307402 0.479145 0.644411 0.273473 0.134137 0.220729 0.815944 0.808981 0.575050 0.788587 0.0 0.349254 0.376307 0.342617 0.343348 0.388623 0.403861 0.0 0.403861 0.390288 0.471324 0.392438 0.332882 0.358450 0.319824 0.401641 0.398275 0.373236 0.363320
5 RRBS_trito_pool_1_TAAGGCGA.ATCGAC 0.0 0.566031 0.393281 0.591518 0.145246 0.162973 0.199969 0.0 0.199969 0.333682 0.490492 0.667285 0.304792 0.138075 0.248255 0.853356 0.817065 0.601387 0.800064 0.0 0.343104 0.371148 0.338015 0.350522 0.390895 0.405243 0.0 0.405243 0.397617 0.586487 0.367571 0.325332 0.362838 0.333114 0.328968 0.389536 0.375595 0.350909
6 RRBS_trito_pool_1_TAAGGCGA.CAAGAG 0.0 0.566742 0.402345 0.590378 0.152305 0.173906 0.195518 0.0 0.195518 0.346568 0.533040 0.656980 0.304390 0.149536 0.253048 0.805577 0.816897 0.603907 0.803092 0.0 0.350942 0.386671 0.344248 0.364405 0.406190 0.400039 0.0 0.400039 0.421197 0.545560 0.392756 0.338890 0.381441 0.356823 0.307240 0.394851 0.384458 0.357089
7 RRBS_trito_pool_1_TAAGGCGA.CATGAC 0.0 0.566995 0.407400 0.589923 0.148278 0.174522 0.209616 0.0 0.209616 0.337079 0.476748 0.678752 0.300825 0.146025 0.243253 0.863080 0.824824 0.602455 0.801551 0.0 0.345160 0.379410 0.338777 0.353197 0.399885 0.399392 0.0 0.399392 0.398004 0.482805 0.394881 0.334431 0.375372 0.332435 0.345932 0.381070 0.373821 0.350244
8 RRBS_trito_pool_1_TAAGGCGA.CCTTCG 0.0 0.544137 0.377628 0.568732 0.136603 0.163191 0.191971 0.0 0.191971 0.312724 0.428419 0.644483 0.284865 0.139631 0.229110 0.812046 0.810739 0.567608 0.784795 0.0 0.345857 0.376641 0.339524 0.349919 0.393570 0.413810 0.0 0.413810 0.407238 0.334977 0.388436 0.324229 0.367053 0.354882 0.307155 0.397849 0.390108 0.346731
9 RRBS_trito_pool_1_TAAGGCGA.CGGTAG 0.0 0.540051 0.369671 0.565375 0.139633 0.163692 0.198751 0.0 0.198751 0.312778 0.531099 0.642033 0.284431 0.138257 0.223869 0.851775 0.809572 0.567265 0.792353 0.0 0.354063 0.391736 0.346245 0.357297 0.403435 0.414073 0.0 0.414073 0.409215 0.462336 0.404820 0.330088 0.374945 0.334288 0.296450 0.398979 0.390262 0.360787
10 RRBS_trito_pool_1_TAAGGCGA.CTATTG 0.0 0.579098 0.413628 0.602551 0.149996 0.174348 0.206507 0.0 0.206507 0.340917 0.479200 0.681367 0.314310 0.147705 0.250151 0.842639 0.825690 0.610104 0.808571 0.0 0.334826 0.363741 0.331545 0.340101 0.389592 0.384261 0.0 0.384261 0.399187 0.295595 0.361252 0.316965 0.356730 0.331182 0.275036 0.384923 0.370680 0.338867
11 RRBS_trito_pool_1_TAAGGCGA.GACACG 0.0 0.549829 0.379579 0.572968 0.142349 0.163858 0.194206 0.0 0.194206 0.320273 0.511181 0.628813 0.297707 0.137945 0.231191 0.913175 0.808944 0.575022 0.795743 0.0 0.347308 0.375613 0.343617 0.356474 0.398687 0.405318 0.0 0.405318 0.403924 0.414471 0.390100 0.335772 0.368552 0.331528 0.181846 0.396579 0.382919 0.352013
12 RRBS_trito_pool_1_TAAGGCGA.GCATTC 0.0 0.577097 0.411205 0.599875 0.149844 0.176529 0.209171 0.0 0.209171 0.340387 0.512575 0.667962 0.312442 0.150562 0.251066 0.868216 0.824084 0.608142 0.803492 0.0 0.340215 0.377824 0.332149 0.354279 0.402852 0.387265 0.0 0.387265 0.416357 0.373845 0.376155 0.331282 0.370933 0.363017 0.378312 0.390375 0.366399 0.364220
13 RRBS_trito_pool_1_TAAGGCGA.GCTGCC 0.0 0.544259 0.384439 0.564795 0.139736 0.162409 0.176248 0.0 0.176248 0.318136 0.574486 0.634002 0.289043 0.139641 0.223191 0.856793 0.800237 0.554718 0.776976 0.0 0.354565 0.388414 0.347581 0.360744 0.402607 0.390989 0.0 0.390989 0.431375 0.409462 0.385777 0.340581 0.375888 0.372911 0.350551 0.400626 0.404546 0.364278
14 RRBS_trito_pool_1_TAAGGCGA.GGCATC 0.0 0.555539 0.392705 0.579332 0.145652 0.164935 0.184172 0.0 0.184172 0.333482 0.421168 0.661779 0.296269 0.147555 0.241141 0.825601 0.813275 0.589382 0.790102 0.0 0.349269 0.378454 0.343167 0.352282 0.398483 0.383984 0.0 0.383984 0.394837 0.570345 0.396641 0.337457 0.375483 0.341387 0.228666 0.393968 0.384816 0.358044
15 RRBS_trito_pool_1_TAAGGCGA.GTGAGG 0.0 0.530569 0.367508 0.554714 0.134121 0.155524 0.173857 0.0 0.173857 0.315881 0.463581 0.615143 0.280369 0.133060 0.232378 0.794342 0.804363 0.553666 0.777878 0.0 0.358688 0.390493 0.351879 0.361701 0.404384 0.398420 0.0 0.398420 0.422339 0.427769 0.417731 0.341625 0.380159 0.367890 0.326313 0.400965 0.391422 0.373351
16 RRBS_trito_pool_1_TAAGGCGA.GTTGAG 0.0 0.557958 0.396493 0.580842 0.143581 0.165943 0.198223 0.0 0.198223 0.335718 0.511515 0.653486 0.294972 0.142042 0.251258 0.808442 0.821536 0.592957 0.795827 0.0 0.344146 0.371379 0.337804 0.354805 0.397635 0.404505 0.0 0.404505 0.420580 0.415804 0.359175 0.330021 0.365134 0.366449 0.413398 0.375629 0.381035 0.346115
17 RRBS_trito_pool_1_TAAGGCGA.TAGCGG 0.0 0.540878 0.368191 0.564637 0.133724 0.153977 0.166738 0.0 0.166738 0.307876 0.486900 0.632573 0.286827 0.128397 0.230826 0.856077 0.814219 0.577274 0.793247 0.0 0.346260 0.378683 0.339097 0.356597 0.396187 0.381571 0.0 0.381571 0.400928 0.570090 0.362619 0.339981 0.379723 0.344153 0.377799 0.385887 0.384042 0.349956
18 RRBS_trito_pool_1_TAAGGCGA.TATCTC 0.0 0.575676 0.409355 0.600599 0.148591 0.174386 0.194566 0.0 0.194566 0.336279 0.554679 0.662141 0.309579 0.142574 0.240360 0.844196 0.822712 0.607978 0.806099 0.0 0.341342 0.367808 0.337694 0.339362 0.386849 0.380734 0.0 0.380734 0.394130 0.514109 0.395076 0.319727 0.356904 0.318327 0.309309 0.390633 0.376339 0.360353
19 RRBS_trito_pool_1_TAAGGCGA.TCTCTG 0.0 0.573932 0.402771 0.597337 0.153348 0.175088 0.200765 0.0 0.200765 0.347808 0.610443 0.676611 0.311039 0.144939 0.256872 0.854733 0.826905 0.607012 0.802475 0.0 0.342377 0.370775 0.337035 0.356039 0.402495 0.412892 0.0 0.412892 0.408307 0.464521 0.389068 0.330017 0.366829 0.337809 0.241373 0.383198 0.378137 0.345602
20 RRBS_trito_pool_1_TAAGGCGA.TGACAG 0.0 0.567869 0.399077 0.591561 0.148038 0.176237 0.202154 0.0 0.202154 0.343355 0.522213 0.665558 0.303592 0.146534 0.252288 0.796484 0.830742 0.598186 0.806577 0.0 0.345410 0.381678 0.340699 0.363333 0.411799 0.398106 0.0 0.398106 0.406224 0.468663 0.352417 0.345042 0.391606 0.339766 0.370686 0.369564 0.369690 0.345702
21 RRBS_trito_pool_1_TAAGGCGA.TGCTGC 0.0 0.549810 0.381938 0.576183 0.143244 0.162055 0.182065 0.0 0.182065 0.322882 0.522536 0.636949 0.297776 0.142530 0.244198 0.808005 0.807068 0.566512 0.789342 0.0 0.361772 0.392851 0.356422 0.365182 0.413081 0.408349 0.0 0.408349 0.431775 0.422301 0.386494 0.344547 0.390997 0.368241 0.364905 0.392520 0.386162 0.373449
22 RRBS_trito_pool_2_CGTACTAG.ACAACC 0.0 0.571434 0.403317 0.595518 0.150534 0.172412 0.197820 0.0 0.197820 0.335046 0.542060 0.674691 0.311226 0.145333 0.251228 0.782911 0.825876 0.601882 0.804140 0.0 0.356830 0.396188 0.349903 0.388446 0.425970 0.423147 0.0 0.423147 0.414028 0.406933 0.353024 0.363997 0.410352 0.353075 0.404312 0.376888 0.385532 0.352623
23 RRBS_trito_pool_2_CGTACTAG.ACGTGG 0.0 0.547223 0.381861 0.572699 0.142991 0.167937 0.187875 0.0 0.187875 0.322376 0.494384 0.655945 0.290386 0.138317 0.228471 0.824135 0.809657 0.575763 0.782319 0.0 0.367896 0.410155 0.360025 0.392362 0.439690 0.423503 0.0 0.423503 0.424893 0.371724 0.396436 0.364995 0.415006 0.358457 0.266702 0.393868 0.396684 0.361810
24 RRBS_trito_pool_2_CGTACTAG.ACTCAC 0.0 0.574078 0.410369 0.599662 0.154807 0.180722 0.204649 0.0 0.204649 0.346719 0.484916 0.687740 0.314513 0.155080 0.260803 0.812211 0.828639 0.608617 0.812883 0.0 0.360783 0.413536 0.352271 0.396930 0.438502 0.437218 0.0 0.437218 0.434233 0.243128 0.391187 0.371005 0.424675 0.381663 0.401646 0.380932 0.388726 0.364671
25 RRBS_trito_pool_2_CGTACTAG.AGGATG 0.0 0.574464 0.411157 0.598317 0.153244 0.179052 0.205426 0.0 0.205426 0.347422 0.549476 0.674730 0.318179 0.150916 0.255366 0.825079 0.819016 0.600086 0.803518 0.0 0.361346 0.394711 0.356405 0.385541 0.430138 0.435397 0.0 0.435397 0.430261 0.509647 0.397243 0.354870 0.405106 0.376955 0.349346 0.390618 0.387689 0.355591
26 RRBS_trito_pool_2_CGTACTAG.ATAGCG 0.0 0.530317 0.372598 0.555500 0.139056 0.162002 0.188584 0.0 0.188584 0.314593 0.662600 0.651363 0.276445 0.139170 0.226507 0.782103 0.805115 0.577048 0.796431 0.0 0.368334 0.399350 0.363279 0.375962 0.421184 0.422536 0.0 0.422536 0.423652 0.422062 0.390546 0.352241 0.394821 0.362621 0.349869 0.400868 0.397587 0.375378
27 RRBS_trito_pool_2_CGTACTAG.ATCGAC 0.0 0.579706 0.417754 0.601652 0.154766 0.179131 0.214474 0.0 0.214474 0.347695 0.592746 0.693724 0.318722 0.152873 0.251772 0.829067 0.818502 0.603463 0.804070 0.0 0.365534 0.410172 0.359211 0.392262 0.438666 0.432635 0.0 0.432635 0.437134 0.550723 0.406531 0.367066 0.412751 0.381165 0.291126 0.388891 0.385826 0.368804
28 RRBS_trito_pool_2_CGTACTAG.CAAGAG 0.0 0.562102 0.395170 0.587195 0.146861 0.171793 0.193740 0.0 0.193740 0.336475 0.643228 0.644603 0.303090 0.145796 0.249175 0.867900 0.811712 0.594372 0.798705 0.0 0.371383 0.417443 0.364244 0.392824 0.439427 0.424017 0.0 0.424017 0.430895 0.419896 0.406159 0.358254 0.421774 0.375869 0.285465 0.405000 0.385932 0.366543
29 RRBS_trito_pool_2_CGTACTAG.CATGAC 0.0 0.572795 0.406324 0.595511 0.149580 0.173139 0.207084 0.0 0.207084 0.341760 0.582188 0.686507 0.311526 0.145188 0.255092 0.898922 0.822096 0.602356 0.802416 0.0 0.363315 0.403748 0.356727 0.385169 0.426971 0.428521 0.0 0.428521 0.437559 0.666602 0.399967 0.360502 0.402785 0.382317 0.213772 0.390465 0.395524 0.364513
30 RRBS_trito_pool_2_CGTACTAG.CCTTCG 0.0 0.544424 0.372864 0.566449 0.141793 0.161766 0.188699 0.0 0.188699 0.320217 0.520051 0.648292 0.287153 0.134590 0.231480 0.918757 0.808219 0.556881 0.786660 0.0 0.372677 0.409840 0.367423 0.386818 0.428182 0.410043 0.0 0.410043 0.437956 0.431314 0.417184 0.370701 0.403697 0.379120 0.132574 0.402506 0.408092 0.392411
31 RRBS_trito_pool_2_CGTACTAG.CGGTAG 0.0 0.541543 0.377491 0.566615 0.144500 0.166361 0.187541 0.0 0.187541 0.323718 0.570565 0.649134 0.290349 0.139695 0.231105 0.875553 0.805793 0.574939 0.781107 0.0 0.366749 0.406201 0.359577 0.382439 0.427216 0.412182 0.0 0.412182 0.424271 0.326364 0.387866 0.358136 0.400989 0.367082 0.233944 0.405535 0.387747 0.372620
32 RRBS_trito_pool_2_CGTACTAG.CTATTG 0.0 0.571516 0.403810 0.595260 0.147111 0.169791 0.199903 0.0 0.199903 0.345774 0.461720 0.665058 0.308691 0.144474 0.257436 0.833091 0.825356 0.606750 0.808127 0.0 0.352923 0.389431 0.346998 0.367228 0.410854 0.408465 0.0 0.408465 0.420192 0.435325 0.384775 0.344882 0.388064 0.354390 0.322671 0.384292 0.385328 0.353731
33 RRBS_trito_pool_2_CGTACTAG.GACACG 0.0 0.547611 0.384121 0.571736 0.141024 0.160152 0.183884 0.0 0.183884 0.324428 0.516251 0.658554 0.296090 0.142236 0.243037 0.840847 0.810208 0.580500 0.780899 0.0 0.362036 0.396972 0.355362 0.379360 0.423028 0.407819 0.0 0.407819 0.430720 0.413402 0.375936 0.353105 0.395058 0.377523 0.239142 0.397595 0.385070 0.369290
34 RRBS_trito_pool_2_CGTACTAG.GCATTC 0.0 0.574308 0.399926 0.599169 0.146626 0.171425 0.202222 0.0 0.202222 0.339589 0.601239 0.673941 0.311423 0.142003 0.251027 0.900174 0.822302 0.607857 0.804399 0.0 0.350723 0.388557 0.343231 0.379437 0.423172 0.413884 0.0 0.413884 0.430536 0.484843 0.385927 0.351172 0.395395 0.377211 0.260417 0.387313 0.379687 0.352217
35 RRBS_trito_pool_2_CGTACTAG.GCTGCC 0.0 0.557827 0.389318 0.583295 0.146131 0.170323 0.216651 0.0 0.216651 0.332222 0.465542 0.685189 0.303248 0.142679 0.253152 0.925723 0.812384 0.566898 0.789918 0.0 0.364344 0.411073 0.357567 0.386643 0.436352 0.455234 0.0 0.455234 0.427594 0.441989 0.410031 0.357034 0.414984 0.377580 0.122134 0.390103 0.391888 0.356613
36 RRBS_trito_pool_2_CGTACTAG.GGCATC 0.0 0.558443 0.393582 0.581411 0.149267 0.173254 0.202211 0.0 0.202211 0.336075 0.497985 0.658613 0.307422 0.145063 0.253500 0.822348 0.810850 0.579463 0.788819 0.0 0.369847 0.416267 0.362248 0.393373 0.438305 0.432106 0.0 0.432106 0.447392 0.283823 0.414114 0.369362 0.423329 0.390531 0.294508 0.403624 0.394708 0.370958
37 RRBS_trito_pool_2_CGTACTAG.GTGAGG 0.0 0.548763 0.378005 0.573823 0.146951 0.166034 0.189554 0.0 0.189554 0.329531 0.417063 0.656924 0.298588 0.143700 0.237655 0.837979 0.818187 0.578995 0.795407 0.0 0.369274 0.411458 0.362212 0.399234 0.439421 0.426244 0.0 0.426244 0.437661 0.387644 0.379763 0.378844 0.416184 0.393381 0.327526 0.390663 0.404334 0.359301
38 RRBS_trito_pool_2_CGTACTAG.GTTGAG 0.0 0.560388 0.392703 0.585744 0.148877 0.173148 0.191432 0.0 0.191432 0.337372 0.531839 0.657189 0.308189 0.144341 0.245608 0.856433 0.820191 0.594452 0.790093 0.0 0.357508 0.399570 0.348983 0.382424 0.428961 0.426739 0.0 0.426739 0.436677 0.623022 0.378906 0.357031 0.402640 0.380493 0.320648 0.385596 0.378942 0.359409
39 RRBS_trito_pool_2_CGTACTAG.TAGCGG 0.0 0.542448 0.379673 0.565934 0.141666 0.161357 0.190254 0.0 0.190254 0.322077 0.564060 0.661459 0.291782 0.141023 0.236705 0.775427 0.812801 0.584395 0.788413 0.0 0.373111 0.419487 0.363822 0.398524 0.443218 0.429929 0.0 0.429929 0.428916 0.508213 0.389595 0.371629 0.425177 0.379944 0.390322 0.401379 0.390136 0.365038
In [37] used 0.168 MiB RAM in 0.210s, peaked 0.000 MiB above current, total RAM usage 106.270 MiB

In [38]:
stats = pd.read_csv("RRBS_anno_statistics_full_446files_filter50K.csv")


In [38] used 0.020 MiB RAM in 0.008s, peaked 0.000 MiB above current, total RAM usage 106.289 MiB

In [39]:
stats.shape


Out[39]:
(446, 18)
In [39] used 0.000 MiB RAM in 0.003s, peaked 0.000 MiB above current, total RAM usage 106.289 MiB

In [40]:
stats_files = stats.filename


In [40] used 0.000 MiB RAM in 0.002s, peaked 0.000 MiB above current, total RAM usage 106.289 MiB

In [43]:
merged = stats.merge(total_region_files, on='filename')


In [43] used 0.246 MiB RAM in 0.023s, peaked 0.000 MiB above current, total RAM usage 108.641 MiB

In [46]:
merged = merged.drop(['thisMeth', 'mixedReadCount', 'total_reads', 'total_cpg_no_filter', 'total_cpg_gtrthan1',
       'total_cpg_gtrthan38', 'avgReadCpgs_nofilter','avgReadCpgs_lessthan1CpG', 'avgReadCpgs_gtreql3.8CpG', 'bsRate',], axis=1)


In [46] used 0.000 MiB RAM in 0.005s, peaked 0.000 MiB above current, total RAM usage 110.254 MiB

In [47]:
merged


Out[47]:
filename methylation PDR_total methylation_unweighted PDR_unweighted type bio protocol methylation_tssDistance methylation_genesDistance methylation_exonsDistance methylation_intronsDistance methylation_promoterDistance methylation_cgiDistance methylation_ctcfDistance methylation_ctcfUpDistance methylation_ctcfDownDistance methylation_geneDistalRegulatoryModulesDistance methylation_vistaEnhancersDistance methylation_3PrimeUTRDistance methylation_5PrimeUTRDistance methylation_firstExonDistance methylation_geneDistalRegulatoryModulesK562Distance methylation_hypoInHues64Distance methylation_intergenic methylation_shore methylation_shelf PDR_tssDistance PDR_genesDistance PDR_exonsDistance PDR_intronsDistance PDR_promoterDistance PDR_cgiDistance PDR_ctcfDistance PDR_ctcfUpDistance PDR_ctcfDownDistance PDR_geneDistalRegulatoryModulesDistance PDR_vistaEnhancersDistance PDR_3PrimeUTRDistance PDR_5PrimeUTRDistance PDR_firstExonDistance PDR_geneDistalRegulatoryModulesK562Distance PDR_hypoInHues64Distance PDR_intergenic PDR_shore PDR_shelf
0 RRBS_normal_B_cell_A1_24_TAAGGCGA.ACAACC 0.591346 0.259001 0.691996 0.254835 normal normal_B normal_B_cell_A1_24 0.0 0.572922 0.388607 0.597003 0.127235 0.135145 0.178152 0.0 0.178152 0.313712 0.354954 0.728744 0.282305 0.125011 0.230720 0.926937 0.902930 0.637052 0.896802 0.0 0.249665 0.320241 0.237896 0.363833 0.388255 0.419154 0.0 0.419154 0.364421 0.489309 0.214752 0.295134 0.379389 0.352525 0.020851 0.183311 0.285294 0.169240
1 RRBS_normal_B_cell_A1_24_TAAGGCGA.ACCGCG 0.531169 0.411448 0.620106 0.390562 normal normal_B normal_B_cell_A1_24 0.0 0.505145 0.359230 0.526446 0.134970 0.171891 0.198475 0.0 0.198475 0.352037 0.516644 0.625871 0.266546 0.123009 0.233159 0.865658 0.777870 0.528428 0.750640 0.0 0.389613 0.436141 0.383879 0.426575 0.439299 0.424045 0.0 0.424045 0.468612 0.632490 0.413364 0.384389 0.446504 0.426731 0.016904 0.426067 0.413426 0.430125
2 RRBS_normal_B_cell_A1_24_TAAGGCGA.ACGTGG 0.586403 0.278568 0.699736 0.266418 normal normal_B normal_B_cell_A1_24 0.0 0.553568 0.359975 0.583731 0.117959 0.125268 0.176109 0.0 0.176109 0.309668 0.712070 0.718911 0.277957 0.101747 0.227526 0.942223 0.895722 0.618027 0.868715 0.0 0.276292 0.353180 0.264764 0.381896 0.403526 0.431742 0.0 0.431742 0.362307 0.148328 0.312959 0.323942 0.389617 0.349141 0.150021 0.199184 0.311731 0.206646
3 RRBS_normal_B_cell_A1_24_TAAGGCGA.AGGATG 0.628623 0.248006 0.732036 0.240201 normal normal_B normal_B_cell_A1_24 0.0 0.600840 0.392730 0.633467 0.130846 0.134532 0.228113 0.0 0.228113 0.344969 0.738883 0.721101 0.315410 0.110561 0.254307 0.955880 0.916222 0.672012 0.891286 0.0 0.242686 0.329575 0.226780 0.380465 0.395003 0.428452 0.0 0.428452 0.378543 0.293278 0.226409 0.306003 0.391967 0.380095 0.156554 0.167441 0.275408 0.184916
4 RRBS_normal_B_cell_A1_24_TAAGGCGA.ATAGCG 0.568354 0.434929 0.648127 0.425702 normal normal_B normal_B_cell_A1_24 0.0 0.553723 0.424441 0.572004 0.201759 0.254062 0.243405 0.0 0.243405 0.365090 0.703915 0.690610 0.324217 0.225215 0.250693 0.628902 0.744224 0.591154 0.764444 0.0 0.396151 0.413207 0.393634 0.414282 0.434310 0.404922 0.0 0.404922 0.454559 0.520165 0.398875 0.398097 0.431908 0.403162 0.649024 0.500065 0.432751 0.402732
5 RRBS_normal_B_cell_A1_24_TAAGGCGA.ATCGAC 0.622386 0.272543 0.716552 0.270967 normal normal_B normal_B_cell_A1_24 0.0 0.598241 0.400137 0.625556 0.135943 0.142543 0.197461 0.0 0.197461 0.328983 0.561384 0.724674 0.319542 0.123392 0.245854 0.957901 0.896857 0.631902 0.876983 0.0 0.263207 0.329618 0.253472 0.378263 0.392845 0.387199 0.0 0.387199 0.376833 0.406406 0.247637 0.313278 0.389120 0.356423 0.008145 0.210394 0.305067 0.196524
6 RRBS_normal_B_cell_A1_24_TAAGGCGA.CAAGAG 0.580746 0.358441 0.670718 0.348679 normal normal_B normal_B_cell_A1_24 0.0 0.557673 0.393037 0.581964 0.136496 0.154840 0.185085 0.0 0.185085 0.336342 0.548913 0.724639 0.289102 0.134083 0.242662 0.914303 0.839622 0.609142 0.828499 0.0 0.346284 0.400937 0.334616 0.403498 0.421695 0.446341 0.0 0.446341 0.415259 0.457161 0.375382 0.381060 0.431724 0.366805 0.323921 0.349326 0.361848 0.326459
7 RRBS_normal_B_cell_A1_24_TAAGGCGA.CATGAC 0.579873 0.374401 0.668592 0.364613 normal normal_B normal_B_cell_A1_24 0.0 0.555496 0.372118 0.585104 0.130767 0.149758 0.174434 0.0 0.174434 0.325693 0.489904 0.691227 0.303725 0.117398 0.252648 0.877326 0.830718 0.612466 0.819436 0.0 0.352702 0.397442 0.342892 0.398493 0.420841 0.398592 0.0 0.398592 0.433256 0.586659 0.322179 0.351247 0.405937 0.401367 0.420977 0.381797 0.388051 0.361306
8 RRBS_normal_B_cell_A1_24_TAAGGCGA.CGGTAG 0.580833 0.285978 0.701418 0.271634 normal normal_B normal_B_cell_A1_24 0.0 0.547152 0.343528 0.574055 0.117823 0.121103 0.155409 0.0 0.155409 0.304843 0.593398 0.648511 0.280638 0.096895 0.228697 0.986942 0.887179 0.600183 0.863762 0.0 0.280334 0.358519 0.267715 0.392784 0.396120 0.372254 0.0 0.372254 0.399106 0.174794 0.363332 0.337774 0.402650 0.366518 0.060128 0.231404 0.321895 0.210547
9 RRBS_normal_B_cell_A1_24_TAAGGCGA.CTATTG 0.582590 0.427069 0.650146 0.424804 normal normal_B normal_B_cell_A1_24 0.0 0.569780 0.422603 0.591459 0.171423 0.213337 0.222178 0.0 0.222178 0.366234 0.698103 0.685389 0.327695 0.170005 0.263775 0.864895 0.775154 0.610494 0.773103 0.0 0.393549 0.428448 0.389104 0.421122 0.442946 0.424529 0.0 0.424529 0.465571 0.501746 0.424570 0.388460 0.445857 0.430022 0.365360 0.478388 0.410114 0.419770
10 RRBS_normal_B_cell_A1_24_TAAGGCGA.CTCAGC 0.577931 0.441120 0.640678 0.433420 normal normal_B normal_B_cell_A1_24 0.0 0.565528 0.437396 0.585564 0.207370 0.274443 0.235145 0.0 0.235145 0.377706 0.636860 0.669217 0.337121 0.216485 0.247102 0.909352 0.737474 0.578117 0.735609 0.0 0.409112 0.426369 0.406486 0.421935 0.442577 0.428453 0.0 0.428453 0.502348 0.678498 0.373862 0.418496 0.439376 0.451782 0.296798 0.502111 0.430704 0.404902
11 RRBS_normal_B_cell_A1_24_TAAGGCGA.GACACG 0.603615 0.259780 0.705313 0.246132 normal normal_B normal_B_cell_A1_24 0.0 0.568850 0.361969 0.599111 0.122396 0.127940 0.159862 0.0 0.159862 0.320582 0.725028 0.694968 0.304324 0.117228 0.238581 0.971203 0.898648 0.616594 0.874494 0.0 0.259987 0.346939 0.247972 0.376738 0.384038 0.371028 0.0 0.371028 0.370863 0.041421 0.276435 0.305850 0.396597 0.374203 0.122058 0.193916 0.294751 0.178923
12 RRBS_normal_B_cell_A1_24_TAAGGCGA.GCTGCC 0.602191 0.274941 0.696495 0.261564 normal normal_B normal_B_cell_A1_24 0.0 0.569876 0.362369 0.602931 0.119385 0.123399 0.166912 0.0 0.166912 0.312508 0.483566 0.664175 0.295046 0.104887 0.227071 0.888816 0.894996 0.621557 0.873106 0.0 0.270732 0.350990 0.255018 0.379740 0.403785 0.393961 0.0 0.393961 0.374419 0.151892 0.287812 0.327504 0.390677 0.347520 0.373774 0.199908 0.307455 0.208747
13 RRBS_normal_B_cell_A1_24_TAAGGCGA.GGCATC 0.592896 0.309917 0.688324 0.293166 normal normal_B normal_B_cell_A1_24 0.0 0.568737 0.375639 0.597084 0.126210 0.134600 0.173529 0.0 0.173529 0.311749 0.609371 0.670949 0.303669 0.104746 0.230130 0.933383 0.874730 0.619164 0.858757 0.0 0.302826 0.370106 0.290653 0.398204 0.419242 0.411253 0.0 0.411253 0.405264 0.556019 0.334586 0.327526 0.392053 0.379147 0.134353 0.259203 0.328873 0.271502
14 RRBS_normal_B_cell_A1_24_TAAGGCGA.GTGAGG 0.576342 0.269746 0.692209 0.255067 normal normal_B normal_B_cell_A1_24 0.0 0.543913 0.345264 0.578228 0.119943 0.117022 0.186957 0.0 0.186957 0.304374 0.275972 0.686426 0.258585 0.103817 0.243962 0.973117 0.893765 0.601321 0.862469 0.0 0.269974 0.351114 0.255608 0.384957 0.398879 0.421579 0.0 0.421579 0.369955 0.324316 0.242306 0.326748 0.397716 0.360025 0.077235 0.189490 0.295756 0.182461
15 RRBS_normal_B_cell_A1_24_TAAGGCGA.GTTGAG 0.573082 0.434043 0.645717 0.421790 normal normal_B normal_B_cell_A1_24 0.0 0.567695 0.412468 0.587454 0.152377 0.169104 0.213248 0.0 0.213248 0.325752 0.478594 0.672947 0.300417 0.138325 0.267002 0.718321 0.755050 0.585532 0.768680 0.0 0.394381 0.413518 0.390813 0.422574 0.436592 0.393126 0.0 0.393126 0.423319 0.545367 0.442718 0.413598 0.426612 0.399205 0.439942 0.496714 0.422975 0.392376
16 RRBS_normal_B_cell_A1_24_TAAGGCGA.TAGCGG 0.563537 0.344400 0.671286 0.324550 normal normal_B normal_B_cell_A1_24 0.0 0.537509 0.353348 0.562371 0.118371 0.130877 0.170794 0.0 0.170794 0.303084 0.460163 0.660977 0.281453 0.108285 0.226112 0.777019 0.856849 0.597661 0.836136 0.0 0.334426 0.403578 0.325768 0.395605 0.424208 0.433552 0.0 0.433552 0.385425 0.504680 0.365191 0.353273 0.424782 0.364329 0.507417 0.312744 0.359828 0.303938
17 RRBS_normal_B_cell_A1_24_TAAGGCGA.TATCTC 0.592870 0.383162 0.663549 0.384798 normal normal_B normal_B_cell_A1_24 0.0 0.574181 0.422529 0.596448 0.157292 0.185985 0.197790 0.0 0.197790 0.333505 0.549745 0.695153 0.306368 0.162632 0.244439 0.958932 0.817648 0.615515 0.796791 0.0 0.359375 0.390368 0.355666 0.395329 0.415846 0.400304 0.0 0.400304 0.420615 0.631283 0.386431 0.353516 0.402680 0.372923 0.120302 0.406370 0.387130 0.361715
18 RRBS_normal_B_cell_A1_24_TAAGGCGA.TCTCTG 0.566829 0.459303 0.621230 0.456004 normal normal_B normal_B_cell_A1_24 0.0 0.561735 0.445770 0.575198 0.219102 0.270627 0.262653 0.0 0.262653 0.388225 0.425444 0.658489 0.339626 0.221938 0.268746 0.693445 0.707242 0.582715 0.734725 0.0 0.424692 0.428625 0.423983 0.416932 0.438851 0.435457 0.0 0.435457 0.458630 0.506799 0.461580 0.424516 0.410753 0.402906 0.446482 0.535373 0.447916 0.449113
19 RRBS_normal_B_cell_A1_24_TAAGGCGA.TGACAG 0.572760 0.339617 0.670456 0.330762 normal normal_B normal_B_cell_A1_24 0.0 0.545209 0.366002 0.571545 0.129616 0.136915 0.184688 0.0 0.184688 0.302508 0.516610 0.667369 0.283649 0.109472 0.226291 0.815052 0.858191 0.595584 0.824065 0.0 0.328561 0.362106 0.322815 0.385218 0.409109 0.420144 0.0 0.420144 0.416980 0.540203 0.342566 0.359376 0.387962 0.391970 0.426135 0.310284 0.363570 0.329527
20 RRBS_normal_B_cell_B1_24_CGTACTAG.ACAACC 0.626281 0.266847 0.723172 0.246680 normal normal_B normal_B_cell_B1_24 0.0 0.600708 0.393908 0.630897 0.130491 0.132780 0.202374 0.0 0.202374 0.352837 0.713675 0.725713 0.320354 0.106622 0.268270 0.927542 0.898352 0.640993 0.877308 0.0 0.259897 0.334198 0.248877 0.382296 0.401650 0.425809 0.0 0.425809 0.382371 0.255144 0.252366 0.309392 0.380196 0.367431 0.180845 0.201513 0.301175 0.213756
21 RRBS_normal_B_cell_B1_24_CGTACTAG.ACCGCG 0.537494 0.432684 0.620718 0.409340 normal normal_B normal_B_cell_B1_24 0.0 0.519674 0.378490 0.541383 0.159885 0.190980 0.207475 0.0 0.207475 0.318937 0.704963 0.615859 0.290296 0.155550 0.229599 0.820416 0.733765 0.532819 0.736470 0.0 0.398962 0.428732 0.396038 0.410177 0.437335 0.420623 0.0 0.420623 0.464244 0.680829 0.429482 0.377868 0.416697 0.435796 0.262760 0.487506 0.420668 0.395321
22 RRBS_normal_B_cell_B1_24_CGTACTAG.ACTCAC 0.641663 0.246022 0.731753 0.227309 normal normal_B normal_B_cell_B1_24 0.0 0.612460 0.404062 0.641619 0.138321 0.134888 0.200057 0.0 0.200057 0.365257 0.464153 0.743855 0.331324 0.115728 0.271711 0.888724 0.914558 0.655884 0.902320 0.0 0.242492 0.329096 0.230329 0.387293 0.406756 0.409925 0.0 0.409925 0.381945 0.411628 0.222693 0.307246 0.402911 0.373759 0.088080 0.165203 0.278304 0.167580
23 RRBS_normal_B_cell_B1_24_CGTACTAG.ATAGCG 0.589376 0.261165 0.710628 0.230766 normal normal_B normal_B_cell_B1_24 0.0 0.564847 0.359529 0.595146 0.118704 0.114490 0.173252 0.0 0.173252 0.310655 0.503806 0.691586 0.285765 0.103678 0.228965 0.970596 0.901415 0.619064 0.889049 0.0 0.258756 0.349989 0.242980 0.378504 0.394998 0.419274 0.0 0.419274 0.350099 0.564988 0.274140 0.311913 0.397878 0.335510 0.049096 0.181269 0.296877 0.184334
24 RRBS_normal_B_cell_B1_24_CGTACTAG.CAAGAG 0.573636 0.410016 0.649119 0.394524 normal normal_B normal_B_cell_B1_24 0.0 0.558144 0.410151 0.580173 0.160099 0.195220 0.200590 0.0 0.200590 0.349321 0.549857 0.668894 0.309413 0.163017 0.243278 0.919356 0.784926 0.568173 0.761311 0.0 0.386441 0.419907 0.381549 0.411113 0.441415 0.406298 0.0 0.406298 0.448031 0.547251 0.410927 0.394507 0.435718 0.393209 0.308112 0.437090 0.407888 0.396319
25 RRBS_normal_B_cell_B1_24_CGTACTAG.CATGAC 0.624309 0.250108 0.726315 0.228687 normal normal_B normal_B_cell_B1_24 0.0 0.596158 0.388236 0.627931 0.130745 0.131519 0.172315 0.0 0.172315 0.338750 0.456898 0.722962 0.315504 0.112215 0.257224 0.969047 0.914006 0.639293 0.894982 0.0 0.248352 0.341477 0.232416 0.376812 0.406716 0.399623 0.0 0.399623 0.368001 0.332834 0.242602 0.312927 0.398130 0.356175 0.101891 0.168523 0.281624 0.169076
26 RRBS_normal_B_cell_B1_24_CGTACTAG.CCTTCG 0.596452 0.329267 0.698844 0.306790 normal normal_B normal_B_cell_B1_24 0.0 0.571273 0.377748 0.599836 0.130880 0.142904 0.178021 0.0 0.178021 0.331112 0.590600 0.684578 0.300336 0.120516 0.255226 0.910927 0.858774 0.607147 0.851038 0.0 0.313604 0.385012 0.301194 0.409911 0.429555 0.440444 0.0 0.440444 0.411064 0.403216 0.356594 0.351884 0.427722 0.393187 0.197940 0.299706 0.348462 0.263281
27 RRBS_normal_B_cell_B1_24_CGTACTAG.CGGTAG 0.525054 0.433851 0.609985 0.410107 normal normal_B normal_B_cell_B1_24 0.0 0.507179 0.348300 0.529348 0.131065 0.146562 0.159320 0.0 0.159320 0.291927 0.585224 0.598317 0.271001 0.122086 0.204311 0.831863 0.748557 0.519638 0.731533 0.0 0.407883 0.431943 0.402297 0.405059 0.427618 0.414400 0.0 0.414400 0.426879 0.483459 0.421985 0.395431 0.423924 0.373547 0.410587 0.484246 0.438821 0.430328
28 RRBS_normal_B_cell_B1_24_CGTACTAG.CTATTG 0.593262 0.424328 0.660035 0.416186 normal normal_B normal_B_cell_B1_24 0.0 0.579434 0.434890 0.600699 0.188624 0.233725 0.230526 0.0 0.230526 0.366878 0.612085 0.672638 0.334540 0.186837 0.249289 0.849944 0.774006 0.594751 0.778275 0.0 0.393161 0.420478 0.388782 0.413085 0.445061 0.400803 0.0 0.400803 0.437013 0.527192 0.421103 0.396852 0.430744 0.381902 0.351360 0.469681 0.420649 0.397929
29 RRBS_normal_B_cell_B1_24_CGTACTAG.CTCAGC 0.615582 0.270868 0.718351 0.245807 normal normal_B normal_B_cell_B1_24 0.0 0.584390 0.381342 0.614639 0.128080 0.134331 0.181411 0.0 0.181411 0.326547 0.521315 0.699421 0.307323 0.114798 0.250506 0.958901 0.900439 0.617142 0.875827 0.0 0.267871 0.351355 0.252870 0.389412 0.415080 0.433702 0.0 0.433702 0.390515 0.388477 0.260598 0.320489 0.397438 0.368093 0.046859 0.194196 0.307630 0.192950
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
416 RRBS_trito_pool_1_TAAGGCGA.GCTGCC 0.565787 0.379120 0.636582 0.361267 CLL CLL trito_pool_1 0.0 0.544259 0.384439 0.564795 0.139736 0.162409 0.176248 0.0 0.176248 0.318136 0.574486 0.634002 0.289043 0.139641 0.223191 0.856793 0.800237 0.554718 0.776976 0.0 0.354565 0.388414 0.347581 0.360744 0.402607 0.390989 0.0 0.390989 0.431375 0.409462 0.385777 0.340581 0.375888 0.372911 0.350551 0.400626 0.404546 0.364278
417 RRBS_trito_pool_1_TAAGGCGA.GGCATC 0.578365 0.372803 0.639499 0.362328 CLL CLL trito_pool_1 0.0 0.555539 0.392705 0.579332 0.145652 0.164935 0.184172 0.0 0.184172 0.333482 0.421168 0.661779 0.296269 0.147555 0.241141 0.825601 0.813275 0.589382 0.790102 0.0 0.349269 0.378454 0.343167 0.352282 0.398483 0.383984 0.0 0.383984 0.394837 0.570345 0.396641 0.337457 0.375483 0.341387 0.228666 0.393968 0.384816 0.358044
418 RRBS_trito_pool_1_TAAGGCGA.GTGAGG 0.554707 0.379959 0.630143 0.365870 CLL CLL trito_pool_1 0.0 0.530569 0.367508 0.554714 0.134121 0.155524 0.173857 0.0 0.173857 0.315881 0.463581 0.615143 0.280369 0.133060 0.232378 0.794342 0.804363 0.553666 0.777878 0.0 0.358688 0.390493 0.351879 0.361701 0.404384 0.398420 0.0 0.398420 0.422339 0.427769 0.417731 0.341625 0.380159 0.367890 0.326313 0.400965 0.391422 0.373351
419 RRBS_trito_pool_1_TAAGGCGA.GTTGAG 0.581650 0.365969 0.651666 0.354063 CLL CLL trito_pool_1 0.0 0.557958 0.396493 0.580842 0.143581 0.165943 0.198223 0.0 0.198223 0.335718 0.511515 0.653486 0.294972 0.142042 0.251258 0.808442 0.821536 0.592957 0.795827 0.0 0.344146 0.371379 0.337804 0.354805 0.397635 0.404505 0.0 0.404505 0.420580 0.415804 0.359175 0.330021 0.365134 0.366449 0.413398 0.375629 0.381035 0.346115
420 RRBS_trito_pool_1_TAAGGCGA.TAGCGG 0.564165 0.368605 0.635522 0.354078 CLL CLL trito_pool_1 0.0 0.540878 0.368191 0.564637 0.133724 0.153977 0.166738 0.0 0.166738 0.307876 0.486900 0.632573 0.286827 0.128397 0.230826 0.856077 0.814219 0.577274 0.793247 0.0 0.346260 0.378683 0.339097 0.356597 0.396187 0.381571 0.0 0.381571 0.400928 0.570090 0.362619 0.339981 0.379723 0.344153 0.377799 0.385887 0.384042 0.349956
421 RRBS_trito_pool_1_TAAGGCGA.TATCTC 0.598086 0.365747 0.640908 0.360269 CLL CLL trito_pool_1 0.0 0.575676 0.409355 0.600599 0.148591 0.174386 0.194566 0.0 0.194566 0.336279 0.554679 0.662141 0.309579 0.142574 0.240360 0.844196 0.822712 0.607978 0.806099 0.0 0.341342 0.367808 0.337694 0.339362 0.386849 0.380734 0.0 0.380734 0.394130 0.514109 0.395076 0.319727 0.356904 0.318327 0.309309 0.390633 0.376339 0.360353
422 RRBS_trito_pool_1_TAAGGCGA.TCTCTG 0.598637 0.367210 0.649959 0.358410 CLL CLL trito_pool_1 0.0 0.573932 0.402771 0.597337 0.153348 0.175088 0.200765 0.0 0.200765 0.347808 0.610443 0.676611 0.311039 0.144939 0.256872 0.854733 0.826905 0.607012 0.802475 0.0 0.342377 0.370775 0.337035 0.356039 0.402495 0.412892 0.0 0.412892 0.408307 0.464521 0.389068 0.330017 0.366829 0.337809 0.241373 0.383198 0.378137 0.345602
423 RRBS_trito_pool_1_TAAGGCGA.TGACAG 0.592655 0.364070 0.651817 0.350381 CLL CLL trito_pool_1 0.0 0.567869 0.399077 0.591561 0.148038 0.176237 0.202154 0.0 0.202154 0.343355 0.522213 0.665558 0.303592 0.146534 0.252288 0.796484 0.830742 0.598186 0.806577 0.0 0.345410 0.381678 0.340699 0.363333 0.411799 0.398106 0.0 0.398106 0.406224 0.468663 0.352417 0.345042 0.391606 0.339766 0.370686 0.369564 0.369690 0.345702
424 RRBS_trito_pool_1_TAAGGCGA.TGCTGC 0.572118 0.380804 0.638604 0.366845 CLL CLL trito_pool_1 0.0 0.549810 0.381938 0.576183 0.143244 0.162055 0.182065 0.0 0.182065 0.322882 0.522536 0.636949 0.297776 0.142530 0.244198 0.808005 0.807068 0.566512 0.789342 0.0 0.361772 0.392851 0.356422 0.365182 0.413081 0.408349 0.0 0.408349 0.431775 0.422301 0.386494 0.344547 0.390997 0.368241 0.364905 0.392520 0.386162 0.373449
425 RRBS_trito_pool_2_CGTACTAG.ACAACC 0.593766 0.374765 0.655123 0.357956 CLL CLL trito_pool_2 0.0 0.571434 0.403317 0.595518 0.150534 0.172412 0.197820 0.0 0.197820 0.335046 0.542060 0.674691 0.311226 0.145333 0.251228 0.782911 0.825876 0.601882 0.804140 0.0 0.356830 0.396188 0.349903 0.388446 0.425970 0.423147 0.0 0.423147 0.414028 0.406933 0.353024 0.363997 0.410352 0.353075 0.404312 0.376888 0.385532 0.352623
426 RRBS_trito_pool_2_CGTACTAG.ACGTGG 0.569259 0.388716 0.648626 0.369196 CLL CLL trito_pool_2 0.0 0.547223 0.381861 0.572699 0.142991 0.167937 0.187875 0.0 0.187875 0.322376 0.494384 0.655945 0.290386 0.138317 0.228471 0.824135 0.809657 0.575763 0.782319 0.0 0.367896 0.410155 0.360025 0.392362 0.439690 0.423503 0.0 0.423503 0.424893 0.371724 0.396436 0.364995 0.415006 0.358457 0.266702 0.393868 0.396684 0.361810
427 RRBS_trito_pool_2_CGTACTAG.ACTCAC 0.595647 0.380070 0.652962 0.364404 CLL CLL trito_pool_2 0.0 0.574078 0.410369 0.599662 0.154807 0.180722 0.204649 0.0 0.204649 0.346719 0.484916 0.687740 0.314513 0.155080 0.260803 0.812211 0.828639 0.608617 0.812883 0.0 0.360783 0.413536 0.352271 0.396930 0.438502 0.437218 0.0 0.437218 0.434233 0.243128 0.391187 0.371005 0.424675 0.381663 0.401646 0.380932 0.388726 0.364671
428 RRBS_trito_pool_2_CGTACTAG.AGGATG 0.595616 0.384004 0.658358 0.369689 CLL CLL trito_pool_2 0.0 0.574464 0.411157 0.598317 0.153244 0.179052 0.205426 0.0 0.205426 0.347422 0.549476 0.674730 0.318179 0.150916 0.255366 0.825079 0.819016 0.600086 0.803518 0.0 0.361346 0.394711 0.356405 0.385541 0.430138 0.435397 0.0 0.435397 0.430261 0.509647 0.397243 0.354870 0.405106 0.376955 0.349346 0.390618 0.387689 0.355591
429 RRBS_trito_pool_2_CGTACTAG.ATAGCG 0.554055 0.387759 0.635741 0.364157 CLL CLL trito_pool_2 0.0 0.530317 0.372598 0.555500 0.139056 0.162002 0.188584 0.0 0.188584 0.314593 0.662600 0.651363 0.276445 0.139170 0.226507 0.782103 0.805115 0.577048 0.796431 0.0 0.368334 0.399350 0.363279 0.375962 0.421184 0.422536 0.0 0.422536 0.423652 0.422062 0.390546 0.352241 0.394821 0.362621 0.349869 0.400868 0.397587 0.375378
430 RRBS_trito_pool_2_CGTACTAG.ATCGAC 0.598233 0.384663 0.661048 0.366513 CLL CLL trito_pool_2 0.0 0.579706 0.417754 0.601652 0.154766 0.179131 0.214474 0.0 0.214474 0.347695 0.592746 0.693724 0.318722 0.152873 0.251772 0.829067 0.818502 0.603463 0.804070 0.0 0.365534 0.410172 0.359211 0.392262 0.438666 0.432635 0.0 0.432635 0.437134 0.550723 0.406531 0.367066 0.412751 0.381165 0.291126 0.388891 0.385826 0.368804
431 RRBS_trito_pool_2_CGTACTAG.CAAGAG 0.583609 0.393008 0.649029 0.373233 CLL CLL trito_pool_2 0.0 0.562102 0.395170 0.587195 0.146861 0.171793 0.193740 0.0 0.193740 0.336475 0.643228 0.644603 0.303090 0.145796 0.249175 0.867900 0.811712 0.594372 0.798705 0.0 0.371383 0.417443 0.364244 0.392824 0.439427 0.424017 0.0 0.424017 0.430895 0.419896 0.406159 0.358254 0.421774 0.375869 0.285465 0.405000 0.385932 0.366543
432 RRBS_trito_pool_2_CGTACTAG.CATGAC 0.593843 0.383170 0.659853 0.366034 CLL CLL trito_pool_2 0.0 0.572795 0.406324 0.595511 0.149580 0.173139 0.207084 0.0 0.207084 0.341760 0.582188 0.686507 0.311526 0.145188 0.255092 0.898922 0.822096 0.602356 0.802416 0.0 0.363315 0.403748 0.356727 0.385169 0.426971 0.428521 0.0 0.428521 0.437559 0.666602 0.399967 0.360502 0.402785 0.382317 0.213772 0.390465 0.395524 0.364513
433 RRBS_trito_pool_2_CGTACTAG.CCTTCG 0.568050 0.392622 0.647402 0.371530 CLL CLL trito_pool_2 0.0 0.544424 0.372864 0.566449 0.141793 0.161766 0.188699 0.0 0.188699 0.320217 0.520051 0.648292 0.287153 0.134590 0.231480 0.918757 0.808219 0.556881 0.786660 0.0 0.372677 0.409840 0.367423 0.386818 0.428182 0.410043 0.0 0.410043 0.437956 0.431314 0.417184 0.370701 0.403697 0.379120 0.132574 0.402506 0.408092 0.392411
434 RRBS_trito_pool_2_CGTACTAG.CGGTAG 0.569010 0.390108 0.645339 0.372348 CLL CLL trito_pool_2 0.0 0.541543 0.377491 0.566615 0.144500 0.166361 0.187541 0.0 0.187541 0.323718 0.570565 0.649134 0.290349 0.139695 0.231105 0.875553 0.805793 0.574939 0.781107 0.0 0.366749 0.406201 0.359577 0.382439 0.427216 0.412182 0.0 0.412182 0.424271 0.326364 0.387866 0.358136 0.400989 0.367082 0.233944 0.405535 0.387747 0.372620
435 RRBS_trito_pool_2_CGTACTAG.CTATTG 0.595037 0.373701 0.659609 0.357034 CLL CLL trito_pool_2 0.0 0.571516 0.403810 0.595260 0.147111 0.169791 0.199903 0.0 0.199903 0.345774 0.461720 0.665058 0.308691 0.144474 0.257436 0.833091 0.825356 0.606750 0.808127 0.0 0.352923 0.389431 0.346998 0.367228 0.410854 0.408465 0.0 0.408465 0.420192 0.435325 0.384775 0.344882 0.388064 0.354390 0.322671 0.384292 0.385328 0.353731
436 RRBS_trito_pool_2_CGTACTAG.GACACG 0.570886 0.384283 0.645343 0.367303 CLL CLL trito_pool_2 0.0 0.547611 0.384121 0.571736 0.141024 0.160152 0.183884 0.0 0.183884 0.324428 0.516251 0.658554 0.296090 0.142236 0.243037 0.840847 0.810208 0.580500 0.780899 0.0 0.362036 0.396972 0.355362 0.379360 0.423028 0.407819 0.0 0.407819 0.430720 0.413402 0.375936 0.353105 0.395058 0.377523 0.239142 0.397595 0.385070 0.369290
437 RRBS_trito_pool_2_CGTACTAG.GCATTC 0.596410 0.375766 0.656557 0.357551 CLL CLL trito_pool_2 0.0 0.574308 0.399926 0.599169 0.146626 0.171425 0.202222 0.0 0.202222 0.339589 0.601239 0.673941 0.311423 0.142003 0.251027 0.900174 0.822302 0.607857 0.804399 0.0 0.350723 0.388557 0.343231 0.379437 0.423172 0.413884 0.0 0.413884 0.430536 0.484843 0.385927 0.351172 0.395395 0.377211 0.260417 0.387313 0.379687 0.352217
438 RRBS_trito_pool_2_CGTACTAG.GCTGCC 0.583331 0.384057 0.650519 0.361400 CLL CLL trito_pool_2 0.0 0.557827 0.389318 0.583295 0.146131 0.170323 0.216651 0.0 0.216651 0.332222 0.465542 0.685189 0.303248 0.142679 0.253152 0.925723 0.812384 0.566898 0.789918 0.0 0.364344 0.411073 0.357567 0.386643 0.436352 0.455234 0.0 0.455234 0.427594 0.441989 0.410031 0.357034 0.414984 0.377580 0.122134 0.390103 0.391888 0.356613
439 RRBS_trito_pool_2_CGTACTAG.GGCATC 0.583463 0.392946 0.648295 0.372514 CLL CLL trito_pool_2 0.0 0.558443 0.393582 0.581411 0.149267 0.173254 0.202211 0.0 0.202211 0.336075 0.497985 0.658613 0.307422 0.145063 0.253500 0.822348 0.810850 0.579463 0.788819 0.0 0.369847 0.416267 0.362248 0.393373 0.438305 0.432106 0.0 0.432106 0.447392 0.283823 0.414114 0.369362 0.423329 0.390531 0.294508 0.403624 0.394708 0.370958
440 RRBS_trito_pool_2_CGTACTAG.GTGAGG 0.572670 0.387705 0.655986 0.366706 CLL CLL trito_pool_2 0.0 0.548763 0.378005 0.573823 0.146951 0.166034 0.189554 0.0 0.189554 0.329531 0.417063 0.656924 0.298588 0.143700 0.237655 0.837979 0.818187 0.578995 0.795407 0.0 0.369274 0.411458 0.362212 0.399234 0.439421 0.426244 0.0 0.426244 0.437661 0.387644 0.379763 0.378844 0.416184 0.393381 0.327526 0.390663 0.404334 0.359301
441 RRBS_trito_pool_2_CGTACTAG.GTTGAG 0.584506 0.379222 0.659633 0.362664 CLL CLL trito_pool_2 0.0 0.560388 0.392703 0.585744 0.148877 0.173148 0.191432 0.0 0.191432 0.337372 0.531839 0.657189 0.308189 0.144341 0.245608 0.856433 0.820191 0.594452 0.790093 0.0 0.357508 0.399570 0.348983 0.382424 0.428961 0.426739 0.0 0.426739 0.436677 0.623022 0.378906 0.357031 0.402640 0.380493 0.320648 0.385596 0.378942 0.359409
442 RRBS_trito_pool_2_CGTACTAG.TAGCGG 0.567804 0.392930 0.650606 0.371967 CLL CLL trito_pool_2 0.0 0.542448 0.379673 0.565934 0.141666 0.161357 0.190254 0.0 0.190254 0.322077 0.564060 0.661459 0.291782 0.141023 0.236705 0.775427 0.812801 0.584395 0.788413 0.0 0.373111 0.419487 0.363822 0.398524 0.443218 0.429929 0.0 0.429929 0.428916 0.508213 0.389595 0.371629 0.425177 0.379944 0.390322 0.401379 0.390136 0.365038
443 RRBS_trito_pool_2_CGTACTAG.TATCTC 0.599881 0.371286 0.647639 0.360518 CLL CLL trito_pool_2 0.0 0.575018 0.407772 0.599556 0.150032 0.172842 0.207183 0.0 0.207183 0.348191 0.524038 0.668196 0.314785 0.147052 0.263346 0.860790 0.826304 0.608059 0.807089 0.0 0.350740 0.387199 0.345054 0.369375 0.416003 0.412018 0.0 0.412018 0.418236 0.488627 0.380793 0.343215 0.395603 0.371997 0.304058 0.381193 0.377228 0.343486
444 RRBS_trito_pool_2_CGTACTAG.TCTCTG 0.597122 0.387602 0.651297 0.369044 CLL CLL trito_pool_2 0.0 0.574766 0.414443 0.597902 0.153941 0.177219 0.200535 0.0 0.200535 0.344312 0.593102 0.687200 0.318141 0.152659 0.260521 0.857143 0.820577 0.596070 0.810128 0.0 0.364954 0.410597 0.357541 0.390213 0.434403 0.418302 0.0 0.418302 0.436308 0.430239 0.403836 0.358261 0.411722 0.383838 0.307817 0.397070 0.395306 0.363937
445 RRBS_trito_pool_2_CGTACTAG.TGACAG 0.588475 0.377043 0.654169 0.362253 CLL CLL trito_pool_2 0.0 0.566762 0.398915 0.590485 0.146940 0.169929 0.200901 0.0 0.200901 0.330337 0.614493 0.668077 0.309691 0.140807 0.241382 0.877043 0.819644 0.588788 0.797851 0.0 0.352289 0.386013 0.345792 0.381209 0.425696 0.436417 0.0 0.436417 0.417572 0.441868 0.379378 0.356776 0.394481 0.368598 0.322557 0.387040 0.387626 0.360044

446 rows × 46 columns

In [47] used 0.281 MiB RAM in 0.306s, peaked 0.000 MiB above current, total RAM usage 110.535 MiB

In [48]:
merged.to_csv("total_genomic_region.csv", index=False)


In [48] used 1.469 MiB RAM in 0.095s, peaked 0.000 MiB above current, total RAM usage 112.004 MiB

In [49]:
merged.shape


Out[49]:
(446, 46)
In [49] used 0.000 MiB RAM in 0.003s, peaked 0.000 MiB above current, total RAM usage 112.004 MiB

In [50]:
merged.columns


Out[50]:
Index(['filename', 'methylation', 'PDR_total', 'methylation_unweighted',
       'PDR_unweighted', 'type', 'bio', 'protocol', 'methylation_tssDistance',
       'methylation_genesDistance', 'methylation_exonsDistance',
       'methylation_intronsDistance', 'methylation_promoterDistance',
       'methylation_cgiDistance', 'methylation_ctcfDistance',
       'methylation_ctcfUpDistance', 'methylation_ctcfDownDistance',
       'methylation_geneDistalRegulatoryModulesDistance',
       'methylation_vistaEnhancersDistance', 'methylation_3PrimeUTRDistance',
       'methylation_5PrimeUTRDistance', 'methylation_firstExonDistance',
       'methylation_geneDistalRegulatoryModulesK562Distance',
       'methylation_hypoInHues64Distance', 'methylation_intergenic',
       'methylation_shore', 'methylation_shelf', 'PDR_tssDistance',
       'PDR_genesDistance', 'PDR_exonsDistance', 'PDR_intronsDistance',
       'PDR_promoterDistance', 'PDR_cgiDistance', 'PDR_ctcfDistance',
       'PDR_ctcfUpDistance', 'PDR_ctcfDownDistance',
       'PDR_geneDistalRegulatoryModulesDistance', 'PDR_vistaEnhancersDistance',
       'PDR_3PrimeUTRDistance', 'PDR_5PrimeUTRDistance',
       'PDR_firstExonDistance', 'PDR_geneDistalRegulatoryModulesK562Distance',
       'PDR_hypoInHues64Distance', 'PDR_intergenic', 'PDR_shore', 'PDR_shelf'],
      dtype='object')
In [50] used 0.000 MiB RAM in 0.006s, peaked 0.000 MiB above current, total RAM usage 112.004 MiB

In [51]:
#
# First do pairs by CLL vs Normal B;    We could discuss protocols at a later point
#
normal = merged[merged["type"]=="normal"]
CLL = merged[merged["type"]=="CLL"]


In [51] used 0.000 MiB RAM in 0.005s, peaked 0.000 MiB above current, total RAM usage 112.004 MiB

In [53]:
print(len(normal))
print(len(CLL))


342
104
In [53] used 0.000 MiB RAM in 0.002s, peaked 0.000 MiB above current, total RAM usage 112.004 MiB

In [54]:
CLL_pairs = CLL
normal_pairs = normal


In [54] used 0.000 MiB RAM in 0.001s, peaked 0.000 MiB above current, total RAM usage 112.004 MiB

In [58]:
CLL_pairs.columns


Out[58]:
Index(['filename', 'methylation', 'PDR_total', 'methylation_unweighted',
       'PDR_unweighted', 'type', 'bio', 'protocol', 'methylation_tssDistance',
       'methylation_genesDistance', 'methylation_exonsDistance',
       'methylation_intronsDistance', 'methylation_promoterDistance',
       'methylation_cgiDistance', 'methylation_ctcfDistance',
       'methylation_ctcfUpDistance', 'methylation_ctcfDownDistance',
       'methylation_geneDistalRegulatoryModulesDistance',
       'methylation_vistaEnhancersDistance', 'methylation_3PrimeUTRDistance',
       'methylation_5PrimeUTRDistance', 'methylation_firstExonDistance',
       'methylation_geneDistalRegulatoryModulesK562Distance',
       'methylation_hypoInHues64Distance', 'methylation_intergenic',
       'methylation_shore', 'methylation_shelf', 'PDR_tssDistance',
       'PDR_genesDistance', 'PDR_exonsDistance', 'PDR_intronsDistance',
       'PDR_promoterDistance', 'PDR_cgiDistance', 'PDR_ctcfDistance',
       'PDR_ctcfUpDistance', 'PDR_ctcfDownDistance',
       'PDR_geneDistalRegulatoryModulesDistance', 'PDR_vistaEnhancersDistance',
       'PDR_3PrimeUTRDistance', 'PDR_5PrimeUTRDistance',
       'PDR_firstExonDistance', 'PDR_geneDistalRegulatoryModulesK562Distance',
       'PDR_hypoInHues64Distance', 'PDR_intergenic', 'PDR_shore', 'PDR_shelf'],
      dtype='object')
In [58] used 0.012 MiB RAM in 0.005s, peaked 0.000 MiB above current, total RAM usage 149.809 MiB

In [80]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.methylation, CLL_pairs.methylation)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_difference': stacked})[['filename', 'methylation_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs1 = pd.merge(out, methylation_differences, how='inner')
print(pairs1.shape)


(5356, 44)
In [80] used 3.605 MiB RAM in 14.733s, peaked 0.000 MiB above current, total RAM usage 178.270 MiB

In [81]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.PDR_total, CLL_pairs.PDR_total)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
PDR_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_difference': stacked})[['filename', 'PDR_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs2 = pd.merge(out, PDR_differences, how='inner')
print(pairs2.shape)


(5356, 44)
In [81] used 2.234 MiB RAM in 15.129s, peaked 0.000 MiB above current, total RAM usage 180.504 MiB

In [82]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.methylation_unweighted, CLL_pairs.methylation_unweighted)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_unweighted_difference': stacked})[['filename', 'methylation_unweighted_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs3 = pd.merge(out, methylation_differences, how='inner')
print(pairs3.shape)


(5356, 44)
In [82] used 3.953 MiB RAM in 14.488s, peaked 0.000 MiB above current, total RAM usage 184.457 MiB

In [83]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.PDR_unweighted, CLL_pairs.PDR_unweighted)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
PDR_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_unweighted_difference': stacked})[['filename', 'PDR_unweighted_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs4 = pd.merge(out, PDR_differences, how='inner')
print(pairs4.shape)


(5356, 44)
In [83] used 1.730 MiB RAM in 14.159s, peaked 0.000 MiB above current, total RAM usage 186.188 MiB

In [84]:
"""
  'methylation_tssDistance',
       'methylation_genesDistance', 'methylation_exonsDistance',
       'methylation_intronsDistance', 'methylation_promoterDistance',
       'methylation_cgiDistance', 'methylation_ctcfDistance',
       'methylation_ctcfUpDistance', 'methylation_ctcfDownDistance',
       'methylation_geneDistalRegulatoryModulesDistance',
       'methylation_vistaEnhancersDistance', 'methylation_3PrimeUTRDistance',
       'methylation_5PrimeUTRDistance', 'methylation_firstExonDistance',
       'methylation_geneDistalRegulatoryModulesK562Distance',
       'methylation_hypoInHues64Distance', 'methylation_intergenic',
       'methylation_shore', 'methylation_shelf'

"""


Out[84]:
"\n  'methylation_tssDistance',\n       'methylation_genesDistance', 'methylation_exonsDistance',\n       'methylation_intronsDistance', 'methylation_promoterDistance',\n       'methylation_cgiDistance', 'methylation_ctcfDistance',\n       'methylation_ctcfUpDistance', 'methylation_ctcfDownDistance',\n       'methylation_geneDistalRegulatoryModulesDistance',\n       'methylation_vistaEnhancersDistance', 'methylation_3PrimeUTRDistance',\n       'methylation_5PrimeUTRDistance', 'methylation_firstExonDistance',\n       'methylation_geneDistalRegulatoryModulesK562Distance',\n       'methylation_hypoInHues64Distance', 'methylation_intergenic',\n       'methylation_shore', 'methylation_shelf'\n\n"
In [84] used 0.016 MiB RAM in 0.006s, peaked 0.000 MiB above current, total RAM usage 186.203 MiB

In [85]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.methylation_tssDistance, CLL_pairs.methylation_tssDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_tssDistance_difference': stacked})[['filename', 'methylation_tssDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs5 = pd.merge(out, methylation_differences, how='inner')
print(pairs5.shape)


(5356, 44)
In [85] used 0.016 MiB RAM in 15.618s, peaked 0.000 MiB above current, total RAM usage 186.219 MiB

In [86]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.methylation_genesDistance, CLL_pairs.methylation_genesDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_genesDistance_difference': stacked})[['filename', 'methylation_genesDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs6 = pd.merge(out, methylation_differences, how='inner')
print(pairs6.shape)


(5356, 44)
In [86] used 2.703 MiB RAM in 15.963s, peaked 0.000 MiB above current, total RAM usage 188.922 MiB

In [87]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.methylation_exonsDistance, CLL_pairs.methylation_exonsDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_exonsDistance_difference': stacked})[['filename', 'methylation_exonsDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs7 = pd.merge(out, methylation_differences, how='inner')
print(pairs7.shape)


(5356, 44)
In [87] used 2.430 MiB RAM in 14.548s, peaked 0.000 MiB above current, total RAM usage 191.352 MiB

In [88]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.methylation_intronsDistance, CLL_pairs.methylation_intronsDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_intronsDistance_difference': stacked})[['filename', 'methylation_intronsDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs8 = pd.merge(out, methylation_differences, how='inner')
print(pairs8.shape)


(5356, 44)
In [88] used 2.504 MiB RAM in 14.440s, peaked 0.000 MiB above current, total RAM usage 193.855 MiB

In [89]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.methylation_promoterDistance, CLL_pairs.methylation_promoterDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_promoterDistance_difference': stacked})[['filename', 'methylation_promoterDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs9 = pd.merge(out, methylation_differences, how='inner')
print(pairs9.shape)


(5356, 44)
In [89] used 2.066 MiB RAM in 14.449s, peaked 0.000 MiB above current, total RAM usage 195.922 MiB

In [90]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.methylation_cgiDistance, CLL_pairs.methylation_cgiDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_cgiDistance_difference': stacked})[['filename', 'methylation_cgiDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs10 = pd.merge(out, methylation_differences, how='inner')
print(pairs10.shape)


(5356, 44)
In [90] used 1.984 MiB RAM in 14.396s, peaked 0.000 MiB above current, total RAM usage 197.906 MiB

In [91]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.methylation_ctcfDistance, CLL_pairs.methylation_ctcfDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_ctcfDistance_difference': stacked})[['filename', 'methylation_ctcfDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs11 = pd.merge(out, methylation_differences, how='inner')
print(pairs11.shape)


(5356, 44)
In [91] used 0.766 MiB RAM in 14.460s, peaked 0.332 MiB above current, total RAM usage 198.672 MiB

In [92]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.methylation_ctcfUpDistance, CLL_pairs.methylation_ctcfUpDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_ctcfUpDistance_difference': stacked})[['filename', 'methylation_ctcfUpDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs12 = pd.merge(out, methylation_differences, how='inner')
print(pairs12.shape)


(5356, 44)
In [92] used 1.820 MiB RAM in 15.949s, peaked 0.000 MiB above current, total RAM usage 200.492 MiB

In [93]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.methylation_ctcfDownDistance, CLL_pairs.methylation_ctcfDownDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_ctcfDownDistance_difference': stacked})[['filename', 'methylation_ctcfDownDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs13 = pd.merge(out, methylation_differences, how='inner')
print(pairs13.shape)


(5356, 44)
In [93] used 2.316 MiB RAM in 15.657s, peaked 0.000 MiB above current, total RAM usage 202.809 MiB

In [94]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.methylation_geneDistalRegulatoryModulesDistance, CLL_pairs.methylation_geneDistalRegulatoryModulesDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_geneDistalRegulatoryModulesDistance_difference': stacked})[['filename', 'methylation_geneDistalRegulatoryModulesDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs14 = pd.merge(out, methylation_differences, how='inner')
print(pairs14.shape)


(5356, 44)
In [94] used 1.984 MiB RAM in 15.914s, peaked 0.000 MiB above current, total RAM usage 204.793 MiB

In [95]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.methylation_vistaEnhancersDistance, CLL_pairs.methylation_vistaEnhancersDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_vistaEnhancersDistance_difference': stacked})[['filename', 'methylation_vistaEnhancersDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs15 = pd.merge(out, methylation_differences, how='inner')
print(pairs15.shape)


(5356, 44)
In [95] used 2.051 MiB RAM in 15.193s, peaked 0.000 MiB above current, total RAM usage 206.844 MiB

In [96]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.methylation_3PrimeUTRDistance, CLL_pairs.methylation_3PrimeUTRDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_3PrimeUTRDistance_difference': stacked})[['filename', 'methylation_3PrimeUTRDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs16 = pd.merge(out, methylation_differences, how='inner')
print(pairs16.shape)


(5356, 44)
In [96] used 2.930 MiB RAM in 14.938s, peaked 0.000 MiB above current, total RAM usage 209.773 MiB

In [97]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.methylation_5PrimeUTRDistance, CLL_pairs.methylation_5PrimeUTRDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_5PrimeUTRDistance_difference': stacked})[['filename', 'methylation_5PrimeUTRDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs17 = pd.merge(out, methylation_differences, how='inner')
print(pairs17.shape)


(5356, 44)
In [97] used 2.066 MiB RAM in 15.019s, peaked 0.000 MiB above current, total RAM usage 211.840 MiB

In [98]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.methylation_firstExonDistance, CLL_pairs.methylation_firstExonDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_firstExonDistance_difference': stacked})[['filename', 'methylation_firstExonDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs18 = pd.merge(out, methylation_differences, how='inner')
print(pairs18.shape)


(5356, 44)
In [98] used 2.316 MiB RAM in 15.135s, peaked 0.000 MiB above current, total RAM usage 214.156 MiB

In [99]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.methylation_geneDistalRegulatoryModulesK562Distance, CLL_pairs.methylation_geneDistalRegulatoryModulesK562Distance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_geneDistalRegulatoryModulesK562Distance_difference': stacked})[['filename', 'methylation_geneDistalRegulatoryModulesK562Distance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs19 = pd.merge(out, methylation_differences, how='inner')
print(pairs19.shape)


(5356, 44)
In [99] used 3.656 MiB RAM in 16.087s, peaked 0.000 MiB above current, total RAM usage 217.812 MiB

In [100]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.methylation_hypoInHues64Distance, CLL_pairs.methylation_hypoInHues64Distance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_hypoInHues64Distance_difference': stacked})[['filename', 'methylation_hypoInHues64Distance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs20 = pd.merge(out, methylation_differences, how='inner')
print(pairs20.shape)


(5356, 44)
In [100] used 2.152 MiB RAM in 14.291s, peaked 0.000 MiB above current, total RAM usage 219.965 MiB

In [101]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.methylation_intergenic, CLL_pairs.methylation_intergenic)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_intergenic_difference': stacked})[['filename', 'methylation_intergenic_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs21 = pd.merge(out, methylation_differences, how='inner')
print(pairs21.shape)


(5356, 44)
In [101] used 2.242 MiB RAM in 15.156s, peaked 0.000 MiB above current, total RAM usage 222.207 MiB

In [102]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.methylation_shore, CLL_pairs.methylation_shore)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_shore_difference': stacked})[['filename', 'methylation_shore_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs22 = pd.merge(out, methylation_differences, how='inner')
print(pairs22.shape)


(5356, 44)
In [102] used 2.191 MiB RAM in 14.919s, peaked 0.000 MiB above current, total RAM usage 224.398 MiB

In [103]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.methylation_shelf, CLL_pairs.methylation_shelf)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_shelf_difference': stacked})[['filename', 'methylation_shelf_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs23 = pd.merge(out, methylation_differences, how='inner')
print(pairs23.shape)


(5356, 44)
In [103] used 2.320 MiB RAM in 16.496s, peaked 0.000 MiB above current, total RAM usage 226.719 MiB

In [ ]:


In [105]:
"""
###
PDR by genomic regions
###
"""


Out[105]:
'\n###\nPDR by genomic regions\n###\n'
In [105] used 0.000 MiB RAM in 0.002s, peaked 0.000 MiB above current, total RAM usage 226.129 MiB

In [106]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.PDR_tssDistance, CLL_pairs.PDR_tssDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_tssDistance_difference': stacked})[['filename', 'PDR_tssDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs24 = pd.merge(out, methylation_differences, how='inner')
print(pairs24.shape)


(5356, 44)
In [106] used 2.242 MiB RAM in 17.194s, peaked 0.000 MiB above current, total RAM usage 228.371 MiB

In [107]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.PDR_genesDistance, CLL_pairs.PDR_genesDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_genesDistance_difference': stacked})[['filename', 'PDR_genesDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs25 = pd.merge(out, methylation_differences, how='inner')
print(pairs25.shape)


(5356, 44)
In [107] used 2.273 MiB RAM in 16.817s, peaked 0.000 MiB above current, total RAM usage 230.645 MiB

In [108]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.PDR_exonsDistance, CLL_pairs.PDR_exonsDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_exonsDistance_difference': stacked})[['filename', 'PDR_exonsDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs26 = pd.merge(out, methylation_differences, how='inner')
print(pairs26.shape)


(5356, 44)
In [108] used 2.398 MiB RAM in 17.337s, peaked 0.000 MiB above current, total RAM usage 233.043 MiB

In [109]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.PDR_intronsDistance, CLL_pairs.PDR_intronsDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_intronsDistance_difference': stacked})[['filename', 'PDR_intronsDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs27 = pd.merge(out, methylation_differences, how='inner')
print(pairs27.shape)


(5356, 44)
In [109] used 0.430 MiB RAM in 15.936s, peaked 0.000 MiB above current, total RAM usage 233.473 MiB

In [110]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.PDR_promoterDistance, CLL_pairs.PDR_promoterDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_promoterDistance_difference': stacked})[['filename', 'PDR_promoterDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs28 = pd.merge(out, methylation_differences, how='inner')
print(pairs28.shape)


(5356, 44)
In [110] used 2.398 MiB RAM in 16.367s, peaked 0.000 MiB above current, total RAM usage 235.871 MiB

In [111]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.PDR_cgiDistance, CLL_pairs.PDR_cgiDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_cgiDistance_difference': stacked})[['filename', 'PDR_cgiDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs29 = pd.merge(out, methylation_differences, how='inner')
print(pairs29.shape)


(5356, 44)
In [111] used 1.969 MiB RAM in 19.534s, peaked 0.000 MiB above current, total RAM usage 237.840 MiB

In [112]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.PDR_ctcfDistance, CLL_pairs.PDR_ctcfDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_ctcfDistance_difference': stacked})[['filename', 'PDR_ctcfDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs30 = pd.merge(out, methylation_differences, how='inner')
print(pairs30.shape)


(5356, 44)
In [112] used 2.465 MiB RAM in 17.458s, peaked 0.000 MiB above current, total RAM usage 240.305 MiB

In [113]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.PDR_ctcfUpDistance, CLL_pairs.PDR_ctcfUpDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_ctcfUpDistance_difference': stacked})[['filename', 'PDR_ctcfUpDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs31 = pd.merge(out, methylation_differences, how='inner')
print(pairs31.shape)


(5356, 44)
In [113] used 2.234 MiB RAM in 16.812s, peaked 0.000 MiB above current, total RAM usage 242.539 MiB

In [114]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.PDR_ctcfDownDistance, CLL_pairs.PDR_ctcfDownDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_ctcfDownDistance_difference': stacked})[['filename', 'PDR_ctcfDownDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs32 = pd.merge(out, methylation_differences, how='inner')
print(pairs32.shape)


(5356, 44)
In [114] used 1.984 MiB RAM in 19.603s, peaked 0.000 MiB above current, total RAM usage 244.523 MiB

In [115]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.PDR_geneDistalRegulatoryModulesDistance, CLL_pairs.PDR_geneDistalRegulatoryModulesDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_geneDistalRegulatoryModulesDistance_difference': stacked})[['filename', 'PDR_geneDistalRegulatoryModulesDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs33 = pd.merge(out, methylation_differences, how='inner')
print(pairs33.shape)


(5356, 44)
In [115] used 2.566 MiB RAM in 18.701s, peaked 0.000 MiB above current, total RAM usage 247.090 MiB

In [116]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.PDR_vistaEnhancersDistance, CLL_pairs.PDR_vistaEnhancersDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_vistaEnhancersDistance_difference': stacked})[['filename', 'PDR_vistaEnhancersDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs34 = pd.merge(out, methylation_differences, how='inner')
print(pairs34.shape)


(5356, 44)
In [116] used 2.234 MiB RAM in 17.241s, peaked 0.000 MiB above current, total RAM usage 249.324 MiB

In [117]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.PDR_3PrimeUTRDistance, CLL_pairs.PDR_3PrimeUTRDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_3PrimeUTRDistance_difference': stacked})[['filename', 'PDR_3PrimeUTRDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs35 = pd.merge(out, methylation_differences, how='inner')
print(pairs35.shape)


(5356, 44)
In [117] used 3.953 MiB RAM in 14.677s, peaked 0.000 MiB above current, total RAM usage 253.277 MiB

In [118]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.PDR_5PrimeUTRDistance, CLL_pairs.PDR_5PrimeUTRDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_5PrimeUTRDistance_difference': stacked})[['filename', 'PDR_5PrimeUTRDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs36 = pd.merge(out, methylation_differences, how='inner')
print(pairs36.shape)


(5356, 44)
In [118] used 1.824 MiB RAM in 14.132s, peaked 0.000 MiB above current, total RAM usage 255.102 MiB

In [119]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.PDR_firstExonDistance, CLL_pairs.PDR_firstExonDistance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_firstExonDistance_difference': stacked})[['filename', 'PDR_firstExonDistance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs37 = pd.merge(out, methylation_differences, how='inner')
print(pairs37.shape)


(5356, 44)
In [119] used 2.316 MiB RAM in 14.119s, peaked 0.000 MiB above current, total RAM usage 257.418 MiB

In [120]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.PDR_geneDistalRegulatoryModulesK562Distance, CLL_pairs.PDR_geneDistalRegulatoryModulesK562Distance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_geneDistalRegulatoryModulesK562Distance_difference': stacked})[['filename', 'PDR_geneDistalRegulatoryModulesK562Distance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs38 = pd.merge(out, methylation_differences, how='inner')
print(pairs38.shape)


(5356, 44)
In [120] used 2.148 MiB RAM in 15.021s, peaked 0.000 MiB above current, total RAM usage 259.566 MiB

In [121]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.PDR_hypoInHues64Distance, CLL_pairs.PDR_hypoInHues64Distance)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_hypoInHues64Distance_difference': stacked})[['filename', 'PDR_hypoInHues64Distance_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs39 = pd.merge(out, methylation_differences, how='inner')
print(pairs39.shape)


(5356, 44)
In [121] used 1.902 MiB RAM in 14.078s, peaked 0.000 MiB above current, total RAM usage 261.469 MiB

In [122]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.PDR_intergenic, CLL_pairs.PDR_intergenic)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_intergenic_difference': stacked})[['filename', 'PDR_intergenic_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs40 = pd.merge(out, methylation_differences, how='inner')
print(pairs40.shape)


(5356, 44)
In [122] used 2.234 MiB RAM in 16.987s, peaked 0.000 MiB above current, total RAM usage 263.703 MiB

In [123]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.PDR_shore, CLL_pairs.PDR_shore)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_shore_difference': stacked})[['filename', 'PDR_shore_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs41 = pd.merge(out, methylation_differences, how='inner')
print(pairs41.shape)


(5356, 44)
In [123] used 1.984 MiB RAM in 19.821s, peaked 0.000 MiB above current, total RAM usage 265.688 MiB

In [124]:
CLL_pairsA = CLL_pairs.set_index("filename")
from itertools import combinations
cc = list(combinations(CLL_pairs.filename, 2)) # combines into all pairs
out = pd.DataFrame([CLL_pairsA.loc[c,:].mean() for c in cc], index=cc)  # covariates between pairs == mean
df_ex = pd.DataFrame(np.abs(np.subtract.outer(CLL_pairs.PDR_shelf, CLL_pairs.PDR_shelf)), CLL_pairs.filename, CLL_pairs.filename)
stacked = df_ex.stack()
methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_shelf_difference': stacked})[['filename', 'PDR_shelf_difference']].reset_index(drop=True)
out['filename'] = out.index
out = out.reset_index(drop=True)
pairs42 = pd.merge(out, methylation_differences, how='inner')
print(pairs42.shape)


(5356, 44)
In [124] used 2.234 MiB RAM in 16.024s, peaked 0.000 MiB above current, total RAM usage 267.922 MiB

In [ ]:


In [125]:
pairs42


Out[125]:
methylation PDR_total methylation_unweighted PDR_unweighted methylation_tssDistance methylation_genesDistance methylation_exonsDistance methylation_intronsDistance methylation_promoterDistance methylation_cgiDistance methylation_ctcfDistance methylation_ctcfUpDistance methylation_ctcfDownDistance methylation_geneDistalRegulatoryModulesDistance methylation_vistaEnhancersDistance methylation_3PrimeUTRDistance methylation_5PrimeUTRDistance methylation_firstExonDistance methylation_geneDistalRegulatoryModulesK562Distance methylation_hypoInHues64Distance methylation_intergenic methylation_shore methylation_shelf PDR_tssDistance PDR_genesDistance PDR_exonsDistance PDR_intronsDistance PDR_promoterDistance PDR_cgiDistance PDR_ctcfDistance PDR_ctcfUpDistance PDR_ctcfDownDistance PDR_geneDistalRegulatoryModulesDistance PDR_vistaEnhancersDistance PDR_3PrimeUTRDistance PDR_5PrimeUTRDistance PDR_firstExonDistance PDR_geneDistalRegulatoryModulesK562Distance PDR_hypoInHues64Distance PDR_intergenic PDR_shore PDR_shelf filename PDR_shelf_difference
0 0.551295 0.397926 0.626034 0.370854 0.0 0.526491 0.356053 0.552839 0.144775 0.165605 0.186561 0.0 0.186561 0.313298 0.450917 0.623572 0.281453 0.140055 0.241887 0.748198 0.808540 0.560646 0.793948 0.0 0.379121 0.427961 0.371383 0.413445 0.454698 0.439447 0.0 0.439447 0.443367 0.617032 0.392821 0.381384 0.442329 0.391547 0.371952 0.399228 0.391493 0.353839 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.017234
1 0.561168 0.395660 0.632905 0.371997 0.0 0.538927 0.372424 0.564144 0.143587 0.167704 0.199828 0.0 0.199828 0.322613 0.498961 0.657855 0.288344 0.140775 0.236259 0.783529 0.806741 0.571259 0.796352 0.0 0.377060 0.425335 0.367250 0.409391 0.453187 0.444993 0.0 0.444993 0.448398 0.359164 0.426179 0.381641 0.439329 0.401194 0.338826 0.395636 0.383800 0.354399 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.018353
2 0.586573 0.383793 0.649101 0.363193 0.0 0.563676 0.393126 0.589529 0.153506 0.176220 0.197228 0.0 0.197228 0.335660 0.571508 0.662528 0.305788 0.148918 0.247356 0.836527 0.825884 0.600020 0.809709 0.0 0.366661 0.418496 0.356945 0.414704 0.452944 0.437487 0.0 0.437487 0.449179 0.435398 0.393152 0.379366 0.444245 0.394349 0.284400 0.374797 0.382018 0.345564 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.000683
3 0.580396 0.385693 0.644074 0.363936 0.0 0.555869 0.388916 0.579945 0.148965 0.173668 0.196107 0.0 0.196107 0.334811 0.514905 0.655823 0.296143 0.144047 0.248409 0.850315 0.822764 0.591608 0.807360 0.0 0.367904 0.417351 0.358361 0.407637 0.449419 0.435152 0.0 0.435152 0.437409 0.400163 0.403501 0.376191 0.432096 0.386120 0.336083 0.381035 0.378476 0.345977 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.001510
4 0.565347 0.386762 0.641001 0.362010 0.0 0.542164 0.376641 0.568011 0.142906 0.164885 0.192264 0.0 0.192264 0.315714 0.475214 0.651849 0.286462 0.140486 0.231512 0.831779 0.816135 0.583955 0.794591 0.0 0.369056 0.416448 0.359475 0.405798 0.445169 0.430005 0.0 0.430005 0.437915 0.342854 0.394958 0.375891 0.433419 0.379527 0.359972 0.384842 0.379762 0.348089 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.005733
5 0.583745 0.388191 0.648812 0.366751 0.0 0.561490 0.393073 0.586520 0.151211 0.176944 0.202230 0.0 0.202230 0.339140 0.528837 0.664487 0.304993 0.145939 0.252372 0.812901 0.820019 0.593443 0.805641 0.0 0.369654 0.419303 0.360364 0.412780 0.454607 0.442732 0.0 0.442732 0.444900 0.416998 0.388882 0.379385 0.441311 0.392011 0.375738 0.383585 0.383928 0.343012 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.004421
6 0.575364 0.388562 0.642310 0.366305 0.0 0.552458 0.386006 0.578217 0.150924 0.173986 0.196806 0.0 0.196806 0.326897 0.566176 0.665881 0.297271 0.150684 0.243671 0.815142 0.819686 0.589356 0.802803 0.0 0.373075 0.427406 0.363035 0.415715 0.455041 0.444185 0.0 0.444185 0.446741 0.440788 0.399946 0.383779 0.446417 0.393044 0.347103 0.378908 0.381748 0.347989 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.005533
7 0.582381 0.388683 0.646698 0.367074 0.0 0.558780 0.393456 0.583339 0.150527 0.175767 0.204094 0.0 0.204094 0.335604 0.484967 0.659229 0.301772 0.150516 0.249021 0.824676 0.822305 0.590635 0.805443 0.0 0.371754 0.422702 0.362783 0.413011 0.455296 0.452751 0.0 0.452751 0.448067 0.372743 0.403088 0.382035 0.441989 0.395990 0.358361 0.382204 0.384448 0.352543 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.014641
8 0.567135 0.395730 0.637371 0.371038 0.0 0.544832 0.380601 0.569006 0.145946 0.171282 0.190641 0.0 0.190641 0.316497 0.542712 0.637379 0.293785 0.140265 0.234106 0.820998 0.808825 0.580077 0.795201 0.0 0.377668 0.426121 0.368661 0.413402 0.455472 0.451045 0.0 0.451045 0.449853 0.494214 0.413741 0.378131 0.439004 0.392492 0.368676 0.394071 0.387644 0.357420 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.024396
9 0.566839 0.391394 0.633777 0.366020 0.0 0.544572 0.379085 0.569705 0.146409 0.169433 0.197102 0.0 0.197102 0.319901 0.579025 0.641064 0.295379 0.149054 0.237203 0.824352 0.813197 0.575825 0.793850 0.0 0.376125 0.425598 0.367472 0.416301 0.455418 0.441600 0.0 0.441600 0.442144 0.339510 0.402269 0.386363 0.448292 0.386875 0.342879 0.382705 0.386876 0.347095 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.003746
10 0.572828 0.391152 0.642154 0.367973 0.0 0.549228 0.382513 0.573596 0.147106 0.171157 0.195066 0.0 0.195066 0.323383 0.517167 0.639984 0.297912 0.143004 0.240815 0.806413 0.818149 0.584713 0.799527 0.0 0.373111 0.420968 0.363387 0.414048 0.456939 0.438866 0.0 0.438866 0.441841 0.490781 0.398909 0.382705 0.438881 0.391178 0.377740 0.384666 0.384590 0.348841 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.007237
11 0.570431 0.394100 0.640568 0.368005 0.0 0.548445 0.383462 0.572895 0.150021 0.171691 0.197512 0.0 0.197512 0.324700 0.530873 0.642001 0.299824 0.147831 0.245901 0.811482 0.814329 0.575783 0.796441 0.0 0.376631 0.431420 0.366070 0.420563 0.462508 0.460409 0.0 0.460409 0.451409 0.393751 0.422416 0.384087 0.452035 0.393007 0.425199 0.386985 0.388808 0.349917 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.009390
12 0.586555 0.387184 0.649357 0.366469 0.0 0.565676 0.397133 0.591080 0.152368 0.177463 0.208336 0.0 0.208336 0.339760 0.498276 0.676277 0.307506 0.146069 0.256148 0.805441 0.819506 0.595773 0.806092 0.0 0.367493 0.417118 0.357402 0.413121 0.450975 0.448832 0.0 0.448832 0.442611 0.410428 0.391999 0.380184 0.437740 0.387471 0.317387 0.384328 0.383423 0.348236 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.006028
13 0.571879 0.398715 0.637367 0.371785 0.0 0.548319 0.380245 0.574034 0.149515 0.171182 0.199198 0.0 0.199198 0.324016 0.401829 0.644951 0.297459 0.145199 0.238716 0.813369 0.806891 0.577883 0.791293 0.0 0.381746 0.436265 0.372351 0.426457 0.467592 0.462471 0.0 0.462471 0.453047 0.304228 0.431407 0.392820 0.454017 0.395637 0.476810 0.391672 0.388853 0.352361 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.014278
14 0.574364 0.390812 0.638729 0.369089 0.0 0.553203 0.384591 0.577964 0.147751 0.168572 0.190728 0.0 0.190728 0.331216 0.592341 0.659529 0.301509 0.140018 0.244405 0.831855 0.811678 0.582078 0.797774 0.0 0.369719 0.417920 0.361119 0.408143 0.450672 0.435474 0.0 0.435474 0.447435 0.362022 0.418218 0.372678 0.431081 0.395873 0.301459 0.391714 0.390792 0.354544 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.018643
15 0.563025 0.392571 0.635052 0.365296 0.0 0.537338 0.374109 0.562412 0.146864 0.171379 0.195148 0.0 0.195148 0.318039 0.507820 0.652158 0.286704 0.143441 0.235816 0.862091 0.814903 0.570021 0.790561 0.0 0.377474 0.430295 0.367318 0.417978 0.460599 0.447405 0.0 0.447405 0.448256 0.442620 0.413828 0.387365 0.446590 0.390753 0.275255 0.378730 0.384141 0.358707 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.026970
16 0.558105 0.401336 0.629414 0.375672 0.0 0.536255 0.371571 0.561038 0.142838 0.167093 0.188449 0.0 0.188449 0.317543 0.489753 0.622220 0.288219 0.139591 0.239610 0.766199 0.802490 0.562680 0.788462 0.0 0.383528 0.430493 0.374530 0.415803 0.456866 0.443240 0.0 0.443240 0.455693 0.410082 0.408209 0.386900 0.442361 0.401489 0.379217 0.401247 0.394236 0.359091 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.027737
17 0.589533 0.384442 0.648214 0.365278 0.0 0.567071 0.397548 0.592438 0.152635 0.176725 0.200567 0.0 0.200567 0.335821 0.558278 0.665961 0.311514 0.147307 0.249976 0.837922 0.824039 0.600182 0.807817 0.0 0.366934 0.418365 0.357557 0.413279 0.452939 0.437543 0.0 0.437543 0.444521 0.357537 0.402062 0.378518 0.441674 0.394321 0.325255 0.377396 0.378557 0.346325 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.002207
18 0.584829 0.386122 0.645871 0.362919 0.0 0.561357 0.393114 0.586628 0.153172 0.176179 0.204353 0.0 0.204353 0.336560 0.549394 0.670547 0.306589 0.149030 0.251405 0.834840 0.824608 0.596731 0.807800 0.0 0.369391 0.420641 0.359994 0.415739 0.457035 0.450444 0.0 0.450444 0.447941 0.352114 0.391470 0.385953 0.446827 0.392647 0.287559 0.376076 0.381377 0.345494 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.000544
19 0.569344 0.395428 0.634900 0.377481 0.0 0.545893 0.380526 0.571689 0.144707 0.166553 0.187823 0.0 0.187823 0.320157 0.516365 0.652378 0.296744 0.143074 0.236713 0.768369 0.814746 0.585734 0.801079 0.0 0.379299 0.432388 0.368599 0.420700 0.460410 0.453024 0.0 0.453024 0.449037 0.522290 0.419229 0.390426 0.455015 0.393230 0.387566 0.387376 0.387222 0.356956 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.023467
20 0.532427 0.400277 0.608316 0.375039 0.0 0.508848 0.344650 0.534670 0.136203 0.159195 0.179771 0.0 0.179771 0.294223 0.336044 0.633580 0.258574 0.134258 0.219630 0.849467 0.802652 0.543726 0.779226 0.0 0.381811 0.429993 0.372421 0.415090 0.455377 0.437571 0.0 0.437571 0.451545 0.283441 0.433468 0.389132 0.445993 0.400376 0.360881 0.401288 0.401163 0.344121 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.002202
21 0.532230 0.403112 0.610903 0.377874 0.0 0.507741 0.355698 0.533184 0.138236 0.167710 0.186971 0.0 0.186971 0.308027 0.471364 0.621988 0.258712 0.134631 0.224186 0.890476 0.806141 0.553951 0.779511 0.0 0.390140 0.430686 0.383338 0.417405 0.457783 0.442420 0.0 0.442420 0.448978 0.345026 0.411213 0.392350 0.446466 0.403405 0.231880 0.389633 0.391709 0.342310 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.005825
22 0.594374 0.395049 0.655438 0.374199 0.0 0.573736 0.405002 0.598216 0.158911 0.182839 0.201224 0.0 0.201224 0.350799 0.634227 0.667989 0.320649 0.154182 0.264668 0.818538 0.818372 0.592535 0.806155 0.0 0.376020 0.432429 0.365967 0.431037 0.473852 0.456570 0.0 0.456570 0.456758 0.263758 0.407668 0.386698 0.456228 0.407150 0.317577 0.386858 0.387665 0.346707 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.002969
23 0.564698 0.400428 0.633837 0.376670 0.0 0.541999 0.378636 0.568568 0.146165 0.169332 0.193734 0.0 0.193734 0.324749 0.477238 0.655109 0.291000 0.140393 0.240504 0.816043 0.807187 0.575423 0.796047 0.0 0.380053 0.434202 0.368875 0.427783 0.465704 0.467551 0.0 0.467551 0.452715 0.292837 0.404489 0.392381 0.459191 0.398842 0.346153 0.399896 0.391161 0.363957 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.037471
24 0.559202 0.398322 0.637497 0.372609 0.0 0.536085 0.376006 0.560128 0.143768 0.168587 0.203345 0.0 0.203345 0.316097 0.483790 0.650469 0.288335 0.140910 0.236297 0.865983 0.809547 0.566528 0.792310 0.0 0.378806 0.427979 0.369818 0.424106 0.464195 0.445216 0.0 0.445216 0.447473 0.424498 0.394161 0.388758 0.450582 0.396406 0.286419 0.392375 0.396380 0.347522 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.004599
25 0.579701 0.391229 0.646686 0.372045 0.0 0.557762 0.392912 0.582109 0.150211 0.174574 0.197605 0.0 0.197605 0.332346 0.484299 0.681663 0.301759 0.147482 0.247505 0.841507 0.820166 0.585983 0.801525 0.0 0.375333 0.426090 0.366425 0.423118 0.463918 0.452272 0.0 0.452272 0.455448 0.562734 0.405547 0.389146 0.453629 0.400900 0.421621 0.379836 0.379635 0.344143 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.002158
26 0.579455 0.393811 0.645546 0.374480 0.0 0.558045 0.388241 0.583680 0.149822 0.170872 0.197980 0.0 0.197980 0.326318 0.500363 0.656266 0.306368 0.146329 0.242470 0.821164 0.814876 0.590116 0.798047 0.0 0.374556 0.426213 0.364076 0.419114 0.458953 0.456082 0.0 0.456082 0.450773 0.519483 0.407983 0.383316 0.443942 0.404958 0.407448 0.390763 0.386732 0.356864 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.023284
27 0.567381 0.395007 0.635819 0.371447 0.0 0.545113 0.377321 0.569878 0.145036 0.168330 0.193045 0.0 0.193045 0.322619 0.500578 0.650690 0.289468 0.140298 0.241467 0.821806 0.812082 0.578802 0.794615 0.0 0.377992 0.426348 0.369121 0.420506 0.460840 0.449413 0.0 0.449413 0.451783 0.439480 0.404228 0.393466 0.445314 0.398843 0.398555 0.390258 0.386032 0.356223 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.022001
28 0.561348 0.398227 0.636291 0.375595 0.0 0.537481 0.368655 0.563892 0.144020 0.166652 0.188667 0.0 0.188667 0.316822 0.544610 0.647264 0.289621 0.138206 0.229463 0.825901 0.806857 0.568495 0.786405 0.0 0.380795 0.431907 0.371971 0.417989 0.461357 0.451012 0.0 0.451012 0.450138 0.459838 0.422747 0.386098 0.446144 0.391688 0.318395 0.392682 0.392195 0.344016 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.002413
29 0.579461 0.395725 0.646355 0.376230 0.0 0.556855 0.396536 0.580833 0.152036 0.178565 0.198986 0.0 0.198986 0.336717 0.501606 0.654408 0.301080 0.152396 0.249491 0.819054 0.818837 0.585999 0.801209 0.0 0.378741 0.428184 0.370138 0.423813 0.465175 0.462379 0.0 0.462379 0.459329 0.458150 0.413899 0.386857 0.453231 0.408654 0.409612 0.386064 0.392521 0.357648 (RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACA... 0.024851
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
5326 0.596766 0.381684 0.653927 0.363298 0.0 0.574537 0.407185 0.598535 0.150284 0.174322 0.201379 0.0 0.201379 0.341951 0.597171 0.680571 0.314782 0.147331 0.255774 0.878658 0.821440 0.601963 0.807264 0.0 0.357838 0.399577 0.350386 0.384825 0.428788 0.416093 0.0 0.416093 0.433422 0.457541 0.394881 0.354717 0.403558 0.380525 0.284117 0.392191 0.387496 0.358077 (RRBS_trito_pool_2_CGTACTAG.GCATTC, RRBS_trito... 0.011720
5327 0.592442 0.376405 0.655363 0.359902 0.0 0.570535 0.399421 0.594827 0.146783 0.170677 0.201562 0.0 0.201562 0.334963 0.607866 0.671009 0.310557 0.141405 0.246204 0.888608 0.820973 0.598322 0.801125 0.0 0.351506 0.387285 0.344512 0.380323 0.424434 0.425150 0.0 0.425150 0.424054 0.463356 0.382653 0.353974 0.394938 0.372905 0.291487 0.387177 0.383656 0.356130 (RRBS_trito_pool_2_CGTACTAG.GCATTC, RRBS_trito... 0.007827
5328 0.583397 0.388502 0.649407 0.366957 0.0 0.558135 0.391450 0.582353 0.147699 0.171788 0.209431 0.0 0.209431 0.334148 0.481764 0.671901 0.305335 0.143871 0.253326 0.874036 0.811617 0.573180 0.789369 0.0 0.367095 0.413670 0.359908 0.390008 0.437328 0.443670 0.0 0.443670 0.437493 0.362906 0.412073 0.363198 0.419156 0.384055 0.208321 0.396863 0.393298 0.363785 (RRBS_trito_pool_2_CGTACTAG.GCTGCC, RRBS_trito... 0.014345
5329 0.578001 0.385881 0.653252 0.364053 0.0 0.553295 0.383661 0.578559 0.146541 0.168179 0.203103 0.0 0.203103 0.330876 0.441302 0.671057 0.300918 0.143190 0.245404 0.881851 0.815285 0.572946 0.792663 0.0 0.366809 0.411266 0.359889 0.392939 0.437887 0.440739 0.0 0.440739 0.432628 0.414817 0.394897 0.367939 0.415584 0.385481 0.224830 0.390383 0.398111 0.357957 (RRBS_trito_pool_2_CGTACTAG.GCTGCC, RRBS_trito... 0.002688
5330 0.583919 0.381640 0.655076 0.362032 0.0 0.559107 0.391010 0.584520 0.147504 0.171735 0.204042 0.0 0.204042 0.334797 0.498691 0.671189 0.305718 0.143510 0.249380 0.891078 0.816287 0.580675 0.790006 0.0 0.360926 0.405322 0.353275 0.384534 0.432656 0.440986 0.0 0.440986 0.432136 0.532505 0.394468 0.357032 0.408812 0.379037 0.221391 0.387849 0.385415 0.358011 (RRBS_trito_pool_2_CGTACTAG.GCTGCC, RRBS_trito... 0.002796
5331 0.575568 0.388494 0.650562 0.366684 0.0 0.550137 0.384495 0.574614 0.143898 0.165840 0.203452 0.0 0.203452 0.327149 0.514801 0.673324 0.297515 0.141851 0.244929 0.850575 0.812592 0.575647 0.789166 0.0 0.368728 0.415280 0.360695 0.392584 0.439785 0.442581 0.0 0.442581 0.428255 0.475101 0.399813 0.364332 0.420080 0.378762 0.256228 0.395741 0.391012 0.360825 (RRBS_trito_pool_2_CGTACTAG.GCTGCC, RRBS_trito... 0.008425
5332 0.591606 0.377671 0.649079 0.360959 0.0 0.566422 0.398545 0.591425 0.148082 0.171582 0.211917 0.0 0.211917 0.340206 0.494790 0.676692 0.309016 0.144866 0.258249 0.893256 0.819344 0.587478 0.798504 0.0 0.357542 0.399136 0.351311 0.378009 0.426178 0.433626 0.0 0.433626 0.422915 0.465308 0.395412 0.350125 0.405293 0.374789 0.213096 0.385648 0.384558 0.350049 (RRBS_trito_pool_2_CGTACTAG.GCTGCC, RRBS_trito... 0.013126
5333 0.590227 0.385830 0.650908 0.365222 0.0 0.566296 0.401880 0.590599 0.150036 0.173771 0.208593 0.0 0.208593 0.338267 0.529322 0.686195 0.310695 0.147669 0.256837 0.891433 0.816480 0.581484 0.800023 0.0 0.364649 0.410835 0.357554 0.388428 0.435377 0.436768 0.0 0.436768 0.431951 0.436114 0.406933 0.357647 0.413353 0.380709 0.214975 0.393586 0.393597 0.360275 (RRBS_trito_pool_2_CGTACTAG.GCTGCC, RRBS_trito... 0.007324
5334 0.585903 0.380550 0.652344 0.361827 0.0 0.562295 0.394116 0.586890 0.146536 0.170126 0.208776 0.0 0.208776 0.331279 0.540018 0.676633 0.306469 0.141743 0.247267 0.901383 0.816014 0.577843 0.793885 0.0 0.358316 0.398543 0.351680 0.383926 0.431024 0.445825 0.0 0.445825 0.422583 0.441928 0.394705 0.356905 0.404732 0.373089 0.222345 0.388572 0.389757 0.358328 (RRBS_trito_pool_2_CGTACTAG.GCTGCC, RRBS_trito... 0.003431
5335 0.578066 0.390325 0.652140 0.369610 0.0 0.553603 0.385794 0.577617 0.148109 0.169644 0.195882 0.0 0.195882 0.332803 0.457524 0.657769 0.303005 0.144382 0.245578 0.830164 0.814519 0.579229 0.792113 0.0 0.369561 0.413863 0.362230 0.396304 0.438863 0.429175 0.0 0.429175 0.442527 0.335733 0.396939 0.374103 0.419756 0.391956 0.311017 0.397143 0.399521 0.365129 (RRBS_trito_pool_2_CGTACTAG.GGCATC, RRBS_trito... 0.011657
5336 0.583985 0.386084 0.653964 0.367589 0.0 0.559416 0.393142 0.583578 0.149072 0.173201 0.196821 0.0 0.196821 0.336724 0.514912 0.657901 0.307806 0.144702 0.249554 0.839391 0.815520 0.586957 0.789456 0.0 0.363677 0.407919 0.355615 0.387898 0.433633 0.429422 0.0 0.429422 0.442035 0.453422 0.396510 0.363197 0.412984 0.385512 0.307578 0.394610 0.386825 0.365183 (RRBS_trito_pool_2_CGTACTAG.GGCATC, RRBS_trito... 0.011549
5337 0.575633 0.392938 0.649450 0.372241 0.0 0.550446 0.386627 0.573672 0.145467 0.167305 0.196232 0.0 0.196232 0.329076 0.531023 0.660036 0.299602 0.143043 0.245102 0.798888 0.811826 0.581929 0.788616 0.0 0.371479 0.417877 0.363035 0.395949 0.440761 0.431017 0.0 0.431017 0.438154 0.396018 0.401855 0.370496 0.424253 0.385237 0.342415 0.402501 0.392422 0.367998 (RRBS_trito_pool_2_CGTACTAG.GGCATC, RRBS_trito... 0.005920
5338 0.591672 0.382116 0.647967 0.366516 0.0 0.566731 0.400677 0.590483 0.149650 0.173048 0.204697 0.0 0.204697 0.342133 0.511011 0.663404 0.311103 0.146058 0.258423 0.841569 0.818577 0.593761 0.797954 0.0 0.360294 0.401733 0.353651 0.381374 0.427154 0.422062 0.0 0.422062 0.432814 0.386225 0.397453 0.356289 0.409466 0.381264 0.299283 0.392408 0.385968 0.357222 (RRBS_trito_pool_2_CGTACTAG.GGCATC, RRBS_trito... 0.027471
5339 0.590293 0.390274 0.649796 0.370779 0.0 0.566605 0.404013 0.589656 0.151604 0.175236 0.201373 0.0 0.201373 0.340194 0.545544 0.672907 0.312782 0.148861 0.257011 0.839746 0.815714 0.587766 0.799474 0.0 0.367401 0.413432 0.359895 0.391793 0.436354 0.425204 0.0 0.425204 0.441850 0.357031 0.408975 0.363812 0.417526 0.387184 0.301162 0.400347 0.395007 0.367447 (RRBS_trito_pool_2_CGTACTAG.GGCATC, RRBS_trito... 0.007021
5340 0.585969 0.384995 0.651232 0.367383 0.0 0.562603 0.396248 0.585948 0.148104 0.171591 0.201556 0.0 0.201556 0.333206 0.556239 0.663345 0.308556 0.142935 0.247441 0.849696 0.815247 0.584125 0.793335 0.0 0.361068 0.401140 0.354020 0.387291 0.432000 0.434261 0.0 0.434261 0.432482 0.362845 0.396746 0.363069 0.408905 0.379564 0.308532 0.395332 0.391167 0.365501 (RRBS_trito_pool_2_CGTACTAG.GGCATC, RRBS_trito... 0.010914
5341 0.578588 0.383463 0.657809 0.364685 0.0 0.554576 0.385354 0.579783 0.147914 0.169591 0.190493 0.0 0.190493 0.333452 0.474451 0.657057 0.303389 0.144020 0.241632 0.847206 0.819189 0.586723 0.792750 0.0 0.363391 0.405514 0.355597 0.390829 0.434191 0.426491 0.0 0.426491 0.437169 0.505333 0.379335 0.367937 0.409412 0.386937 0.324087 0.388130 0.391638 0.359355 (RRBS_trito_pool_2_CGTACTAG.GTGAGG, RRBS_trito... 0.000108
5342 0.570237 0.390317 0.653296 0.369337 0.0 0.545606 0.378839 0.569878 0.144309 0.163695 0.189904 0.0 0.189904 0.325804 0.490562 0.659192 0.295185 0.142362 0.237180 0.806703 0.815494 0.581695 0.791910 0.0 0.371193 0.415473 0.363017 0.398879 0.441320 0.428086 0.0 0.428086 0.433289 0.447929 0.384679 0.375237 0.420681 0.386663 0.358924 0.396021 0.397235 0.362169 (RRBS_trito_pool_2_CGTACTAG.GTGAGG, RRBS_trito... 0.005737
5343 0.586276 0.379495 0.651812 0.363612 0.0 0.561891 0.392889 0.586689 0.148492 0.169438 0.198368 0.0 0.198368 0.338861 0.470550 0.662560 0.306687 0.145376 0.250501 0.849384 0.822245 0.593527 0.801248 0.0 0.360007 0.399329 0.353633 0.384305 0.427712 0.419131 0.0 0.419131 0.427949 0.438135 0.380278 0.361030 0.405894 0.382689 0.315792 0.385928 0.390781 0.351394 (RRBS_trito_pool_2_CGTACTAG.GTGAGG, RRBS_trito... 0.015815
5344 0.584896 0.387653 0.653641 0.367875 0.0 0.561765 0.396224 0.585862 0.150446 0.171627 0.195045 0.0 0.195045 0.336922 0.505082 0.672062 0.308365 0.148180 0.249088 0.847561 0.819382 0.587532 0.802768 0.0 0.367114 0.411028 0.359876 0.394723 0.436912 0.422273 0.0 0.422273 0.436985 0.408941 0.391799 0.368553 0.413953 0.388610 0.317672 0.393867 0.399820 0.361619 (RRBS_trito_pool_2_CGTACTAG.GTGAGG, RRBS_trito... 0.004636
5345 0.580572 0.382374 0.655077 0.364479 0.0 0.557763 0.388460 0.582154 0.146946 0.167982 0.195228 0.0 0.195228 0.329934 0.515778 0.662501 0.304139 0.142254 0.239518 0.857511 0.818916 0.583891 0.796629 0.0 0.360781 0.398736 0.354002 0.390221 0.432559 0.431330 0.0 0.431330 0.427617 0.414756 0.379571 0.367810 0.405332 0.380990 0.325042 0.388852 0.395980 0.359672 (RRBS_trito_pool_2_CGTACTAG.GTGAGG, RRBS_trito... 0.000743
5346 0.576155 0.386076 0.655120 0.367315 0.0 0.551418 0.386188 0.575839 0.145272 0.167252 0.190843 0.0 0.190843 0.329725 0.547950 0.659324 0.299986 0.142682 0.241156 0.815930 0.816496 0.589424 0.789253 0.0 0.365310 0.409529 0.356403 0.390474 0.436089 0.428334 0.0 0.428334 0.432797 0.565617 0.384250 0.364330 0.413908 0.380218 0.355485 0.393488 0.384539 0.362223 (RRBS_trito_pool_2_CGTACTAG.GTTGAG, RRBS_trito... 0.005629
5347 0.592194 0.375254 0.653636 0.361591 0.0 0.567703 0.400237 0.592650 0.149455 0.172995 0.199307 0.0 0.199307 0.342781 0.527938 0.662692 0.311487 0.145697 0.254477 0.858611 0.823247 0.601256 0.798591 0.0 0.354124 0.393385 0.347019 0.375900 0.422482 0.419378 0.0 0.419378 0.427457 0.555824 0.379849 0.350123 0.399122 0.376245 0.312353 0.383394 0.378085 0.351447 (RRBS_trito_pool_2_CGTACTAG.GTTGAG, RRBS_trito... 0.015923
5348 0.590814 0.383412 0.655465 0.365854 0.0 0.567577 0.403573 0.591823 0.151409 0.175183 0.195984 0.0 0.195984 0.340842 0.562470 0.672195 0.313165 0.148500 0.253065 0.856788 0.820384 0.595261 0.800111 0.0 0.361231 0.405084 0.353262 0.386318 0.431682 0.422521 0.0 0.422521 0.436493 0.526630 0.391371 0.357646 0.407181 0.382166 0.314232 0.391333 0.387124 0.361673 (RRBS_trito_pool_2_CGTACTAG.GTTGAG, RRBS_trito... 0.004528
5349 0.586491 0.378133 0.656901 0.362458 0.0 0.563575 0.395809 0.588115 0.147909 0.171538 0.196167 0.0 0.196167 0.333854 0.573166 0.662633 0.308940 0.142574 0.243495 0.866738 0.819918 0.591620 0.793972 0.0 0.354898 0.392792 0.347387 0.381816 0.427328 0.431578 0.0 0.431578 0.427125 0.532445 0.379142 0.356903 0.398560 0.374545 0.321602 0.386318 0.383284 0.359726 (RRBS_trito_pool_2_CGTACTAG.GTTGAG, RRBS_trito... 0.000635
5350 0.583842 0.382108 0.649122 0.366242 0.0 0.558733 0.393722 0.582745 0.145849 0.167099 0.198718 0.0 0.198718 0.335134 0.544049 0.664828 0.303284 0.144038 0.250026 0.818109 0.819553 0.596227 0.797751 0.0 0.361926 0.403343 0.354438 0.383950 0.429611 0.420973 0.0 0.420973 0.423576 0.498420 0.385194 0.357422 0.410390 0.375970 0.347190 0.391286 0.383682 0.354262 (RRBS_trito_pool_2_CGTACTAG.TAGCGG, RRBS_trito... 0.021552
5351 0.582463 0.390266 0.650951 0.370506 0.0 0.558607 0.397058 0.581918 0.147804 0.169288 0.195395 0.0 0.195395 0.333195 0.578581 0.674330 0.304962 0.146841 0.248613 0.816285 0.816689 0.590232 0.799270 0.0 0.369033 0.415042 0.360682 0.394368 0.438811 0.424115 0.0 0.424115 0.432612 0.469226 0.396715 0.364945 0.418450 0.381891 0.349069 0.399225 0.392721 0.364487 (RRBS_trito_pool_2_CGTACTAG.TAGCGG, RRBS_trito... 0.001101
5352 0.578139 0.384987 0.652388 0.367110 0.0 0.554605 0.389294 0.578209 0.144303 0.165643 0.195577 0.0 0.195577 0.326207 0.589277 0.664768 0.300736 0.140915 0.239043 0.826235 0.816223 0.586592 0.793132 0.0 0.362700 0.402750 0.354807 0.389866 0.434457 0.433173 0.0 0.433173 0.423244 0.475040 0.384487 0.364203 0.409829 0.374271 0.356439 0.394210 0.388881 0.362541 (RRBS_trito_pool_2_CGTACTAG.TAGCGG, RRBS_trito... 0.004994
5353 0.598502 0.379444 0.649468 0.364781 0.0 0.574892 0.411108 0.598729 0.151987 0.175031 0.203859 0.0 0.203859 0.346251 0.558570 0.677698 0.316463 0.149856 0.261934 0.858966 0.823441 0.602064 0.808609 0.0 0.357847 0.398898 0.351298 0.379794 0.425203 0.415160 0.0 0.415160 0.427272 0.459433 0.392314 0.350738 0.403663 0.377918 0.305938 0.389131 0.386267 0.353712 (RRBS_trito_pool_2_CGTACTAG.TATCTC, RRBS_trito... 0.020451
5354 0.594178 0.374164 0.650904 0.361385 0.0 0.570890 0.403343 0.595020 0.148486 0.171386 0.204042 0.0 0.204042 0.339264 0.569265 0.668136 0.312238 0.143930 0.252364 0.868917 0.822974 0.598423 0.802470 0.0 0.351515 0.386606 0.345423 0.375292 0.420850 0.424217 0.0 0.424217 0.417904 0.465247 0.380085 0.349996 0.395042 0.370298 0.313308 0.384117 0.382427 0.351765 (RRBS_trito_pool_2_CGTACTAG.TATCTC, RRBS_trito... 0.016558
5355 0.592798 0.382323 0.652733 0.365649 0.0 0.570764 0.406679 0.594193 0.150441 0.173574 0.200718 0.0 0.200718 0.337324 0.603798 0.677639 0.313916 0.146733 0.250951 0.867093 0.820111 0.592429 0.803990 0.0 0.358621 0.398305 0.351667 0.385711 0.430049 0.427359 0.0 0.427359 0.426940 0.436053 0.391607 0.357518 0.403102 0.376218 0.315187 0.392055 0.391466 0.361990 (RRBS_trito_pool_2_CGTACTAG.TCTCTG, RRBS_trito... 0.003893

5356 rows × 44 columns

In [125] used -0.355 MiB RAM in 0.276s, peaked 0.293 MiB above current, total RAM usage 267.566 MiB

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