kpmp_picaso_analysis
[7]:
%load_ext autoreload
%autoreload 2
import os, sys
sys.path.insert(0, "../")
print(sys.path)
#otherwise leidenalg is used in an outdated version ...
import networkx as nx
import leidenalg as la
print("la", la.version)
%autoreload 2
from PICASO.kgraph import *
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from collections import defaultdict, Counter
import random
random.seed(42)
import pickle
The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
['../', '../', '', '/opt/jhub/lib/python3.11/site-packages', '/opt/jhub/lib64/python3.11/site-packages', '/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python311.zip', '/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11', '/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/lib-dynload', '/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages', '/opt/jhub/lib/python3.11/site-packages/setuptools/_vendor']
la 0.10.2
[2]:
kg = KGraph()
kg.load_kgraph("../data/initial_base_graph.out")
[3]:
exprKGs = None
exprKGs = pickle.load(open("ctcond_exprkgs.pickle", "rb"))
[ ]:
[4]:
#'ATL_AKI', 'ATL_COV_AKI', 'ATL_DKD', 'ATL_H_CKD', 'ATL_Ref', 'CNT_AKI'
tissue2zone2kg = defaultdict(lambda : dict())
for x in exprKGs:
celltype, condition = x.split("_",1)
tissue2zone2kg[celltype]["{}_{}".format(celltype, condition)] = exprKGs[x]
[5]:
for x in tissue2zone2kg:
print(x, len(tissue2zone2kg[x]), [x for x in tissue2zone2kg[x]])
ATL 5 ['ATL_AKI', 'ATL_COV_AKI', 'ATL_DKD', 'ATL_H_CKD', 'ATL_Ref']
CNT 5 ['CNT_AKI', 'CNT_COV_AKI', 'CNT_DKD', 'CNT_H_CKD', 'CNT_Ref']
DCT 5 ['DCT_AKI', 'DCT_COV_AKI', 'DCT_DKD', 'DCT_H_CKD', 'DCT_Ref']
DTL 5 ['DTL_AKI', 'DTL_COV_AKI', 'DTL_DKD', 'DTL_H_CKD', 'DTL_Ref']
EC 5 ['EC_AKI', 'EC_COV_AKI', 'EC_DKD', 'EC_H_CKD', 'EC_Ref']
FIB 5 ['FIB_AKI', 'FIB_COV_AKI', 'FIB_DKD', 'FIB_H_CKD', 'FIB_Ref']
IC 5 ['IC_AKI', 'IC_COV_AKI', 'IC_DKD', 'IC_H_CKD', 'IC_Ref']
IMM 5 ['IMM_AKI', 'IMM_COV_AKI', 'IMM_DKD', 'IMM_H_CKD', 'IMM_Ref']
NEU 5 ['NEU_AKI', 'NEU_COV_AKI', 'NEU_DKD', 'NEU_H_CKD', 'NEU_Ref']
PC 5 ['PC_AKI', 'PC_COV_AKI', 'PC_DKD', 'PC_H_CKD', 'PC_Ref']
PEC 5 ['PEC_AKI', 'PEC_COV_AKI', 'PEC_DKD', 'PEC_H_CKD', 'PEC_Ref']
POD 5 ['POD_AKI', 'POD_COV_AKI', 'POD_DKD', 'POD_H_CKD', 'POD_Ref']
PT 5 ['PT_AKI', 'PT_COV_AKI', 'PT_DKD', 'PT_H_CKD', 'PT_Ref']
PapE 5 ['PapE_AKI', 'PapE_COV_AKI', 'PapE_DKD', 'PapE_H_CKD', 'PapE_Ref']
TAL 5 ['TAL_AKI', 'TAL_COV_AKI', 'TAL_DKD', 'TAL_H_CKD', 'TAL_Ref']
VSM/P 5 ['VSM/P_AKI', 'VSM/P_COV_AKI', 'VSM/P_DKD', 'VSM/P_H_CKD', 'VSM/P_Ref']
[6]:
renameClass = "VSM/P"
renameElems = tissue2zone2kg[renameClass]
newclass = renameClass.replace("/", "-")
tissue2zone2kg[newclass] = dict()
for x in renameElems:
newElem = x.replace("/", "-")
tissue2zone2kg[newclass][newElem] = renameElems[x]
if renameClass in tissue2zone2kg:
del tissue2zone2kg[renameClass]
for x in tissue2zone2kg:
print(x, len(tissue2zone2kg[x]), [x for x in tissue2zone2kg[x]])
ATL 5 ['ATL_AKI', 'ATL_COV_AKI', 'ATL_DKD', 'ATL_H_CKD', 'ATL_Ref']
CNT 5 ['CNT_AKI', 'CNT_COV_AKI', 'CNT_DKD', 'CNT_H_CKD', 'CNT_Ref']
DCT 5 ['DCT_AKI', 'DCT_COV_AKI', 'DCT_DKD', 'DCT_H_CKD', 'DCT_Ref']
DTL 5 ['DTL_AKI', 'DTL_COV_AKI', 'DTL_DKD', 'DTL_H_CKD', 'DTL_Ref']
EC 5 ['EC_AKI', 'EC_COV_AKI', 'EC_DKD', 'EC_H_CKD', 'EC_Ref']
FIB 5 ['FIB_AKI', 'FIB_COV_AKI', 'FIB_DKD', 'FIB_H_CKD', 'FIB_Ref']
IC 5 ['IC_AKI', 'IC_COV_AKI', 'IC_DKD', 'IC_H_CKD', 'IC_Ref']
IMM 5 ['IMM_AKI', 'IMM_COV_AKI', 'IMM_DKD', 'IMM_H_CKD', 'IMM_Ref']
NEU 5 ['NEU_AKI', 'NEU_COV_AKI', 'NEU_DKD', 'NEU_H_CKD', 'NEU_Ref']
PC 5 ['PC_AKI', 'PC_COV_AKI', 'PC_DKD', 'PC_H_CKD', 'PC_Ref']
PEC 5 ['PEC_AKI', 'PEC_COV_AKI', 'PEC_DKD', 'PEC_H_CKD', 'PEC_Ref']
POD 5 ['POD_AKI', 'POD_COV_AKI', 'POD_DKD', 'POD_H_CKD', 'POD_Ref']
PT 5 ['PT_AKI', 'PT_COV_AKI', 'PT_DKD', 'PT_H_CKD', 'PT_Ref']
PapE 5 ['PapE_AKI', 'PapE_COV_AKI', 'PapE_DKD', 'PapE_H_CKD', 'PapE_Ref']
TAL 5 ['TAL_AKI', 'TAL_COV_AKI', 'TAL_DKD', 'TAL_H_CKD', 'TAL_Ref']
VSM-P 5 ['VSM-P_AKI', 'VSM-P_COV_AKI', 'VSM-P_DKD', 'VSM-P_H_CKD', 'VSM-P_Ref']
[7]:
tissue2zone2kg["FIB"]
[7]:
{'FIB_AKI': KGraph KGraph with 111032 nodes and 1617389 edges,
'FIB_COV_AKI': KGraph KGraph with 111032 nodes and 1617389 edges,
'FIB_DKD': KGraph KGraph with 111032 nodes and 1617389 edges,
'FIB_H_CKD': KGraph KGraph with 111032 nodes and 1617389 edges,
'FIB_Ref': KGraph KGraph with 111032 nodes and 1617389 edges}
[8]:
short_t2z2k = {}
short_t2z2k["FIB"] = tissue2zone2kg["FIB"]
short_t2z2k["EC"] = tissue2zone2kg["EC"]
with open("short_t2z2k.pickle", 'wb') as f:
pickle.dump(short_t2z2k, f)
[9]:
kg = KGraph()
kg.load_kgraph("../data/initial_base_graph.out")
with open("short_t2z2k.pickle", 'rb') as f:
short_t2z2k = pickle.load(f)
[9]:
zoneSort = {
"Ref": 0,
"AKI": 1,
"COV_AKI": 2,
"DKD": 3,
"H_CKD": 4
}
[10]:
tlda = TwoLevelDifferentialAnalysis(tissue2zone2kg, [x for x in zoneSort], output_folder_formatter="diff_{}/", fullKG=kg)
[11]:
tlda.calculate_modules(relevant_cellgroups=None)
ATL
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/scipy/stats/_stats_py.py:6556: RuntimeWarning: invalid value encountered in scalar divide
svar = ((n1 - 1) * v1 + (n2 - 1) * v2) / df
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.
return _methods._mean(a, axis=axis, dtype=dtype,
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
ret = ret.dtype.type(ret / rcount)
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1113: RuntimeWarning: divide by zero encountered in divide
var *= np.divide(n, n-ddof) # to avoid error on division by zero
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1113: RuntimeWarning: invalid value encountered in scalar multiply
var *= np.divide(n, n-ddof) # to avoid error on division by zero
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/scipy/stats/_axis_nan_policy.py:573: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.
res = hypotest_fun_out(*samples, **kwds)
{'ATL_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'ATL_COV_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'ATL_DKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'ATL_H_CKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing ATL_AKI
Identified communities
Number of communities: 258
Average community size 35.33720930232558
Median community size 28.5
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 18. 28.5 43. 212. ]
Significant communities
Number of communities: 194
Average community size 38.65979381443299
Median community size 31.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 20. 31. 47. 212.]
ATL_AKI_mod_229 11
ATL_AKI_mod_127 20
ATL_AKI_mod_208 17
ATL_AKI_mod_110 21
ATL_AKI_mod_152 47
ATL_AKI_mod_48 29
ATL_AKI_mod_133 16
ATL_AKI_mod_179 18
ATL_AKI_mod_15 55
ATL_AKI_mod_5 157
ATL_AKI_mod_33 15
ATL_AKI_mod_126 10
ATL_AKI_mod_186 69
ATL_AKI_mod_134 88
ATL_AKI_mod_78 32
ATL_AKI_mod_221 24
ATL_AKI_mod_218 16
ATL_AKI_mod_58 115
ATL_AKI_mod_17 100
ATL_AKI_mod_80 80
ATL_AKI_mod_97 40
ATL_AKI_mod_205 14
ATL_AKI_mod_151 23
ATL_AKI_mod_164 72
ATL_AKI_mod_140 43
ATL_AKI_mod_180 17
ATL_AKI_mod_52 52
ATL_AKI_mod_145 15
ATL_AKI_mod_22 62
ATL_AKI_mod_124 14
ATL_AKI_mod_202 19
ATL_AKI_mod_220 13
ATL_AKI_mod_231 12
ATL_AKI_mod_129 88
ATL_AKI_mod_3 39
ATL_AKI_mod_72 34
ATL_AKI_mod_148 12
ATL_AKI_mod_209 29
ATL_AKI_mod_242 22
ATL_AKI_mod_4 42
ATL_AKI_mod_24 11
ATL_AKI_mod_162 64
ATL_AKI_mod_181 22
ATL_AKI_mod_43 212
ATL_AKI_mod_245 23
ATL_AKI_mod_23 52
ATL_AKI_mod_219 25
ATL_AKI_mod_240 20
ATL_AKI_mod_55 77
ATL_AKI_mod_107 39
ATL_AKI_mod_50 90
ATL_AKI_mod_32 49
ATL_AKI_mod_178 10
ATL_AKI_mod_0 21
ATL_AKI_mod_49 32
ATL_AKI_mod_172 16
ATL_AKI_mod_170 17
ATL_AKI_mod_63 22
ATL_AKI_mod_131 18
ATL_AKI_mod_154 65
ATL_AKI_mod_81 50
ATL_AKI_mod_108 10
ATL_AKI_mod_102 39
ATL_AKI_mod_191 45
ATL_AKI_mod_60 76
ATL_AKI_mod_36 11
ATL_AKI_mod_71 60
ATL_AKI_mod_8 18
ATL_AKI_mod_64 34
ATL_AKI_mod_204 28
ATL_AKI_mod_128 20
ATL_AKI_mod_109 15
ATL_AKI_mod_139 65
ATL_AKI_mod_7 81
ATL_AKI_mod_119 25
ATL_AKI_mod_96 51
ATL_AKI_mod_98 21
ATL_AKI_mod_38 50
ATL_AKI_mod_106 30
ATL_AKI_mod_70 22
ATL_AKI_mod_185 18
ATL_AKI_mod_13 64
ATL_AKI_mod_51 46
ATL_AKI_mod_135 30
ATL_AKI_mod_53 52
ATL_AKI_mod_77 46
ATL_AKI_mod_197 23
ATL_AKI_mod_195 29
ATL_AKI_mod_214 19
ATL_AKI_mod_76 20
ATL_AKI_mod_21 34
ATL_AKI_mod_239 16
ATL_AKI_mod_230 30
ATL_AKI_mod_69 48
ATL_AKI_mod_1 12
ATL_AKI_mod_79 82
ATL_AKI_mod_203 32
ATL_AKI_mod_67 31
ATL_AKI_mod_225 26
ATL_AKI_mod_14 16
ATL_AKI_mod_66 30
ATL_AKI_mod_238 17
ATL_AKI_mod_168 22
ATL_AKI_mod_138 27
ATL_AKI_mod_117 25
ATL_AKI_mod_113 31
ATL_AKI_mod_149 39
ATL_AKI_mod_227 12
ATL_AKI_mod_252 42
ATL_AKI_mod_120 63
ATL_AKI_mod_144 39
ATL_AKI_mod_41 85
ATL_AKI_mod_226 29
ATL_AKI_mod_83 33
ATL_AKI_mod_189 41
ATL_AKI_mod_101 36
ATL_AKI_mod_194 24
ATL_AKI_mod_163 32
ATL_AKI_mod_29 94
ATL_AKI_mod_146 10
ATL_AKI_mod_257 19
ATL_AKI_mod_19 46
ATL_AKI_mod_59 33
ATL_AKI_mod_45 111
ATL_AKI_mod_249 66
ATL_AKI_mod_192 23
ATL_AKI_mod_61 18
ATL_AKI_mod_254 42
ATL_AKI_mod_132 36
ATL_AKI_mod_92 78
ATL_AKI_mod_177 53
ATL_AKI_mod_256 22
ATL_AKI_mod_167 21
ATL_AKI_mod_94 57
ATL_AKI_mod_122 42
ATL_AKI_mod_173 18
ATL_AKI_mod_87 22
ATL_AKI_mod_95 39
ATL_AKI_mod_25 75
ATL_AKI_mod_125 55
ATL_AKI_mod_222 12
ATL_AKI_mod_153 28
ATL_AKI_mod_74 40
ATL_AKI_mod_228 32
ATL_AKI_mod_176 21
ATL_AKI_mod_40 82
ATL_AKI_mod_250 20
ATL_AKI_mod_206 13
ATL_AKI_mod_16 19
ATL_AKI_mod_84 32
ATL_AKI_mod_150 29
ATL_AKI_mod_207 35
ATL_AKI_mod_182 51
ATL_AKI_mod_160 24
ATL_AKI_mod_37 47
ATL_AKI_mod_246 24
ATL_AKI_mod_130 47
ATL_AKI_mod_233 18
ATL_AKI_mod_184 31
ATL_AKI_mod_20 14
ATL_AKI_mod_243 20
ATL_AKI_mod_65 20
ATL_AKI_mod_155 43
ATL_AKI_mod_223 38
ATL_AKI_mod_103 34
ATL_AKI_mod_54 107
ATL_AKI_mod_200 24
ATL_AKI_mod_212 17
ATL_AKI_mod_235 15
ATL_AKI_mod_193 33
ATL_AKI_mod_198 19
ATL_AKI_mod_12 40
ATL_AKI_mod_10 118
ATL_AKI_mod_90 152
ATL_AKI_mod_171 34
ATL_AKI_mod_57 26
ATL_AKI_mod_85 43
ATL_AKI_mod_188 31
ATL_AKI_mod_9 33
ATL_AKI_mod_89 29
ATL_AKI_mod_224 15
ATL_AKI_mod_111 67
ATL_AKI_mod_143 41
ATL_AKI_mod_187 28
ATL_AKI_mod_88 22
ATL_AKI_mod_114 35
ATL_AKI_mod_39 80
ATL_AKI_mod_159 18
ATL_AKI_mod_237 14
ATL_AKI_mod_42 46
ATL_AKI_mod_104 12
ATL_AKI_mod_6 49
ATL_AKI_mod_199 32
ATL_AKI_mod_56 27
Number of saved communities: 94
Analysing ATL_COV_AKI
Identified communities
Number of communities: 255
Average community size 26.24705882352941
Median community size 21.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 12. 21. 35. 206.]
Significant communities
Number of communities: 74
Average community size 25.7972972972973
Median community size 20.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 15.25 20. 30.75 101. ]
ATL_COV_AKI_mod_117 51
ATL_COV_AKI_mod_180 21
ATL_COV_AKI_mod_65 22
ATL_COV_AKI_mod_203 10
ATL_COV_AKI_mod_10 43
ATL_COV_AKI_mod_127 19
ATL_COV_AKI_mod_47 15
ATL_COV_AKI_mod_3 50
ATL_COV_AKI_mod_130 12
ATL_COV_AKI_mod_204 15
ATL_COV_AKI_mod_18 29
ATL_COV_AKI_mod_194 14
ATL_COV_AKI_mod_80 55
ATL_COV_AKI_mod_42 49
ATL_COV_AKI_mod_74 19
ATL_COV_AKI_mod_151 10
ATL_COV_AKI_mod_104 20
ATL_COV_AKI_mod_222 53
ATL_COV_AKI_mod_112 29
ATL_COV_AKI_mod_178 18
ATL_COV_AKI_mod_154 15
ATL_COV_AKI_mod_27 14
ATL_COV_AKI_mod_182 17
ATL_COV_AKI_mod_82 13
ATL_COV_AKI_mod_115 40
ATL_COV_AKI_mod_14 38
ATL_COV_AKI_mod_139 13
ATL_COV_AKI_mod_121 30
ATL_COV_AKI_mod_108 15
ATL_COV_AKI_mod_140 20
ATL_COV_AKI_mod_39 101
ATL_COV_AKI_mod_107 55
ATL_COV_AKI_mod_202 13
ATL_COV_AKI_mod_131 12
ATL_COV_AKI_mod_116 13
ATL_COV_AKI_mod_22 35
ATL_COV_AKI_mod_159 11
ATL_COV_AKI_mod_19 32
ATL_COV_AKI_mod_36 46
ATL_COV_AKI_mod_158 27
ATL_COV_AKI_mod_153 23
ATL_COV_AKI_mod_173 17
ATL_COV_AKI_mod_90 39
ATL_COV_AKI_mod_75 24
ATL_COV_AKI_mod_192 19
ATL_COV_AKI_mod_38 26
ATL_COV_AKI_mod_106 19
ATL_COV_AKI_mod_137 18
ATL_COV_AKI_mod_58 19
ATL_COV_AKI_mod_172 24
ATL_COV_AKI_mod_160 38
ATL_COV_AKI_mod_214 23
ATL_COV_AKI_mod_189 16
ATL_COV_AKI_mod_206 10
ATL_COV_AKI_mod_57 23
ATL_COV_AKI_mod_59 11
ATL_COV_AKI_mod_89 52
ATL_COV_AKI_mod_169 24
ATL_COV_AKI_mod_62 16
ATL_COV_AKI_mod_92 31
ATL_COV_AKI_mod_211 22
ATL_COV_AKI_mod_148 30
ATL_COV_AKI_mod_133 16
ATL_COV_AKI_mod_164 16
ATL_COV_AKI_mod_88 30
ATL_COV_AKI_mod_200 16
ATL_COV_AKI_mod_43 24
ATL_COV_AKI_mod_187 15
ATL_COV_AKI_mod_195 12
ATL_COV_AKI_mod_168 20
ATL_COV_AKI_mod_226 20
ATL_COV_AKI_mod_191 43
ATL_COV_AKI_mod_49 40
ATL_COV_AKI_mod_79 19
Number of saved communities: 15
Analysing ATL_DKD
Identified communities
Number of communities: 437
Average community size 6.345537757437071
Median community size 2.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 1. 2. 9. 167.]
Significant communities
Number of communities: 8
Average community size 33.5
Median community size 17.5
Quantile (0,0.25,0.5,0.75,1) community size [ 11. 13.75 17.5 23.5 147. ]
ATL_DKD_mod_31 25
ATL_DKD_mod_2 23
ATL_DKD_mod_79 16
ATL_DKD_mod_153 11
ATL_DKD_mod_145 19
ATL_DKD_mod_52 14
ATL_DKD_mod_105 13
ATL_DKD_mod_6 147
Number of saved communities: 0
Analysing ATL_H_CKD
Identified communities
Number of communities: 259
Average community size 9.988416988416988
Median community size 6.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 2. 6. 14. 144.]
Significant communities
Number of communities: 5
Average community size 16.8
Median community size 16.0
Quantile (0,0.25,0.5,0.75,1) community size [10. 13. 16. 17. 28.]
ATL_H_CKD_mod_24 28
ATL_H_CKD_mod_29 13
ATL_H_CKD_mod_13 10
ATL_H_CKD_mod_165 16
ATL_H_CKD_mod_44 17
Number of saved communities: 0
CNT
{'CNT_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'CNT_COV_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'CNT_DKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'CNT_H_CKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing CNT_AKI
Identified communities
Number of communities: 276
Average community size 42.19202898550725
Median community size 31.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 19. 31. 55.25 384. ]
Significant communities
Number of communities: 60
Average community size 50.15
Median community size 33.5
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 20. 33.5 65.25 384. ]
CNT_AKI_mod_189 12
CNT_AKI_mod_56 20
CNT_AKI_mod_113 36
CNT_AKI_mod_44 384
CNT_AKI_mod_59 105
CNT_AKI_mod_108 22
CNT_AKI_mod_124 15
CNT_AKI_mod_76 88
CNT_AKI_mod_176 20
CNT_AKI_mod_63 43
CNT_AKI_mod_230 12
CNT_AKI_mod_139 14
CNT_AKI_mod_241 13
CNT_AKI_mod_195 30
CNT_AKI_mod_239 17
CNT_AKI_mod_3 39
CNT_AKI_mod_220 25
CNT_AKI_mod_158 35
CNT_AKI_mod_267 13
CNT_AKI_mod_174 18
CNT_AKI_mod_223 23
CNT_AKI_mod_100 10
CNT_AKI_mod_0 153
CNT_AKI_mod_30 67
CNT_AKI_mod_37 65
CNT_AKI_mod_154 36
CNT_AKI_mod_90 128
CNT_AKI_mod_254 17
CNT_AKI_mod_35 101
CNT_AKI_mod_173 24
CNT_AKI_mod_91 97
CNT_AKI_mod_77 85
CNT_AKI_mod_58 60
CNT_AKI_mod_149 44
CNT_AKI_mod_109 22
CNT_AKI_mod_120 37
CNT_AKI_mod_105 97
CNT_AKI_mod_253 13
CNT_AKI_mod_157 61
CNT_AKI_mod_95 54
CNT_AKI_mod_163 27
CNT_AKI_mod_114 97
CNT_AKI_mod_45 50
CNT_AKI_mod_106 82
CNT_AKI_mod_182 43
CNT_AKI_mod_9 66
CNT_AKI_mod_49 29
CNT_AKI_mod_164 87
CNT_AKI_mod_204 36
CNT_AKI_mod_101 32
CNT_AKI_mod_168 27
CNT_AKI_mod_41 13
CNT_AKI_mod_127 69
CNT_AKI_mod_217 26
CNT_AKI_mod_104 25
CNT_AKI_mod_256 18
CNT_AKI_mod_160 20
CNT_AKI_mod_126 58
CNT_AKI_mod_151 18
CNT_AKI_mod_162 31
Number of saved communities: 32
Analysing CNT_COV_AKI
Identified communities
Number of communities: 303
Average community size 41.7029702970297
Median community size 33.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 18. 33. 56.5 222. ]
Significant communities
Number of communities: 188
Average community size 45.59574468085106
Median community size 35.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 24. 35. 60. 222.]
CNT_COV_AKI_mod_121 17
CNT_COV_AKI_mod_71 10
CNT_COV_AKI_mod_1 26
CNT_COV_AKI_mod_86 24
CNT_COV_AKI_mod_227 42
CNT_COV_AKI_mod_263 16
CNT_COV_AKI_mod_63 14
CNT_COV_AKI_mod_156 20
CNT_COV_AKI_mod_104 74
CNT_COV_AKI_mod_149 19
CNT_COV_AKI_mod_199 22
CNT_COV_AKI_mod_168 32
CNT_COV_AKI_mod_102 10
CNT_COV_AKI_mod_166 71
CNT_COV_AKI_mod_62 12
CNT_COV_AKI_mod_131 38
CNT_COV_AKI_mod_157 81
CNT_COV_AKI_mod_64 32
CNT_COV_AKI_mod_253 34
CNT_COV_AKI_mod_272 36
CNT_COV_AKI_mod_155 98
CNT_COV_AKI_mod_128 33
CNT_COV_AKI_mod_220 54
CNT_COV_AKI_mod_114 21
CNT_COV_AKI_mod_135 19
CNT_COV_AKI_mod_61 23
CNT_COV_AKI_mod_6 23
CNT_COV_AKI_mod_107 25
CNT_COV_AKI_mod_31 222
CNT_COV_AKI_mod_288 19
CNT_COV_AKI_mod_78 52
CNT_COV_AKI_mod_109 12
CNT_COV_AKI_mod_151 25
CNT_COV_AKI_mod_265 19
CNT_COV_AKI_mod_130 20
CNT_COV_AKI_mod_176 68
CNT_COV_AKI_mod_96 39
CNT_COV_AKI_mod_266 22
CNT_COV_AKI_mod_54 49
CNT_COV_AKI_mod_241 84
CNT_COV_AKI_mod_0 17
CNT_COV_AKI_mod_147 14
CNT_COV_AKI_mod_197 53
CNT_COV_AKI_mod_226 83
CNT_COV_AKI_mod_237 42
CNT_COV_AKI_mod_24 25
CNT_COV_AKI_mod_28 132
CNT_COV_AKI_mod_154 24
CNT_COV_AKI_mod_171 14
CNT_COV_AKI_mod_280 15
CNT_COV_AKI_mod_23 137
CNT_COV_AKI_mod_141 25
CNT_COV_AKI_mod_82 40
CNT_COV_AKI_mod_179 33
CNT_COV_AKI_mod_284 63
CNT_COV_AKI_mod_228 88
CNT_COV_AKI_mod_17 77
CNT_COV_AKI_mod_186 14
CNT_COV_AKI_mod_233 25
CNT_COV_AKI_mod_15 207
CNT_COV_AKI_mod_55 32
CNT_COV_AKI_mod_214 62
CNT_COV_AKI_mod_221 85
CNT_COV_AKI_mod_113 11
CNT_COV_AKI_mod_48 137
CNT_COV_AKI_mod_122 11
CNT_COV_AKI_mod_273 24
CNT_COV_AKI_mod_37 59
CNT_COV_AKI_mod_267 32
CNT_COV_AKI_mod_256 20
CNT_COV_AKI_mod_153 35
CNT_COV_AKI_mod_282 35
CNT_COV_AKI_mod_126 38
CNT_COV_AKI_mod_72 53
CNT_COV_AKI_mod_9 67
CNT_COV_AKI_mod_3 49
CNT_COV_AKI_mod_236 45
CNT_COV_AKI_mod_148 15
CNT_COV_AKI_mod_249 28
CNT_COV_AKI_mod_69 24
CNT_COV_AKI_mod_2 60
CNT_COV_AKI_mod_91 64
CNT_COV_AKI_mod_286 45
CNT_COV_AKI_mod_279 24
CNT_COV_AKI_mod_291 69
CNT_COV_AKI_mod_110 50
CNT_COV_AKI_mod_18 53
CNT_COV_AKI_mod_89 31
CNT_COV_AKI_mod_70 49
CNT_COV_AKI_mod_295 26
CNT_COV_AKI_mod_178 41
CNT_COV_AKI_mod_51 144
CNT_COV_AKI_mod_75 65
CNT_COV_AKI_mod_143 48
CNT_COV_AKI_mod_183 89
CNT_COV_AKI_mod_222 11
CNT_COV_AKI_mod_172 76
CNT_COV_AKI_mod_224 80
CNT_COV_AKI_mod_232 37
CNT_COV_AKI_mod_93 72
CNT_COV_AKI_mod_254 41
CNT_COV_AKI_mod_293 34
CNT_COV_AKI_mod_260 22
CNT_COV_AKI_mod_66 25
CNT_COV_AKI_mod_160 71
CNT_COV_AKI_mod_275 57
CNT_COV_AKI_mod_169 33
CNT_COV_AKI_mod_42 38
CNT_COV_AKI_mod_252 28
CNT_COV_AKI_mod_245 27
CNT_COV_AKI_mod_136 109
CNT_COV_AKI_mod_194 31
CNT_COV_AKI_mod_77 61
CNT_COV_AKI_mod_170 18
CNT_COV_AKI_mod_53 95
CNT_COV_AKI_mod_98 78
CNT_COV_AKI_mod_38 112
CNT_COV_AKI_mod_16 34
CNT_COV_AKI_mod_240 16
CNT_COV_AKI_mod_292 46
CNT_COV_AKI_mod_184 27
CNT_COV_AKI_mod_125 51
CNT_COV_AKI_mod_133 21
CNT_COV_AKI_mod_274 45
CNT_COV_AKI_mod_20 12
CNT_COV_AKI_mod_76 28
CNT_COV_AKI_mod_111 31
CNT_COV_AKI_mod_257 19
CNT_COV_AKI_mod_145 49
CNT_COV_AKI_mod_27 94
CNT_COV_AKI_mod_134 42
CNT_COV_AKI_mod_58 45
CNT_COV_AKI_mod_41 10
CNT_COV_AKI_mod_262 44
CNT_COV_AKI_mod_204 40
CNT_COV_AKI_mod_190 60
CNT_COV_AKI_mod_250 25
CNT_COV_AKI_mod_298 60
CNT_COV_AKI_mod_108 26
CNT_COV_AKI_mod_212 39
CNT_COV_AKI_mod_14 31
CNT_COV_AKI_mod_225 87
CNT_COV_AKI_mod_208 27
CNT_COV_AKI_mod_4 28
CNT_COV_AKI_mod_219 52
CNT_COV_AKI_mod_195 32
CNT_COV_AKI_mod_300 10
CNT_COV_AKI_mod_35 17
CNT_COV_AKI_mod_132 18
CNT_COV_AKI_mod_246 34
CNT_COV_AKI_mod_283 51
CNT_COV_AKI_mod_290 56
CNT_COV_AKI_mod_12 61
CNT_COV_AKI_mod_213 34
CNT_COV_AKI_mod_88 56
CNT_COV_AKI_mod_218 29
CNT_COV_AKI_mod_239 43
CNT_COV_AKI_mod_127 72
CNT_COV_AKI_mod_5 13
CNT_COV_AKI_mod_118 25
CNT_COV_AKI_mod_211 76
CNT_COV_AKI_mod_57 39
CNT_COV_AKI_mod_261 12
CNT_COV_AKI_mod_271 25
CNT_COV_AKI_mod_36 54
CNT_COV_AKI_mod_259 68
CNT_COV_AKI_mod_60 31
CNT_COV_AKI_mod_68 70
CNT_COV_AKI_mod_268 135
CNT_COV_AKI_mod_182 19
CNT_COV_AKI_mod_217 15
CNT_COV_AKI_mod_187 51
CNT_COV_AKI_mod_83 32
CNT_COV_AKI_mod_8 44
CNT_COV_AKI_mod_206 104
CNT_COV_AKI_mod_142 65
CNT_COV_AKI_mod_139 112
CNT_COV_AKI_mod_229 11
CNT_COV_AKI_mod_159 76
CNT_COV_AKI_mod_39 67
CNT_COV_AKI_mod_152 31
CNT_COV_AKI_mod_56 31
CNT_COV_AKI_mod_244 55
CNT_COV_AKI_mod_59 43
CNT_COV_AKI_mod_164 33
CNT_COV_AKI_mod_46 14
CNT_COV_AKI_mod_251 35
CNT_COV_AKI_mod_243 23
Number of saved communities: 121
Analysing CNT_DKD
Identified communities
Number of communities: 223
Average community size 9.134529147982063
Median community size 3.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 2. 3. 12. 164.]
Significant communities
Number of communities: 1
Average community size 21.0
Median community size 21.0
Quantile (0,0.25,0.5,0.75,1) community size [21. 21. 21. 21. 21.]
CNT_DKD_mod_30 21
Number of saved communities: 0
Analysing CNT_H_CKD
Identified communities
Number of communities: 277
Average community size 17.050541516245488
Median community size 13.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 2. 13. 25. 178.]
Significant communities
Number of communities: 28
Average community size 32.82142857142857
Median community size 23.5
Quantile (0,0.25,0.5,0.75,1) community size [10. 17.75 23.5 44.25 86. ]
CNT_H_CKD_mod_73 10
CNT_H_CKD_mod_37 13
CNT_H_CKD_mod_125 18
CNT_H_CKD_mod_16 21
CNT_H_CKD_mod_86 15
CNT_H_CKD_mod_103 45
CNT_H_CKD_mod_28 49
CNT_H_CKD_mod_59 17
CNT_H_CKD_mod_43 86
CNT_H_CKD_mod_51 72
CNT_H_CKD_mod_14 61
CNT_H_CKD_mod_106 41
CNT_H_CKD_mod_74 63
CNT_H_CKD_mod_96 25
CNT_H_CKD_mod_117 19
CNT_H_CKD_mod_60 19
CNT_H_CKD_mod_66 17
CNT_H_CKD_mod_128 32
CNT_H_CKD_mod_205 15
CNT_H_CKD_mod_22 40
CNT_H_CKD_mod_91 32
CNT_H_CKD_mod_62 20
CNT_H_CKD_mod_61 10
CNT_H_CKD_mod_18 44
CNT_H_CKD_mod_9 22
CNT_H_CKD_mod_101 65
CNT_H_CKD_mod_42 19
CNT_H_CKD_mod_33 29
Number of saved communities: 2
DCT
{'DCT_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'DCT_COV_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'DCT_DKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'DCT_H_CKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing DCT_AKI
Identified communities
Number of communities: 298
Average community size 37.76510067114094
Median community size 28.5
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 16.25 28.5 49. 180. ]
Significant communities
Number of communities: 66
Average community size 52.18181818181818
Median community size 44.5
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 24.5 44.5 71.25 145. ]
DCT_AKI_mod_109 22
DCT_AKI_mod_178 13
DCT_AKI_mod_233 24
DCT_AKI_mod_242 13
DCT_AKI_mod_74 39
DCT_AKI_mod_205 28
DCT_AKI_mod_105 12
DCT_AKI_mod_11 130
DCT_AKI_mod_16 111
DCT_AKI_mod_149 23
DCT_AKI_mod_17 22
DCT_AKI_mod_227 76
DCT_AKI_mod_56 55
DCT_AKI_mod_113 32
DCT_AKI_mod_96 113
DCT_AKI_mod_258 45
DCT_AKI_mod_39 48
DCT_AKI_mod_279 10
DCT_AKI_mod_158 24
DCT_AKI_mod_134 41
DCT_AKI_mod_172 24
DCT_AKI_mod_97 61
DCT_AKI_mod_245 33
DCT_AKI_mod_14 33
DCT_AKI_mod_117 40
DCT_AKI_mod_67 20
DCT_AKI_mod_8 94
DCT_AKI_mod_291 15
DCT_AKI_mod_65 101
DCT_AKI_mod_166 72
DCT_AKI_mod_100 74
DCT_AKI_mod_207 43
DCT_AKI_mod_58 58
DCT_AKI_mod_44 78
DCT_AKI_mod_95 131
DCT_AKI_mod_151 42
DCT_AKI_mod_26 85
DCT_AKI_mod_177 56
DCT_AKI_mod_294 17
DCT_AKI_mod_116 21
DCT_AKI_mod_136 67
DCT_AKI_mod_199 38
DCT_AKI_mod_217 31
DCT_AKI_mod_243 17
DCT_AKI_mod_171 26
DCT_AKI_mod_106 49
DCT_AKI_mod_203 48
DCT_AKI_mod_130 84
DCT_AKI_mod_9 55
DCT_AKI_mod_45 128
DCT_AKI_mod_35 69
DCT_AKI_mod_41 59
DCT_AKI_mod_43 49
DCT_AKI_mod_46 61
DCT_AKI_mod_30 41
DCT_AKI_mod_4 44
DCT_AKI_mod_50 34
DCT_AKI_mod_33 145
DCT_AKI_mod_195 39
DCT_AKI_mod_133 60
DCT_AKI_mod_257 16
DCT_AKI_mod_13 74
DCT_AKI_mod_168 81
DCT_AKI_mod_87 91
DCT_AKI_mod_38 46
DCT_AKI_mod_286 13
Number of saved communities: 33
Analysing DCT_COV_AKI
Identified communities
Number of communities: 300
Average community size 42.75333333333333
Median community size 33.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 17.75 33. 54. 201. ]
Significant communities
Number of communities: 175
Average community size 48.02857142857143
Median community size 36.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 19. 36. 65. 200.]
DCT_COV_AKI_mod_271 11
DCT_COV_AKI_mod_133 16
DCT_COV_AKI_mod_236 18
DCT_COV_AKI_mod_106 34
DCT_COV_AKI_mod_111 14
DCT_COV_AKI_mod_95 59
DCT_COV_AKI_mod_206 12
DCT_COV_AKI_mod_233 67
DCT_COV_AKI_mod_194 19
DCT_COV_AKI_mod_31 72
DCT_COV_AKI_mod_156 15
DCT_COV_AKI_mod_41 18
DCT_COV_AKI_mod_180 13
DCT_COV_AKI_mod_59 50
DCT_COV_AKI_mod_77 21
DCT_COV_AKI_mod_116 16
DCT_COV_AKI_mod_196 34
DCT_COV_AKI_mod_154 33
DCT_COV_AKI_mod_274 11
DCT_COV_AKI_mod_25 61
DCT_COV_AKI_mod_265 35
DCT_COV_AKI_mod_21 24
DCT_COV_AKI_mod_14 143
DCT_COV_AKI_mod_102 78
DCT_COV_AKI_mod_177 13
DCT_COV_AKI_mod_159 28
DCT_COV_AKI_mod_18 36
DCT_COV_AKI_mod_17 174
DCT_COV_AKI_mod_117 67
DCT_COV_AKI_mod_241 13
DCT_COV_AKI_mod_183 17
DCT_COV_AKI_mod_174 30
DCT_COV_AKI_mod_123 19
DCT_COV_AKI_mod_252 26
DCT_COV_AKI_mod_181 38
DCT_COV_AKI_mod_280 27
DCT_COV_AKI_mod_232 36
DCT_COV_AKI_mod_47 162
DCT_COV_AKI_mod_118 52
DCT_COV_AKI_mod_148 17
DCT_COV_AKI_mod_137 54
DCT_COV_AKI_mod_213 12
DCT_COV_AKI_mod_115 39
DCT_COV_AKI_mod_69 41
DCT_COV_AKI_mod_10 92
DCT_COV_AKI_mod_105 51
DCT_COV_AKI_mod_242 15
DCT_COV_AKI_mod_98 29
DCT_COV_AKI_mod_278 22
DCT_COV_AKI_mod_53 20
DCT_COV_AKI_mod_67 12
DCT_COV_AKI_mod_39 102
DCT_COV_AKI_mod_72 33
DCT_COV_AKI_mod_131 12
DCT_COV_AKI_mod_61 37
DCT_COV_AKI_mod_179 51
DCT_COV_AKI_mod_23 163
DCT_COV_AKI_mod_162 94
DCT_COV_AKI_mod_205 47
DCT_COV_AKI_mod_200 13
DCT_COV_AKI_mod_88 48
DCT_COV_AKI_mod_290 12
DCT_COV_AKI_mod_34 48
DCT_COV_AKI_mod_268 18
DCT_COV_AKI_mod_269 29
DCT_COV_AKI_mod_169 99
DCT_COV_AKI_mod_263 35
DCT_COV_AKI_mod_1 64
DCT_COV_AKI_mod_247 26
DCT_COV_AKI_mod_253 18
DCT_COV_AKI_mod_130 51
DCT_COV_AKI_mod_78 123
DCT_COV_AKI_mod_147 22
DCT_COV_AKI_mod_84 131
DCT_COV_AKI_mod_99 138
DCT_COV_AKI_mod_138 30
DCT_COV_AKI_mod_291 13
DCT_COV_AKI_mod_261 13
DCT_COV_AKI_mod_160 52
DCT_COV_AKI_mod_276 17
DCT_COV_AKI_mod_103 117
DCT_COV_AKI_mod_158 54
DCT_COV_AKI_mod_211 69
DCT_COV_AKI_mod_7 55
DCT_COV_AKI_mod_4 100
DCT_COV_AKI_mod_259 10
DCT_COV_AKI_mod_44 127
DCT_COV_AKI_mod_9 184
DCT_COV_AKI_mod_5 26
DCT_COV_AKI_mod_287 13
DCT_COV_AKI_mod_76 46
DCT_COV_AKI_mod_136 121
DCT_COV_AKI_mod_86 79
DCT_COV_AKI_mod_140 26
DCT_COV_AKI_mod_127 17
DCT_COV_AKI_mod_113 93
DCT_COV_AKI_mod_219 17
DCT_COV_AKI_mod_146 86
DCT_COV_AKI_mod_165 28
DCT_COV_AKI_mod_132 14
DCT_COV_AKI_mod_97 53
DCT_COV_AKI_mod_50 122
DCT_COV_AKI_mod_141 24
DCT_COV_AKI_mod_42 74
DCT_COV_AKI_mod_91 52
DCT_COV_AKI_mod_258 16
DCT_COV_AKI_mod_79 38
DCT_COV_AKI_mod_157 60
DCT_COV_AKI_mod_3 85
DCT_COV_AKI_mod_277 12
DCT_COV_AKI_mod_110 10
DCT_COV_AKI_mod_63 47
DCT_COV_AKI_mod_93 94
DCT_COV_AKI_mod_184 26
DCT_COV_AKI_mod_191 15
DCT_COV_AKI_mod_155 30
DCT_COV_AKI_mod_129 25
DCT_COV_AKI_mod_71 39
DCT_COV_AKI_mod_107 41
DCT_COV_AKI_mod_142 54
DCT_COV_AKI_mod_27 83
DCT_COV_AKI_mod_26 76
DCT_COV_AKI_mod_51 76
DCT_COV_AKI_mod_92 44
DCT_COV_AKI_mod_171 48
DCT_COV_AKI_mod_249 19
DCT_COV_AKI_mod_202 37
DCT_COV_AKI_mod_46 52
DCT_COV_AKI_mod_151 34
DCT_COV_AKI_mod_260 19
DCT_COV_AKI_mod_8 75
DCT_COV_AKI_mod_139 33
DCT_COV_AKI_mod_35 125
DCT_COV_AKI_mod_134 56
DCT_COV_AKI_mod_243 17
DCT_COV_AKI_mod_109 31
DCT_COV_AKI_mod_256 16
DCT_COV_AKI_mod_80 70
DCT_COV_AKI_mod_124 21
DCT_COV_AKI_mod_210 19
DCT_COV_AKI_mod_161 20
DCT_COV_AKI_mod_197 66
DCT_COV_AKI_mod_6 23
DCT_COV_AKI_mod_231 34
DCT_COV_AKI_mod_275 39
DCT_COV_AKI_mod_114 19
DCT_COV_AKI_mod_204 29
DCT_COV_AKI_mod_12 87
DCT_COV_AKI_mod_49 62
DCT_COV_AKI_mod_220 44
DCT_COV_AKI_mod_176 40
DCT_COV_AKI_mod_36 88
DCT_COV_AKI_mod_16 88
DCT_COV_AKI_mod_172 37
DCT_COV_AKI_mod_55 45
DCT_COV_AKI_mod_32 86
DCT_COV_AKI_mod_163 30
DCT_COV_AKI_mod_64 66
DCT_COV_AKI_mod_186 21
DCT_COV_AKI_mod_212 76
DCT_COV_AKI_mod_270 12
DCT_COV_AKI_mod_282 15
DCT_COV_AKI_mod_11 67
DCT_COV_AKI_mod_234 20
DCT_COV_AKI_mod_192 51
DCT_COV_AKI_mod_66 19
DCT_COV_AKI_mod_189 30
DCT_COV_AKI_mod_240 25
DCT_COV_AKI_mod_221 34
DCT_COV_AKI_mod_122 53
DCT_COV_AKI_mod_82 25
DCT_COV_AKI_mod_20 200
DCT_COV_AKI_mod_198 48
DCT_COV_AKI_mod_173 79
DCT_COV_AKI_mod_248 22
Number of saved communities: 114
Analysing DCT_DKD
Identified communities
Number of communities: 222
Average community size 14.617117117117116
Median community size 10.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 2. 10. 18. 253.]
Significant communities
Number of communities: 6
Average community size 20.5
Median community size 16.5
Quantile (0,0.25,0.5,0.75,1) community size [13. 16. 16.5 20.75 39. ]
DCT_DKD_mod_91 13
DCT_DKD_mod_3 17
DCT_DKD_mod_99 16
DCT_DKD_mod_63 16
DCT_DKD_mod_21 22
DCT_DKD_mod_51 39
Number of saved communities: 0
Analysing DCT_H_CKD
Identified communities
Number of communities: 266
Average community size 9.721804511278195
Median community size 5.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 2. 5. 14. 95.]
Significant communities
Number of communities: 7
Average community size 25.142857142857142
Median community size 18.0
Quantile (0,0.25,0.5,0.75,1) community size [13. 14. 18. 29.5 58. ]
DCT_H_CKD_mod_8 14
DCT_H_CKD_mod_40 27
DCT_H_CKD_mod_19 18
DCT_H_CKD_mod_265 58
DCT_H_CKD_mod_188 14
DCT_H_CKD_mod_131 13
DCT_H_CKD_mod_117 32
Number of saved communities: 1
DTL
{'DTL_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'DTL_COV_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'DTL_DKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'DTL_H_CKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing DTL_AKI
Identified communities
Number of communities: 301
Average community size 34.4485049833887
Median community size 24.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 11. 24. 49. 295.]
Significant communities
Number of communities: 64
Average community size 42.40625
Median community size 35.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 21.25 35. 59. 125. ]
DTL_AKI_mod_251 18
DTL_AKI_mod_203 49
DTL_AKI_mod_14 26
DTL_AKI_mod_95 47
DTL_AKI_mod_198 15
DTL_AKI_mod_119 41
DTL_AKI_mod_78 33
DTL_AKI_mod_185 25
DTL_AKI_mod_120 45
DTL_AKI_mod_48 51
DTL_AKI_mod_71 24
DTL_AKI_mod_77 72
DTL_AKI_mod_92 10
DTL_AKI_mod_3 50
DTL_AKI_mod_188 11
DTL_AKI_mod_23 72
DTL_AKI_mod_131 55
DTL_AKI_mod_240 18
DTL_AKI_mod_167 45
DTL_AKI_mod_89 68
DTL_AKI_mod_239 11
DTL_AKI_mod_121 70
DTL_AKI_mod_106 29
DTL_AKI_mod_237 27
DTL_AKI_mod_147 64
DTL_AKI_mod_160 23
DTL_AKI_mod_19 17
DTL_AKI_mod_256 27
DTL_AKI_mod_36 85
DTL_AKI_mod_10 83
DTL_AKI_mod_127 59
DTL_AKI_mod_64 27
DTL_AKI_mod_222 23
DTL_AKI_mod_5 125
DTL_AKI_mod_91 105
DTL_AKI_mod_168 38
DTL_AKI_mod_27 55
DTL_AKI_mod_67 66
DTL_AKI_mod_207 18
DTL_AKI_mod_128 51
DTL_AKI_mod_145 46
DTL_AKI_mod_166 19
DTL_AKI_mod_136 22
DTL_AKI_mod_7 83
DTL_AKI_mod_122 36
DTL_AKI_mod_103 78
DTL_AKI_mod_28 59
DTL_AKI_mod_29 50
DTL_AKI_mod_51 73
DTL_AKI_mod_99 18
DTL_AKI_mod_193 12
DTL_AKI_mod_6 74
DTL_AKI_mod_162 16
DTL_AKI_mod_59 30
DTL_AKI_mod_258 14
DTL_AKI_mod_41 38
DTL_AKI_mod_109 33
DTL_AKI_mod_125 11
DTL_AKI_mod_169 32
DTL_AKI_mod_66 34
DTL_AKI_mod_69 103
DTL_AKI_mod_181 14
DTL_AKI_mod_245 24
DTL_AKI_mod_104 17
Number of saved communities: 39
Analysing DTL_COV_AKI
Identified communities
Number of communities: 315
Average community size 38.31111111111111
Median community size 27.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 11.5 27. 50.5 325. ]
Significant communities
Number of communities: 178
Average community size 48.764044943820224
Median community size 34.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 21. 34. 55.75 325. ]
DTL_COV_AKI_mod_196 21
DTL_COV_AKI_mod_113 14
DTL_COV_AKI_mod_0 211
DTL_COV_AKI_mod_147 94
DTL_COV_AKI_mod_203 18
DTL_COV_AKI_mod_138 12
DTL_COV_AKI_mod_22 32
DTL_COV_AKI_mod_71 300
DTL_COV_AKI_mod_139 29
DTL_COV_AKI_mod_76 22
DTL_COV_AKI_mod_67 11
DTL_COV_AKI_mod_222 13
DTL_COV_AKI_mod_246 31
DTL_COV_AKI_mod_140 48
DTL_COV_AKI_mod_153 52
DTL_COV_AKI_mod_254 11
DTL_COV_AKI_mod_199 16
DTL_COV_AKI_mod_2 13
DTL_COV_AKI_mod_5 44
DTL_COV_AKI_mod_163 33
DTL_COV_AKI_mod_168 16
DTL_COV_AKI_mod_63 58
DTL_COV_AKI_mod_184 13
DTL_COV_AKI_mod_240 19
DTL_COV_AKI_mod_29 34
DTL_COV_AKI_mod_205 23
DTL_COV_AKI_mod_249 25
DTL_COV_AKI_mod_16 220
DTL_COV_AKI_mod_36 72
DTL_COV_AKI_mod_87 103
DTL_COV_AKI_mod_43 71
DTL_COV_AKI_mod_54 56
DTL_COV_AKI_mod_197 10
DTL_COV_AKI_mod_155 34
DTL_COV_AKI_mod_179 38
DTL_COV_AKI_mod_40 54
DTL_COV_AKI_mod_24 83
DTL_COV_AKI_mod_1 66
DTL_COV_AKI_mod_212 27
DTL_COV_AKI_mod_117 27
DTL_COV_AKI_mod_261 28
DTL_COV_AKI_mod_106 20
DTL_COV_AKI_mod_6 63
DTL_COV_AKI_mod_186 30
DTL_COV_AKI_mod_3 112
DTL_COV_AKI_mod_72 35
DTL_COV_AKI_mod_85 22
DTL_COV_AKI_mod_21 21
DTL_COV_AKI_mod_247 17
DTL_COV_AKI_mod_269 40
DTL_COV_AKI_mod_128 24
DTL_COV_AKI_mod_65 40
DTL_COV_AKI_mod_135 32
DTL_COV_AKI_mod_93 43
DTL_COV_AKI_mod_127 18
DTL_COV_AKI_mod_122 23
DTL_COV_AKI_mod_237 13
DTL_COV_AKI_mod_47 94
DTL_COV_AKI_mod_157 46
DTL_COV_AKI_mod_83 70
DTL_COV_AKI_mod_130 29
DTL_COV_AKI_mod_30 59
DTL_COV_AKI_mod_248 17
DTL_COV_AKI_mod_84 22
DTL_COV_AKI_mod_206 21
DTL_COV_AKI_mod_32 19
DTL_COV_AKI_mod_57 52
DTL_COV_AKI_mod_132 21
DTL_COV_AKI_mod_98 45
DTL_COV_AKI_mod_258 15
DTL_COV_AKI_mod_187 38
DTL_COV_AKI_mod_180 20
DTL_COV_AKI_mod_149 31
DTL_COV_AKI_mod_49 35
DTL_COV_AKI_mod_115 31
DTL_COV_AKI_mod_8 109
DTL_COV_AKI_mod_211 17
DTL_COV_AKI_mod_4 55
DTL_COV_AKI_mod_109 48
DTL_COV_AKI_mod_284 19
DTL_COV_AKI_mod_152 32
DTL_COV_AKI_mod_74 46
DTL_COV_AKI_mod_154 49
DTL_COV_AKI_mod_156 31
DTL_COV_AKI_mod_228 36
DTL_COV_AKI_mod_50 40
DTL_COV_AKI_mod_52 20
DTL_COV_AKI_mod_214 45
DTL_COV_AKI_mod_10 32
DTL_COV_AKI_mod_256 15
DTL_COV_AKI_mod_59 94
DTL_COV_AKI_mod_105 16
DTL_COV_AKI_mod_17 59
DTL_COV_AKI_mod_193 60
DTL_COV_AKI_mod_92 30
DTL_COV_AKI_mod_123 139
DTL_COV_AKI_mod_270 17
DTL_COV_AKI_mod_78 136
DTL_COV_AKI_mod_11 87
DTL_COV_AKI_mod_209 34
DTL_COV_AKI_mod_25 18
DTL_COV_AKI_mod_234 26
DTL_COV_AKI_mod_182 18
DTL_COV_AKI_mod_202 19
DTL_COV_AKI_mod_66 31
DTL_COV_AKI_mod_145 183
DTL_COV_AKI_mod_142 82
DTL_COV_AKI_mod_104 59
DTL_COV_AKI_mod_112 19
DTL_COV_AKI_mod_51 148
DTL_COV_AKI_mod_143 13
DTL_COV_AKI_mod_111 70
DTL_COV_AKI_mod_97 93
DTL_COV_AKI_mod_285 17
DTL_COV_AKI_mod_219 21
DTL_COV_AKI_mod_116 27
DTL_COV_AKI_mod_255 16
DTL_COV_AKI_mod_151 58
DTL_COV_AKI_mod_146 65
DTL_COV_AKI_mod_73 216
DTL_COV_AKI_mod_243 12
DTL_COV_AKI_mod_75 33
DTL_COV_AKI_mod_264 21
DTL_COV_AKI_mod_262 17
DTL_COV_AKI_mod_124 75
DTL_COV_AKI_mod_165 73
DTL_COV_AKI_mod_286 28
DTL_COV_AKI_mod_88 39
DTL_COV_AKI_mod_90 51
DTL_COV_AKI_mod_61 21
DTL_COV_AKI_mod_14 94
DTL_COV_AKI_mod_221 48
DTL_COV_AKI_mod_60 38
DTL_COV_AKI_mod_174 22
DTL_COV_AKI_mod_89 36
DTL_COV_AKI_mod_271 22
DTL_COV_AKI_mod_226 21
DTL_COV_AKI_mod_45 325
DTL_COV_AKI_mod_31 72
DTL_COV_AKI_mod_173 33
DTL_COV_AKI_mod_164 50
DTL_COV_AKI_mod_64 19
DTL_COV_AKI_mod_58 51
DTL_COV_AKI_mod_190 45
DTL_COV_AKI_mod_27 54
DTL_COV_AKI_mod_15 121
DTL_COV_AKI_mod_82 41
DTL_COV_AKI_mod_177 30
DTL_COV_AKI_mod_34 126
DTL_COV_AKI_mod_7 119
DTL_COV_AKI_mod_235 11
DTL_COV_AKI_mod_80 59
DTL_COV_AKI_mod_191 13
DTL_COV_AKI_mod_263 13
DTL_COV_AKI_mod_53 40
DTL_COV_AKI_mod_35 54
DTL_COV_AKI_mod_48 109
DTL_COV_AKI_mod_33 11
DTL_COV_AKI_mod_141 48
DTL_COV_AKI_mod_55 34
DTL_COV_AKI_mod_176 15
DTL_COV_AKI_mod_131 52
DTL_COV_AKI_mod_129 22
DTL_COV_AKI_mod_19 138
DTL_COV_AKI_mod_99 34
DTL_COV_AKI_mod_126 22
DTL_COV_AKI_mod_175 25
DTL_COV_AKI_mod_69 39
DTL_COV_AKI_mod_101 72
DTL_COV_AKI_mod_56 42
DTL_COV_AKI_mod_68 49
DTL_COV_AKI_mod_158 64
DTL_COV_AKI_mod_245 26
DTL_COV_AKI_mod_161 19
DTL_COV_AKI_mod_162 39
DTL_COV_AKI_mod_169 31
DTL_COV_AKI_mod_215 37
DTL_COV_AKI_mod_120 40
Number of saved communities: 104
Analysing DTL_DKD
Identified communities
Number of communities: 291
Average community size 21.20274914089347
Median community size 14.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 2. 14. 25. 809.]
Significant communities
Number of communities: 35
Average community size 27.914285714285715
Median community size 24.0
Quantile (0,0.25,0.5,0.75,1) community size [10. 17. 24. 33. 84.]
DTL_DKD_mod_134 28
DTL_DKD_mod_103 55
DTL_DKD_mod_76 52
DTL_DKD_mod_47 32
DTL_DKD_mod_64 13
DTL_DKD_mod_61 25
DTL_DKD_mod_91 29
DTL_DKD_mod_13 16
DTL_DKD_mod_87 23
DTL_DKD_mod_148 21
DTL_DKD_mod_132 10
DTL_DKD_mod_59 27
DTL_DKD_mod_72 38
DTL_DKD_mod_27 33
DTL_DKD_mod_21 17
DTL_DKD_mod_58 22
DTL_DKD_mod_211 17
DTL_DKD_mod_185 18
DTL_DKD_mod_215 23
DTL_DKD_mod_81 26
DTL_DKD_mod_3 35
DTL_DKD_mod_24 16
DTL_DKD_mod_111 16
DTL_DKD_mod_147 45
DTL_DKD_mod_203 40
DTL_DKD_mod_174 12
DTL_DKD_mod_35 21
DTL_DKD_mod_85 33
DTL_DKD_mod_153 17
DTL_DKD_mod_38 84
DTL_DKD_mod_30 29
DTL_DKD_mod_162 17
DTL_DKD_mod_170 18
DTL_DKD_mod_19 24
DTL_DKD_mod_151 45
Number of saved communities: 4
Analysing DTL_H_CKD
Identified communities
Number of communities: 255
Average community size 10.466666666666667
Median community size 4.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 2. 4. 13. 291.]
Significant communities
Number of communities: 5
Average community size 23.6
Median community size 21.0
Quantile (0,0.25,0.5,0.75,1) community size [10. 21. 21. 26. 40.]
DTL_H_CKD_mod_62 21
DTL_H_CKD_mod_120 10
DTL_H_CKD_mod_52 26
DTL_H_CKD_mod_51 40
DTL_H_CKD_mod_41 21
Number of saved communities: 1
EC
{'EC_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'EC_COV_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'EC_DKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'EC_H_CKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing EC_AKI
Identified communities
Number of communities: 209
Average community size 11.688995215311005
Median community size 8.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 2. 8. 16. 131.]
Significant communities
Number of communities: 11
Average community size 15.090909090909092
Median community size 14.0
Quantile (0,0.25,0.5,0.75,1) community size [10. 13. 14. 17. 24.]
EC_AKI_mod_80 14
EC_AKI_mod_56 24
EC_AKI_mod_130 10
EC_AKI_mod_145 14
EC_AKI_mod_128 10
EC_AKI_mod_24 19
EC_AKI_mod_59 16
EC_AKI_mod_13 18
EC_AKI_mod_73 14
EC_AKI_mod_71 15
EC_AKI_mod_46 12
Number of saved communities: 0
Analysing EC_COV_AKI
Identified communities
Number of communities: 213
Average community size 23.652582159624412
Median community size 16.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 9. 16. 27. 261.]
Significant communities
Number of communities: 100
Average community size 32.49
Median community size 20.5
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 15. 20.5 29. 261. ]
EC_COV_AKI_mod_9 14
EC_COV_AKI_mod_49 50
EC_COV_AKI_mod_147 16
EC_COV_AKI_mod_100 46
EC_COV_AKI_mod_165 16
EC_COV_AKI_mod_126 15
EC_COV_AKI_mod_39 28
EC_COV_AKI_mod_128 18
EC_COV_AKI_mod_34 24
EC_COV_AKI_mod_109 15
EC_COV_AKI_mod_50 197
EC_COV_AKI_mod_82 16
EC_COV_AKI_mod_73 81
EC_COV_AKI_mod_32 95
EC_COV_AKI_mod_30 250
EC_COV_AKI_mod_104 17
EC_COV_AKI_mod_153 11
EC_COV_AKI_mod_89 29
EC_COV_AKI_mod_0 261
EC_COV_AKI_mod_95 23
EC_COV_AKI_mod_188 12
EC_COV_AKI_mod_1 75
EC_COV_AKI_mod_183 21
EC_COV_AKI_mod_173 11
EC_COV_AKI_mod_53 22
EC_COV_AKI_mod_119 14
EC_COV_AKI_mod_184 11
EC_COV_AKI_mod_71 54
EC_COV_AKI_mod_123 43
EC_COV_AKI_mod_177 23
EC_COV_AKI_mod_186 17
EC_COV_AKI_mod_4 188
EC_COV_AKI_mod_112 23
EC_COV_AKI_mod_90 13
EC_COV_AKI_mod_98 36
EC_COV_AKI_mod_101 27
EC_COV_AKI_mod_46 17
EC_COV_AKI_mod_178 18
EC_COV_AKI_mod_67 37
EC_COV_AKI_mod_60 14
EC_COV_AKI_mod_65 10
EC_COV_AKI_mod_194 17
EC_COV_AKI_mod_93 28
EC_COV_AKI_mod_74 48
EC_COV_AKI_mod_111 22
EC_COV_AKI_mod_47 15
EC_COV_AKI_mod_127 22
EC_COV_AKI_mod_148 10
EC_COV_AKI_mod_62 22
EC_COV_AKI_mod_146 12
EC_COV_AKI_mod_156 29
EC_COV_AKI_mod_75 12
EC_COV_AKI_mod_63 43
EC_COV_AKI_mod_145 15
EC_COV_AKI_mod_107 17
EC_COV_AKI_mod_14 11
EC_COV_AKI_mod_157 15
EC_COV_AKI_mod_172 18
EC_COV_AKI_mod_135 13
EC_COV_AKI_mod_84 23
EC_COV_AKI_mod_8 29
EC_COV_AKI_mod_174 25
EC_COV_AKI_mod_140 16
EC_COV_AKI_mod_142 20
EC_COV_AKI_mod_3 16
EC_COV_AKI_mod_72 24
EC_COV_AKI_mod_78 13
EC_COV_AKI_mod_21 26
EC_COV_AKI_mod_6 16
EC_COV_AKI_mod_110 17
EC_COV_AKI_mod_12 13
EC_COV_AKI_mod_108 26
EC_COV_AKI_mod_28 80
EC_COV_AKI_mod_54 25
EC_COV_AKI_mod_91 33
EC_COV_AKI_mod_55 15
EC_COV_AKI_mod_114 30
EC_COV_AKI_mod_99 27
EC_COV_AKI_mod_5 32
EC_COV_AKI_mod_57 33
EC_COV_AKI_mod_88 18
EC_COV_AKI_mod_185 12
EC_COV_AKI_mod_161 13
EC_COV_AKI_mod_33 26
EC_COV_AKI_mod_17 39
EC_COV_AKI_mod_141 33
EC_COV_AKI_mod_83 16
EC_COV_AKI_mod_116 37
EC_COV_AKI_mod_94 20
EC_COV_AKI_mod_102 22
EC_COV_AKI_mod_154 12
EC_COV_AKI_mod_120 11
EC_COV_AKI_mod_121 19
EC_COV_AKI_mod_56 19
EC_COV_AKI_mod_130 48
EC_COV_AKI_mod_158 24
EC_COV_AKI_mod_139 16
EC_COV_AKI_mod_29 23
EC_COV_AKI_mod_77 15
EC_COV_AKI_mod_134 10
Number of saved communities: 14
Analysing EC_DKD
Identified communities
Number of communities: 208
Average community size 8.95673076923077
Median community size 3.5
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 2. 3.5 11. 216. ]
Significant communities
Number of communities: 0
Average community size -
Median community size -
Quantile (0,0.25,0.5,0.75,1) community size -
Number of saved communities: 0
Analysing EC_H_CKD
Identified communities
Number of communities: 199
Average community size 8.919597989949748
Median community size 5.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 2. 5. 12. 100.]
Significant communities
Number of communities: 9
Average community size 18.88888888888889
Median community size 16.0
Quantile (0,0.25,0.5,0.75,1) community size [11. 13. 16. 24. 35.]
EC_H_CKD_mod_5 12
EC_H_CKD_mod_21 35
EC_H_CKD_mod_40 16
EC_H_CKD_mod_124 13
EC_H_CKD_mod_72 14
EC_H_CKD_mod_84 11
EC_H_CKD_mod_14 24
EC_H_CKD_mod_28 28
EC_H_CKD_mod_13 17
Number of saved communities: 1
FIB
{'FIB_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'FIB_COV_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'FIB_DKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'FIB_H_CKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing FIB_AKI
Identified communities
Number of communities: 445
Average community size 12.667415730337078
Median community size 2.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 1. 2. 17. 673.]
Significant communities
Number of communities: 21
Average community size 30.952380952380953
Median community size 18.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 11. 18. 33. 222.]
FIB_AKI_mod_48 10
FIB_AKI_mod_13 11
FIB_AKI_mod_200 11
FIB_AKI_mod_174 34
FIB_AKI_mod_78 57
FIB_AKI_mod_16 222
FIB_AKI_mod_151 10
FIB_AKI_mod_201 13
FIB_AKI_mod_296 37
FIB_AKI_mod_30 25
FIB_AKI_mod_212 15
FIB_AKI_mod_138 10
FIB_AKI_mod_192 21
FIB_AKI_mod_88 21
FIB_AKI_mod_116 11
FIB_AKI_mod_133 33
FIB_AKI_mod_146 36
FIB_AKI_mod_70 16
FIB_AKI_mod_68 13
FIB_AKI_mod_148 18
FIB_AKI_mod_89 26
Number of saved communities: 4
Analysing FIB_COV_AKI
Identified communities
Number of communities: 304
Average community size 33.26315789473684
Median community size 24.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 12. 24. 44. 259.]
Significant communities
Number of communities: 209
Average community size 41.92344497607655
Median community size 31.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 19. 31. 53. 259.]
FIB_COV_AKI_mod_123 43
FIB_COV_AKI_mod_142 27
FIB_COV_AKI_mod_206 13
FIB_COV_AKI_mod_80 68
FIB_COV_AKI_mod_213 11
FIB_COV_AKI_mod_207 10
FIB_COV_AKI_mod_193 23
FIB_COV_AKI_mod_282 15
FIB_COV_AKI_mod_208 24
FIB_COV_AKI_mod_21 22
FIB_COV_AKI_mod_112 15
FIB_COV_AKI_mod_120 44
FIB_COV_AKI_mod_49 58
FIB_COV_AKI_mod_239 21
FIB_COV_AKI_mod_57 97
FIB_COV_AKI_mod_15 36
FIB_COV_AKI_mod_26 46
FIB_COV_AKI_mod_85 35
FIB_COV_AKI_mod_83 259
FIB_COV_AKI_mod_103 66
FIB_COV_AKI_mod_19 225
FIB_COV_AKI_mod_125 21
FIB_COV_AKI_mod_165 18
FIB_COV_AKI_mod_10 127
FIB_COV_AKI_mod_13 63
FIB_COV_AKI_mod_199 18
FIB_COV_AKI_mod_212 25
FIB_COV_AKI_mod_250 19
FIB_COV_AKI_mod_175 11
FIB_COV_AKI_mod_196 43
FIB_COV_AKI_mod_62 10
FIB_COV_AKI_mod_50 80
FIB_COV_AKI_mod_58 48
FIB_COV_AKI_mod_203 17
FIB_COV_AKI_mod_68 19
FIB_COV_AKI_mod_1 39
FIB_COV_AKI_mod_17 14
FIB_COV_AKI_mod_210 27
FIB_COV_AKI_mod_228 21
FIB_COV_AKI_mod_229 30
FIB_COV_AKI_mod_247 12
FIB_COV_AKI_mod_230 13
FIB_COV_AKI_mod_134 31
FIB_COV_AKI_mod_151 54
FIB_COV_AKI_mod_221 74
FIB_COV_AKI_mod_138 49
FIB_COV_AKI_mod_7 91
FIB_COV_AKI_mod_113 34
FIB_COV_AKI_mod_242 23
FIB_COV_AKI_mod_59 146
FIB_COV_AKI_mod_67 19
FIB_COV_AKI_mod_95 34
FIB_COV_AKI_mod_106 56
FIB_COV_AKI_mod_105 67
FIB_COV_AKI_mod_55 55
FIB_COV_AKI_mod_36 19
FIB_COV_AKI_mod_156 14
FIB_COV_AKI_mod_184 55
FIB_COV_AKI_mod_88 50
FIB_COV_AKI_mod_137 12
FIB_COV_AKI_mod_241 20
FIB_COV_AKI_mod_30 172
FIB_COV_AKI_mod_111 40
FIB_COV_AKI_mod_135 14
FIB_COV_AKI_mod_0 59
FIB_COV_AKI_mod_136 18
FIB_COV_AKI_mod_231 11
FIB_COV_AKI_mod_12 160
FIB_COV_AKI_mod_53 22
FIB_COV_AKI_mod_3 52
FIB_COV_AKI_mod_52 21
FIB_COV_AKI_mod_182 37
FIB_COV_AKI_mod_148 37
FIB_COV_AKI_mod_61 15
FIB_COV_AKI_mod_237 25
FIB_COV_AKI_mod_169 14
FIB_COV_AKI_mod_64 15
FIB_COV_AKI_mod_194 88
FIB_COV_AKI_mod_5 54
FIB_COV_AKI_mod_119 136
FIB_COV_AKI_mod_205 29
FIB_COV_AKI_mod_107 34
FIB_COV_AKI_mod_173 20
FIB_COV_AKI_mod_9 11
FIB_COV_AKI_mod_99 36
FIB_COV_AKI_mod_82 57
FIB_COV_AKI_mod_161 25
FIB_COV_AKI_mod_115 19
FIB_COV_AKI_mod_39 36
FIB_COV_AKI_mod_96 32
FIB_COV_AKI_mod_222 11
FIB_COV_AKI_mod_139 67
FIB_COV_AKI_mod_177 31
FIB_COV_AKI_mod_198 21
FIB_COV_AKI_mod_232 26
FIB_COV_AKI_mod_146 75
FIB_COV_AKI_mod_34 152
FIB_COV_AKI_mod_4 18
FIB_COV_AKI_mod_126 74
FIB_COV_AKI_mod_191 46
FIB_COV_AKI_mod_56 87
FIB_COV_AKI_mod_149 12
FIB_COV_AKI_mod_101 45
FIB_COV_AKI_mod_28 64
FIB_COV_AKI_mod_174 22
FIB_COV_AKI_mod_43 88
FIB_COV_AKI_mod_48 27
FIB_COV_AKI_mod_202 26
FIB_COV_AKI_mod_252 13
FIB_COV_AKI_mod_45 10
FIB_COV_AKI_mod_129 28
FIB_COV_AKI_mod_29 61
FIB_COV_AKI_mod_224 25
FIB_COV_AKI_mod_215 19
FIB_COV_AKI_mod_71 141
FIB_COV_AKI_mod_92 23
FIB_COV_AKI_mod_200 25
FIB_COV_AKI_mod_72 55
FIB_COV_AKI_mod_223 40
FIB_COV_AKI_mod_31 74
FIB_COV_AKI_mod_121 47
FIB_COV_AKI_mod_74 67
FIB_COV_AKI_mod_249 18
FIB_COV_AKI_mod_218 23
FIB_COV_AKI_mod_20 21
FIB_COV_AKI_mod_131 36
FIB_COV_AKI_mod_160 10
FIB_COV_AKI_mod_166 35
FIB_COV_AKI_mod_274 13
FIB_COV_AKI_mod_176 31
FIB_COV_AKI_mod_152 35
FIB_COV_AKI_mod_133 13
FIB_COV_AKI_mod_217 27
FIB_COV_AKI_mod_214 35
FIB_COV_AKI_mod_86 24
FIB_COV_AKI_mod_159 54
FIB_COV_AKI_mod_187 16
FIB_COV_AKI_mod_75 67
FIB_COV_AKI_mod_22 66
FIB_COV_AKI_mod_158 61
FIB_COV_AKI_mod_256 11
FIB_COV_AKI_mod_98 21
FIB_COV_AKI_mod_27 64
FIB_COV_AKI_mod_140 28
FIB_COV_AKI_mod_118 19
FIB_COV_AKI_mod_54 28
FIB_COV_AKI_mod_209 53
FIB_COV_AKI_mod_66 34
FIB_COV_AKI_mod_244 19
FIB_COV_AKI_mod_141 45
FIB_COV_AKI_mod_181 77
FIB_COV_AKI_mod_33 44
FIB_COV_AKI_mod_44 35
FIB_COV_AKI_mod_197 17
FIB_COV_AKI_mod_25 17
FIB_COV_AKI_mod_178 23
FIB_COV_AKI_mod_87 26
FIB_COV_AKI_mod_110 77
FIB_COV_AKI_mod_147 52
FIB_COV_AKI_mod_97 26
FIB_COV_AKI_mod_6 86
FIB_COV_AKI_mod_188 47
FIB_COV_AKI_mod_144 76
FIB_COV_AKI_mod_2 42
FIB_COV_AKI_mod_245 18
FIB_COV_AKI_mod_38 34
FIB_COV_AKI_mod_124 27
FIB_COV_AKI_mod_154 21
FIB_COV_AKI_mod_116 45
FIB_COV_AKI_mod_24 31
FIB_COV_AKI_mod_128 18
FIB_COV_AKI_mod_179 31
FIB_COV_AKI_mod_145 55
FIB_COV_AKI_mod_78 88
FIB_COV_AKI_mod_79 61
FIB_COV_AKI_mod_170 33
FIB_COV_AKI_mod_89 163
FIB_COV_AKI_mod_117 12
FIB_COV_AKI_mod_46 24
FIB_COV_AKI_mod_8 38
FIB_COV_AKI_mod_236 39
FIB_COV_AKI_mod_23 33
FIB_COV_AKI_mod_42 28
FIB_COV_AKI_mod_91 23
FIB_COV_AKI_mod_108 76
FIB_COV_AKI_mod_248 12
FIB_COV_AKI_mod_204 27
FIB_COV_AKI_mod_60 53
FIB_COV_AKI_mod_76 18
FIB_COV_AKI_mod_63 23
FIB_COV_AKI_mod_168 18
FIB_COV_AKI_mod_226 31
FIB_COV_AKI_mod_157 28
FIB_COV_AKI_mod_150 29
FIB_COV_AKI_mod_122 28
FIB_COV_AKI_mod_171 18
FIB_COV_AKI_mod_90 49
FIB_COV_AKI_mod_216 29
FIB_COV_AKI_mod_238 27
FIB_COV_AKI_mod_130 28
FIB_COV_AKI_mod_186 28
FIB_COV_AKI_mod_233 36
FIB_COV_AKI_mod_94 80
FIB_COV_AKI_mod_162 18
FIB_COV_AKI_mod_35 39
FIB_COV_AKI_mod_51 52
FIB_COV_AKI_mod_189 16
FIB_COV_AKI_mod_163 17
FIB_COV_AKI_mod_32 63
Number of saved communities: 91
Analysing FIB_DKD
Identified communities
Number of communities: 246
Average community size 11.66260162601626
Median community size 6.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 2. 6. 15. 265.]
Significant communities
Number of communities: 2
Average community size 31.5
Median community size 31.5
Quantile (0,0.25,0.5,0.75,1) community size [11. 21.25 31.5 41.75 52. ]
FIB_DKD_mod_19 11
FIB_DKD_mod_11 52
Number of saved communities: 0
Analysing FIB_H_CKD
Identified communities
Number of communities: 585
Average community size 5.779487179487179
Median community size 1.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 1. 1. 3. 187.]
Significant communities
Number of communities: 4
Average community size 24.0
Median community size 24.0
Quantile (0,0.25,0.5,0.75,1) community size [13. 16.75 24. 31.25 35. ]
FIB_H_CKD_mod_54 18
FIB_H_CKD_mod_15 35
FIB_H_CKD_mod_167 13
FIB_H_CKD_mod_132 30
Number of saved communities: 1
IC
{'IC_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'IC_COV_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'IC_DKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'IC_H_CKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing IC_AKI
Identified communities
Number of communities: 273
Average community size 30.615384615384617
Median community size 23.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 14. 23. 40. 196.]
Significant communities
Number of communities: 15
Average community size 70.0
Median community size 67.0
Quantile (0,0.25,0.5,0.75,1) community size [ 12. 24. 67. 81.5 196. ]
IC_AKI_mod_117 13
IC_AKI_mod_219 29
IC_AKI_mod_53 74
IC_AKI_mod_87 191
IC_AKI_mod_75 134
IC_AKI_mod_222 17
IC_AKI_mod_47 196
IC_AKI_mod_78 38
IC_AKI_mod_203 12
IC_AKI_mod_105 19
IC_AKI_mod_196 32
IC_AKI_mod_107 67
IC_AKI_mod_22 71
IC_AKI_mod_40 89
IC_AKI_mod_9 68
Number of saved communities: 1
Analysing IC_COV_AKI
Identified communities
Number of communities: 320
Average community size 33.79375
Median community size 26.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 8.75 26. 44. 275. ]
Significant communities
Number of communities: 138
Average community size 46.19565217391305
Median community size 34.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 22. 34. 54.75 275. ]
IC_COV_AKI_mod_200 51
IC_COV_AKI_mod_231 36
IC_COV_AKI_mod_156 34
IC_COV_AKI_mod_242 23
IC_COV_AKI_mod_193 25
IC_COV_AKI_mod_96 275
IC_COV_AKI_mod_105 12
IC_COV_AKI_mod_23 224
IC_COV_AKI_mod_109 36
IC_COV_AKI_mod_114 11
IC_COV_AKI_mod_205 37
IC_COV_AKI_mod_169 19
IC_COV_AKI_mod_82 13
IC_COV_AKI_mod_228 13
IC_COV_AKI_mod_171 26
IC_COV_AKI_mod_43 117
IC_COV_AKI_mod_58 49
IC_COV_AKI_mod_116 56
IC_COV_AKI_mod_227 15
IC_COV_AKI_mod_120 24
IC_COV_AKI_mod_44 43
IC_COV_AKI_mod_141 38
IC_COV_AKI_mod_17 59
IC_COV_AKI_mod_196 59
IC_COV_AKI_mod_122 60
IC_COV_AKI_mod_152 68
IC_COV_AKI_mod_4 59
IC_COV_AKI_mod_107 17
IC_COV_AKI_mod_139 34
IC_COV_AKI_mod_115 15
IC_COV_AKI_mod_118 16
IC_COV_AKI_mod_232 18
IC_COV_AKI_mod_21 31
IC_COV_AKI_mod_128 51
IC_COV_AKI_mod_132 81
IC_COV_AKI_mod_81 29
IC_COV_AKI_mod_45 29
IC_COV_AKI_mod_262 22
IC_COV_AKI_mod_60 35
IC_COV_AKI_mod_195 26
IC_COV_AKI_mod_150 80
IC_COV_AKI_mod_15 228
IC_COV_AKI_mod_142 25
IC_COV_AKI_mod_143 49
IC_COV_AKI_mod_67 16
IC_COV_AKI_mod_49 27
IC_COV_AKI_mod_7 63
IC_COV_AKI_mod_217 14
IC_COV_AKI_mod_34 55
IC_COV_AKI_mod_38 13
IC_COV_AKI_mod_57 71
IC_COV_AKI_mod_165 29
IC_COV_AKI_mod_131 30
IC_COV_AKI_mod_133 21
IC_COV_AKI_mod_8 65
IC_COV_AKI_mod_32 31
IC_COV_AKI_mod_104 143
IC_COV_AKI_mod_186 19
IC_COV_AKI_mod_121 12
IC_COV_AKI_mod_75 58
IC_COV_AKI_mod_236 39
IC_COV_AKI_mod_112 81
IC_COV_AKI_mod_157 53
IC_COV_AKI_mod_245 21
IC_COV_AKI_mod_154 36
IC_COV_AKI_mod_278 10
IC_COV_AKI_mod_229 41
IC_COV_AKI_mod_146 78
IC_COV_AKI_mod_90 133
IC_COV_AKI_mod_201 34
IC_COV_AKI_mod_94 87
IC_COV_AKI_mod_20 124
IC_COV_AKI_mod_53 25
IC_COV_AKI_mod_286 10
IC_COV_AKI_mod_167 27
IC_COV_AKI_mod_235 26
IC_COV_AKI_mod_0 154
IC_COV_AKI_mod_279 21
IC_COV_AKI_mod_40 32
IC_COV_AKI_mod_265 15
IC_COV_AKI_mod_256 18
IC_COV_AKI_mod_247 20
IC_COV_AKI_mod_198 35
IC_COV_AKI_mod_153 45
IC_COV_AKI_mod_148 45
IC_COV_AKI_mod_149 46
IC_COV_AKI_mod_1 28
IC_COV_AKI_mod_93 37
IC_COV_AKI_mod_202 10
IC_COV_AKI_mod_72 33
IC_COV_AKI_mod_151 38
IC_COV_AKI_mod_77 20
IC_COV_AKI_mod_26 97
IC_COV_AKI_mod_213 25
IC_COV_AKI_mod_161 24
IC_COV_AKI_mod_39 80
IC_COV_AKI_mod_145 44
IC_COV_AKI_mod_99 32
IC_COV_AKI_mod_134 20
IC_COV_AKI_mod_189 39
IC_COV_AKI_mod_31 73
IC_COV_AKI_mod_18 107
IC_COV_AKI_mod_136 21
IC_COV_AKI_mod_25 42
IC_COV_AKI_mod_27 115
IC_COV_AKI_mod_125 86
IC_COV_AKI_mod_211 39
IC_COV_AKI_mod_249 13
IC_COV_AKI_mod_129 49
IC_COV_AKI_mod_214 30
IC_COV_AKI_mod_100 30
IC_COV_AKI_mod_137 26
IC_COV_AKI_mod_14 38
IC_COV_AKI_mod_28 15
IC_COV_AKI_mod_212 29
IC_COV_AKI_mod_164 32
IC_COV_AKI_mod_88 83
IC_COV_AKI_mod_85 42
IC_COV_AKI_mod_111 54
IC_COV_AKI_mod_84 41
IC_COV_AKI_mod_9 85
IC_COV_AKI_mod_51 115
IC_COV_AKI_mod_221 35
IC_COV_AKI_mod_158 30
IC_COV_AKI_mod_174 11
IC_COV_AKI_mod_178 21
IC_COV_AKI_mod_225 42
IC_COV_AKI_mod_47 22
IC_COV_AKI_mod_163 33
IC_COV_AKI_mod_206 19
IC_COV_AKI_mod_224 42
IC_COV_AKI_mod_192 15
IC_COV_AKI_mod_41 58
IC_COV_AKI_mod_199 28
IC_COV_AKI_mod_203 30
IC_COV_AKI_mod_10 56
IC_COV_AKI_mod_241 21
IC_COV_AKI_mod_144 29
Number of saved communities: 71
Analysing IC_DKD
Identified communities
Number of communities: 216
Average community size 13.962962962962964
Median community size 11.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 3. 11. 17.25 123. ]
Significant communities
Number of communities: 8
Average community size 31.875
Median community size 23.5
Quantile (0,0.25,0.5,0.75,1) community size [12. 16. 23.5 35.5 90. ]
IC_DKD_mod_115 43
IC_DKD_mod_78 17
IC_DKD_mod_8 28
IC_DKD_mod_165 13
IC_DKD_mod_61 33
IC_DKD_mod_164 12
IC_DKD_mod_20 90
IC_DKD_mod_14 19
Number of saved communities: 0
Analysing IC_H_CKD
Identified communities
Number of communities: 302
Average community size 29.013245033112582
Median community size 23.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 5. 23. 38. 247.]
Significant communities
Number of communities: 21
Average community size 37.095238095238095
Median community size 18.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 13. 18. 25. 247.]
IC_H_CKD_mod_110 19
IC_H_CKD_mod_101 14
IC_H_CKD_mod_0 247
IC_H_CKD_mod_114 18
IC_H_CKD_mod_199 27
IC_H_CKD_mod_258 10
IC_H_CKD_mod_98 13
IC_H_CKD_mod_182 12
IC_H_CKD_mod_5 126
IC_H_CKD_mod_61 34
IC_H_CKD_mod_180 25
IC_H_CKD_mod_239 25
IC_H_CKD_mod_163 25
IC_H_CKD_mod_146 10
IC_H_CKD_mod_219 15
IC_H_CKD_mod_211 10
IC_H_CKD_mod_82 83
IC_H_CKD_mod_200 14
IC_H_CKD_mod_167 17
IC_H_CKD_mod_154 25
IC_H_CKD_mod_260 10
Number of saved communities: 2
IMM
{'IMM_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'IMM_COV_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'IMM_DKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'IMM_H_CKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing IMM_AKI
Identified communities
Number of communities: 272
Average community size 31.797794117647058
Median community size 26.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 15. 26. 40. 154.]
Significant communities
Number of communities: 52
Average community size 39.84615384615385
Median community size 27.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 17.75 27. 50.25 148. ]
IMM_AKI_mod_144 12
IMM_AKI_mod_73 112
IMM_AKI_mod_127 28
IMM_AKI_mod_241 11
IMM_AKI_mod_141 55
IMM_AKI_mod_224 34
IMM_AKI_mod_166 22
IMM_AKI_mod_156 16
IMM_AKI_mod_133 36
IMM_AKI_mod_46 54
IMM_AKI_mod_40 14
IMM_AKI_mod_230 10
IMM_AKI_mod_9 90
IMM_AKI_mod_27 35
IMM_AKI_mod_91 14
IMM_AKI_mod_202 30
IMM_AKI_mod_110 37
IMM_AKI_mod_81 17
IMM_AKI_mod_1 20
IMM_AKI_mod_109 10
IMM_AKI_mod_246 21
IMM_AKI_mod_15 26
IMM_AKI_mod_169 18
IMM_AKI_mod_244 20
IMM_AKI_mod_217 49
IMM_AKI_mod_78 146
IMM_AKI_mod_234 15
IMM_AKI_mod_99 13
IMM_AKI_mod_12 35
IMM_AKI_mod_111 87
IMM_AKI_mod_215 30
IMM_AKI_mod_134 148
IMM_AKI_mod_158 25
IMM_AKI_mod_118 93
IMM_AKI_mod_150 39
IMM_AKI_mod_152 13
IMM_AKI_mod_25 20
IMM_AKI_mod_145 66
IMM_AKI_mod_45 61
IMM_AKI_mod_66 47
IMM_AKI_mod_252 23
IMM_AKI_mod_195 17
IMM_AKI_mod_225 25
IMM_AKI_mod_159 19
IMM_AKI_mod_47 24
IMM_AKI_mod_93 42
IMM_AKI_mod_88 16
IMM_AKI_mod_194 31
IMM_AKI_mod_247 25
IMM_AKI_mod_112 63
IMM_AKI_mod_49 72
IMM_AKI_mod_102 86
Number of saved communities: 9
Analysing IMM_COV_AKI
Identified communities
Number of communities: 328
Average community size 27.3109756097561
Median community size 19.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 5. 19. 37. 306.]
Significant communities
Number of communities: 72
Average community size 39.56944444444444
Median community size 22.5
Quantile (0,0.25,0.5,0.75,1) community size [ 11. 15.75 22.5 38.25 306. ]
IMM_COV_AKI_mod_273 11
IMM_COV_AKI_mod_174 14
IMM_COV_AKI_mod_0 306
IMM_COV_AKI_mod_177 32
IMM_COV_AKI_mod_268 17
IMM_COV_AKI_mod_66 196
IMM_COV_AKI_mod_225 14
IMM_COV_AKI_mod_23 62
IMM_COV_AKI_mod_89 44
IMM_COV_AKI_mod_70 11
IMM_COV_AKI_mod_95 42
IMM_COV_AKI_mod_252 15
IMM_COV_AKI_mod_104 15
IMM_COV_AKI_mod_72 80
IMM_COV_AKI_mod_110 29
IMM_COV_AKI_mod_12 232
IMM_COV_AKI_mod_117 28
IMM_COV_AKI_mod_275 16
IMM_COV_AKI_mod_59 16
IMM_COV_AKI_mod_61 20
IMM_COV_AKI_mod_83 46
IMM_COV_AKI_mod_244 23
IMM_COV_AKI_mod_5 31
IMM_COV_AKI_mod_204 19
IMM_COV_AKI_mod_145 12
IMM_COV_AKI_mod_28 144
IMM_COV_AKI_mod_41 30
IMM_COV_AKI_mod_128 41
IMM_COV_AKI_mod_105 19
IMM_COV_AKI_mod_146 25
IMM_COV_AKI_mod_40 102
IMM_COV_AKI_mod_238 15
IMM_COV_AKI_mod_53 22
IMM_COV_AKI_mod_47 20
IMM_COV_AKI_mod_176 19
IMM_COV_AKI_mod_136 16
IMM_COV_AKI_mod_224 11
IMM_COV_AKI_mod_150 22
IMM_COV_AKI_mod_248 14
IMM_COV_AKI_mod_183 15
IMM_COV_AKI_mod_165 13
IMM_COV_AKI_mod_125 19
IMM_COV_AKI_mod_211 13
IMM_COV_AKI_mod_163 15
IMM_COV_AKI_mod_240 19
IMM_COV_AKI_mod_167 12
IMM_COV_AKI_mod_115 39
IMM_COV_AKI_mod_84 33
IMM_COV_AKI_mod_93 25
IMM_COV_AKI_mod_175 28
IMM_COV_AKI_mod_134 38
IMM_COV_AKI_mod_205 22
IMM_COV_AKI_mod_65 77
IMM_COV_AKI_mod_7 98
IMM_COV_AKI_mod_196 12
IMM_COV_AKI_mod_56 62
IMM_COV_AKI_mod_223 28
IMM_COV_AKI_mod_31 17
IMM_COV_AKI_mod_265 15
IMM_COV_AKI_mod_8 79
IMM_COV_AKI_mod_234 21
IMM_COV_AKI_mod_249 31
IMM_COV_AKI_mod_179 23
IMM_COV_AKI_mod_209 37
IMM_COV_AKI_mod_161 20
IMM_COV_AKI_mod_184 12
IMM_COV_AKI_mod_24 35
IMM_COV_AKI_mod_129 43
IMM_COV_AKI_mod_219 31
IMM_COV_AKI_mod_142 26
IMM_COV_AKI_mod_185 39
IMM_COV_AKI_mod_96 21
Number of saved communities: 22
Analysing IMM_DKD
Identified communities
Number of communities: 210
Average community size 10.466666666666667
Median community size 7.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 2. 7. 13. 125.]
Significant communities
Number of communities: 8
Average community size 43.875
Median community size 25.5
Quantile (0,0.25,0.5,0.75,1) community size [ 14. 18.25 25.5 54.5 125. ]
IMM_DKD_mod_18 22
IMM_DKD_mod_53 46
IMM_DKD_mod_114 19
IMM_DKD_mod_122 14
IMM_DKD_mod_1 125
IMM_DKD_mod_130 16
IMM_DKD_mod_4 80
IMM_DKD_mod_102 29
Number of saved communities: 1
Analysing IMM_H_CKD
Identified communities
Number of communities: 292
Average community size 23.595890410958905
Median community size 18.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 3.75 18. 34.25 199. ]
Significant communities
Number of communities: 9
Average community size 31.77777777777778
Median community size 20.0
Quantile (0,0.25,0.5,0.75,1) community size [11. 16. 20. 33. 75.]
IMM_H_CKD_mod_112 75
IMM_H_CKD_mod_1 11
IMM_H_CKD_mod_141 17
IMM_H_CKD_mod_2 20
IMM_H_CKD_mod_152 33
IMM_H_CKD_mod_39 74
IMM_H_CKD_mod_60 28
IMM_H_CKD_mod_162 12
IMM_H_CKD_mod_94 16
Number of saved communities: 0
NEU
{'NEU_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'NEU_COV_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'NEU_DKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'NEU_H_CKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing NEU_AKI
Identified communities
Number of communities: 217
Average community size 23.8110599078341
Median community size 20.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 12. 20. 30. 327.]
Significant communities
Number of communities: 100
Average community size 26.68
Median community size 23.0
Quantile (0,0.25,0.5,0.75,1) community size [10. 16.75 23. 34. 75. ]
NEU_AKI_mod_184 18
NEU_AKI_mod_173 21
NEU_AKI_mod_111 23
NEU_AKI_mod_23 23
NEU_AKI_mod_11 15
NEU_AKI_mod_78 12
NEU_AKI_mod_39 31
NEU_AKI_mod_95 10
NEU_AKI_mod_105 26
NEU_AKI_mod_10 38
NEU_AKI_mod_196 16
NEU_AKI_mod_5 15
NEU_AKI_mod_119 15
NEU_AKI_mod_205 18
NEU_AKI_mod_92 37
NEU_AKI_mod_109 22
NEU_AKI_mod_157 21
NEU_AKI_mod_70 20
NEU_AKI_mod_186 15
NEU_AKI_mod_147 14
NEU_AKI_mod_191 12
NEU_AKI_mod_144 22
NEU_AKI_mod_77 26
NEU_AKI_mod_178 24
NEU_AKI_mod_107 32
NEU_AKI_mod_99 24
NEU_AKI_mod_192 18
NEU_AKI_mod_113 24
NEU_AKI_mod_94 17
NEU_AKI_mod_48 32
NEU_AKI_mod_126 41
NEU_AKI_mod_45 49
NEU_AKI_mod_51 25
NEU_AKI_mod_0 68
NEU_AKI_mod_65 22
NEU_AKI_mod_130 39
NEU_AKI_mod_123 19
NEU_AKI_mod_56 13
NEU_AKI_mod_35 34
NEU_AKI_mod_84 10
NEU_AKI_mod_28 40
NEU_AKI_mod_152 15
NEU_AKI_mod_57 13
NEU_AKI_mod_27 41
NEU_AKI_mod_9 51
NEU_AKI_mod_162 21
NEU_AKI_mod_142 10
NEU_AKI_mod_120 32
NEU_AKI_mod_188 11
NEU_AKI_mod_67 44
NEU_AKI_mod_204 14
NEU_AKI_mod_177 15
NEU_AKI_mod_36 36
NEU_AKI_mod_112 24
NEU_AKI_mod_182 14
NEU_AKI_mod_101 17
NEU_AKI_mod_90 31
NEU_AKI_mod_97 29
NEU_AKI_mod_1 29
NEU_AKI_mod_18 23
NEU_AKI_mod_181 15
NEU_AKI_mod_61 40
NEU_AKI_mod_104 31
NEU_AKI_mod_8 30
NEU_AKI_mod_86 38
NEU_AKI_mod_135 42
NEU_AKI_mod_174 21
NEU_AKI_mod_63 29
NEU_AKI_mod_19 34
NEU_AKI_mod_50 21
NEU_AKI_mod_7 38
NEU_AKI_mod_200 13
NEU_AKI_mod_46 47
NEU_AKI_mod_73 16
NEU_AKI_mod_72 48
NEU_AKI_mod_122 23
NEU_AKI_mod_17 75
NEU_AKI_mod_66 36
NEU_AKI_mod_4 50
NEU_AKI_mod_40 17
NEU_AKI_mod_172 14
NEU_AKI_mod_128 21
NEU_AKI_mod_32 36
NEU_AKI_mod_49 15
NEU_AKI_mod_169 21
NEU_AKI_mod_166 34
NEU_AKI_mod_187 12
NEU_AKI_mod_24 49
NEU_AKI_mod_16 20
NEU_AKI_mod_53 23
NEU_AKI_mod_161 21
NEU_AKI_mod_160 24
NEU_AKI_mod_43 68
NEU_AKI_mod_12 36
NEU_AKI_mod_156 30
NEU_AKI_mod_194 13
NEU_AKI_mod_82 29
NEU_AKI_mod_29 26
NEU_AKI_mod_20 23
NEU_AKI_mod_2 18
Number of saved communities: 37
Analysing NEU_COV_AKI
Identified communities
Number of communities: 192
Average community size 15.755208333333334
Median community size 13.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 7.75 13. 20.25 157. ]
Significant communities
Number of communities: 59
Average community size 20.45762711864407
Median community size 17.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 13. 17. 22.5 134. ]
NEU_COV_AKI_mod_108 10
NEU_COV_AKI_mod_86 11
NEU_COV_AKI_mod_164 13
NEU_COV_AKI_mod_54 14
NEU_COV_AKI_mod_63 23
NEU_COV_AKI_mod_6 14
NEU_COV_AKI_mod_45 29
NEU_COV_AKI_mod_85 25
NEU_COV_AKI_mod_121 32
NEU_COV_AKI_mod_25 20
NEU_COV_AKI_mod_162 12
NEU_COV_AKI_mod_135 11
NEU_COV_AKI_mod_143 19
NEU_COV_AKI_mod_89 11
NEU_COV_AKI_mod_134 11
NEU_COV_AKI_mod_56 12
NEU_COV_AKI_mod_113 25
NEU_COV_AKI_mod_23 31
NEU_COV_AKI_mod_100 12
NEU_COV_AKI_mod_53 23
NEU_COV_AKI_mod_24 22
NEU_COV_AKI_mod_5 20
NEU_COV_AKI_mod_32 18
NEU_COV_AKI_mod_172 10
NEU_COV_AKI_mod_3 134
NEU_COV_AKI_mod_49 13
NEU_COV_AKI_mod_50 15
NEU_COV_AKI_mod_22 14
NEU_COV_AKI_mod_79 12
NEU_COV_AKI_mod_78 22
NEU_COV_AKI_mod_156 15
NEU_COV_AKI_mod_60 21
NEU_COV_AKI_mod_51 19
NEU_COV_AKI_mod_130 17
NEU_COV_AKI_mod_13 34
NEU_COV_AKI_mod_95 10
NEU_COV_AKI_mod_116 37
NEU_COV_AKI_mod_16 22
NEU_COV_AKI_mod_10 24
NEU_COV_AKI_mod_37 20
NEU_COV_AKI_mod_128 24
NEU_COV_AKI_mod_69 32
NEU_COV_AKI_mod_57 14
NEU_COV_AKI_mod_33 13
NEU_COV_AKI_mod_38 15
NEU_COV_AKI_mod_96 19
NEU_COV_AKI_mod_168 15
NEU_COV_AKI_mod_154 11
NEU_COV_AKI_mod_27 40
NEU_COV_AKI_mod_131 15
NEU_COV_AKI_mod_73 20
NEU_COV_AKI_mod_67 18
NEU_COV_AKI_mod_19 14
NEU_COV_AKI_mod_66 16
NEU_COV_AKI_mod_18 13
NEU_COV_AKI_mod_62 14
NEU_COV_AKI_mod_58 23
NEU_COV_AKI_mod_153 13
NEU_COV_AKI_mod_28 21
Number of saved communities: 18
Analysing NEU_DKD
Identified communities
Number of communities: 254
Average community size 24.41732283464567
Median community size 21.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 15. 21. 30. 148.]
Significant communities
Number of communities: 76
Average community size 27.13157894736842
Median community size 22.5
Quantile (0,0.25,0.5,0.75,1) community size [ 12. 19. 22.5 31. 102. ]
NEU_DKD_mod_91 20
NEU_DKD_mod_104 19
NEU_DKD_mod_77 41
NEU_DKD_mod_241 13
NEU_DKD_mod_171 14
NEU_DKD_mod_127 30
NEU_DKD_mod_208 14
NEU_DKD_mod_214 16
NEU_DKD_mod_74 18
NEU_DKD_mod_182 24
NEU_DKD_mod_81 71
NEU_DKD_mod_68 19
NEU_DKD_mod_215 12
NEU_DKD_mod_138 23
NEU_DKD_mod_212 19
NEU_DKD_mod_184 22
NEU_DKD_mod_130 17
NEU_DKD_mod_61 16
NEU_DKD_mod_121 18
NEU_DKD_mod_117 60
NEU_DKD_mod_235 19
NEU_DKD_mod_101 15
NEU_DKD_mod_162 19
NEU_DKD_mod_152 36
NEU_DKD_mod_79 22
NEU_DKD_mod_181 23
NEU_DKD_mod_58 25
NEU_DKD_mod_120 14
NEU_DKD_mod_7 26
NEU_DKD_mod_202 26
NEU_DKD_mod_187 12
NEU_DKD_mod_27 46
NEU_DKD_mod_20 31
NEU_DKD_mod_199 20
NEU_DKD_mod_1 51
NEU_DKD_mod_191 17
NEU_DKD_mod_106 50
NEU_DKD_mod_168 16
NEU_DKD_mod_177 19
NEU_DKD_mod_0 102
NEU_DKD_mod_189 38
NEU_DKD_mod_227 25
NEU_DKD_mod_141 21
NEU_DKD_mod_169 25
NEU_DKD_mod_76 23
NEU_DKD_mod_194 36
NEU_DKD_mod_64 24
NEU_DKD_mod_163 19
NEU_DKD_mod_39 24
NEU_DKD_mod_238 17
NEU_DKD_mod_98 37
NEU_DKD_mod_13 20
NEU_DKD_mod_89 31
NEU_DKD_mod_123 13
NEU_DKD_mod_195 19
NEU_DKD_mod_173 26
NEU_DKD_mod_109 27
NEU_DKD_mod_174 41
NEU_DKD_mod_131 30
NEU_DKD_mod_26 26
NEU_DKD_mod_185 41
NEU_DKD_mod_223 17
NEU_DKD_mod_11 36
NEU_DKD_mod_140 44
NEU_DKD_mod_2 21
NEU_DKD_mod_49 18
NEU_DKD_mod_186 22
NEU_DKD_mod_90 22
NEU_DKD_mod_78 26
NEU_DKD_mod_147 32
NEU_DKD_mod_88 19
NEU_DKD_mod_132 47
NEU_DKD_mod_145 53
NEU_DKD_mod_200 21
NEU_DKD_mod_178 25
NEU_DKD_mod_60 21
Number of saved communities: 18
Analysing NEU_H_CKD
Identified communities
Number of communities: 228
Average community size 26.092105263157894
Median community size 21.5
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 14. 21.5 34. 345. ]
Significant communities
Number of communities: 133
Average community size 29.601503759398497
Median community size 25.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 17. 25. 37. 345.]
NEU_H_CKD_mod_177 14
NEU_H_CKD_mod_55 27
NEU_H_CKD_mod_4 24
NEU_H_CKD_mod_5 63
NEU_H_CKD_mod_48 12
NEU_H_CKD_mod_183 18
NEU_H_CKD_mod_73 14
NEU_H_CKD_mod_151 19
NEU_H_CKD_mod_70 18
NEU_H_CKD_mod_214 13
NEU_H_CKD_mod_94 30
NEU_H_CKD_mod_211 13
NEU_H_CKD_mod_25 22
NEU_H_CKD_mod_1 37
NEU_H_CKD_mod_173 10
NEU_H_CKD_mod_6 345
NEU_H_CKD_mod_43 86
NEU_H_CKD_mod_15 28
NEU_H_CKD_mod_21 25
NEU_H_CKD_mod_56 37
NEU_H_CKD_mod_33 52
NEU_H_CKD_mod_185 53
NEU_H_CKD_mod_189 14
NEU_H_CKD_mod_7 16
NEU_H_CKD_mod_40 27
NEU_H_CKD_mod_168 13
NEU_H_CKD_mod_20 28
NEU_H_CKD_mod_103 33
NEU_H_CKD_mod_192 21
NEU_H_CKD_mod_62 39
NEU_H_CKD_mod_205 22
NEU_H_CKD_mod_219 12
NEU_H_CKD_mod_130 24
NEU_H_CKD_mod_58 31
NEU_H_CKD_mod_53 30
NEU_H_CKD_mod_140 15
NEU_H_CKD_mod_121 39
NEU_H_CKD_mod_31 65
NEU_H_CKD_mod_223 14
NEU_H_CKD_mod_32 30
NEU_H_CKD_mod_110 22
NEU_H_CKD_mod_101 15
NEU_H_CKD_mod_169 21
NEU_H_CKD_mod_203 17
NEU_H_CKD_mod_29 11
NEU_H_CKD_mod_115 37
NEU_H_CKD_mod_160 23
NEU_H_CKD_mod_196 19
NEU_H_CKD_mod_202 17
NEU_H_CKD_mod_9 27
NEU_H_CKD_mod_210 25
NEU_H_CKD_mod_150 30
NEU_H_CKD_mod_105 17
NEU_H_CKD_mod_61 17
NEU_H_CKD_mod_144 18
NEU_H_CKD_mod_39 25
NEU_H_CKD_mod_178 22
NEU_H_CKD_mod_98 15
NEU_H_CKD_mod_75 16
NEU_H_CKD_mod_126 16
NEU_H_CKD_mod_49 17
NEU_H_CKD_mod_95 19
NEU_H_CKD_mod_82 20
NEU_H_CKD_mod_11 16
NEU_H_CKD_mod_197 19
NEU_H_CKD_mod_83 15
NEU_H_CKD_mod_92 11
NEU_H_CKD_mod_38 37
NEU_H_CKD_mod_23 17
NEU_H_CKD_mod_65 19
NEU_H_CKD_mod_78 56
NEU_H_CKD_mod_102 18
NEU_H_CKD_mod_89 27
NEU_H_CKD_mod_163 14
NEU_H_CKD_mod_153 17
NEU_H_CKD_mod_57 34
NEU_H_CKD_mod_146 12
NEU_H_CKD_mod_176 27
NEU_H_CKD_mod_139 31
NEU_H_CKD_mod_147 41
NEU_H_CKD_mod_198 38
NEU_H_CKD_mod_66 17
NEU_H_CKD_mod_207 11
NEU_H_CKD_mod_35 50
NEU_H_CKD_mod_170 34
NEU_H_CKD_mod_18 40
NEU_H_CKD_mod_71 47
NEU_H_CKD_mod_120 49
NEU_H_CKD_mod_37 38
NEU_H_CKD_mod_3 26
NEU_H_CKD_mod_59 18
NEU_H_CKD_mod_148 30
NEU_H_CKD_mod_161 32
NEU_H_CKD_mod_174 48
NEU_H_CKD_mod_19 35
NEU_H_CKD_mod_87 39
NEU_H_CKD_mod_96 36
NEU_H_CKD_mod_179 17
NEU_H_CKD_mod_157 31
NEU_H_CKD_mod_187 15
NEU_H_CKD_mod_52 45
NEU_H_CKD_mod_17 10
NEU_H_CKD_mod_80 41
NEU_H_CKD_mod_44 37
NEU_H_CKD_mod_46 22
NEU_H_CKD_mod_143 42
NEU_H_CKD_mod_34 44
NEU_H_CKD_mod_45 38
NEU_H_CKD_mod_134 13
NEU_H_CKD_mod_88 28
NEU_H_CKD_mod_72 39
NEU_H_CKD_mod_195 27
NEU_H_CKD_mod_181 21
NEU_H_CKD_mod_200 32
NEU_H_CKD_mod_85 39
NEU_H_CKD_mod_41 51
NEU_H_CKD_mod_81 31
NEU_H_CKD_mod_74 17
NEU_H_CKD_mod_60 27
NEU_H_CKD_mod_184 15
NEU_H_CKD_mod_194 15
NEU_H_CKD_mod_122 39
NEU_H_CKD_mod_221 22
NEU_H_CKD_mod_106 43
NEU_H_CKD_mod_116 26
NEU_H_CKD_mod_97 12
NEU_H_CKD_mod_16 21
NEU_H_CKD_mod_86 39
NEU_H_CKD_mod_142 43
NEU_H_CKD_mod_13 51
NEU_H_CKD_mod_119 14
NEU_H_CKD_mod_152 20
NEU_H_CKD_mod_206 12
Number of saved communities: 57
PC
{'PC_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'PC_COV_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'PC_DKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'PC_H_CKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing PC_AKI
Identified communities
Number of communities: 255
Average community size 25.772549019607844
Median community size 21.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 12. 21. 33. 272.]
Significant communities
Number of communities: 27
Average community size 37.2962962962963
Median community size 26.0
Quantile (0,0.25,0.5,0.75,1) community size [ 12. 16.5 26. 48. 146. ]
PC_AKI_mod_213 12
PC_AKI_mod_195 22
PC_AKI_mod_205 41
PC_AKI_mod_49 32
PC_AKI_mod_48 66
PC_AKI_mod_13 95
PC_AKI_mod_99 42
PC_AKI_mod_176 15
PC_AKI_mod_19 38
PC_AKI_mod_153 49
PC_AKI_mod_33 22
PC_AKI_mod_114 14
PC_AKI_mod_60 58
PC_AKI_mod_22 18
PC_AKI_mod_95 50
PC_AKI_mod_63 41
PC_AKI_mod_123 51
PC_AKI_mod_8 146
PC_AKI_mod_215 16
PC_AKI_mod_212 15
PC_AKI_mod_228 17
PC_AKI_mod_97 16
PC_AKI_mod_147 26
PC_AKI_mod_57 47
PC_AKI_mod_79 20
PC_AKI_mod_206 23
PC_AKI_mod_202 15
Number of saved communities: 6
Analysing PC_COV_AKI
Identified communities
Number of communities: 267
Average community size 30.53183520599251
Median community size 23.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 13. 23. 36. 365.]
Significant communities
Number of communities: 97
Average community size 37.09278350515464
Median community size 25.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 17. 25. 33. 365.]
PC_COV_AKI_mod_45 77
PC_COV_AKI_mod_92 19
PC_COV_AKI_mod_170 16
PC_COV_AKI_mod_163 29
PC_COV_AKI_mod_80 13
PC_COV_AKI_mod_158 15
PC_COV_AKI_mod_0 216
PC_COV_AKI_mod_191 12
PC_COV_AKI_mod_18 20
PC_COV_AKI_mod_111 25
PC_COV_AKI_mod_243 12
PC_COV_AKI_mod_164 14
PC_COV_AKI_mod_224 13
PC_COV_AKI_mod_4 61
PC_COV_AKI_mod_97 29
PC_COV_AKI_mod_98 24
PC_COV_AKI_mod_219 12
PC_COV_AKI_mod_89 365
PC_COV_AKI_mod_148 28
PC_COV_AKI_mod_67 40
PC_COV_AKI_mod_13 48
PC_COV_AKI_mod_255 21
PC_COV_AKI_mod_199 19
PC_COV_AKI_mod_20 33
PC_COV_AKI_mod_233 22
PC_COV_AKI_mod_11 51
PC_COV_AKI_mod_32 18
PC_COV_AKI_mod_99 25
PC_COV_AKI_mod_196 15
PC_COV_AKI_mod_216 27
PC_COV_AKI_mod_180 12
PC_COV_AKI_mod_137 19
PC_COV_AKI_mod_96 31
PC_COV_AKI_mod_41 66
PC_COV_AKI_mod_147 19
PC_COV_AKI_mod_70 11
PC_COV_AKI_mod_253 12
PC_COV_AKI_mod_56 81
PC_COV_AKI_mod_145 18
PC_COV_AKI_mod_88 16
PC_COV_AKI_mod_198 13
PC_COV_AKI_mod_208 13
PC_COV_AKI_mod_187 25
PC_COV_AKI_mod_106 19
PC_COV_AKI_mod_17 38
PC_COV_AKI_mod_9 243
PC_COV_AKI_mod_234 15
PC_COV_AKI_mod_115 26
PC_COV_AKI_mod_33 18
PC_COV_AKI_mod_128 27
PC_COV_AKI_mod_231 17
PC_COV_AKI_mod_51 15
PC_COV_AKI_mod_200 41
PC_COV_AKI_mod_1 91
PC_COV_AKI_mod_176 10
PC_COV_AKI_mod_8 41
PC_COV_AKI_mod_110 30
PC_COV_AKI_mod_64 23
PC_COV_AKI_mod_28 108
PC_COV_AKI_mod_188 26
PC_COV_AKI_mod_69 31
PC_COV_AKI_mod_38 77
PC_COV_AKI_mod_109 29
PC_COV_AKI_mod_169 29
PC_COV_AKI_mod_60 136
PC_COV_AKI_mod_53 12
PC_COV_AKI_mod_107 39
PC_COV_AKI_mod_34 20
PC_COV_AKI_mod_252 12
PC_COV_AKI_mod_123 49
PC_COV_AKI_mod_174 30
PC_COV_AKI_mod_12 28
PC_COV_AKI_mod_83 38
PC_COV_AKI_mod_50 28
PC_COV_AKI_mod_168 36
PC_COV_AKI_mod_183 30
PC_COV_AKI_mod_172 19
PC_COV_AKI_mod_264 27
PC_COV_AKI_mod_186 16
PC_COV_AKI_mod_40 17
PC_COV_AKI_mod_185 23
PC_COV_AKI_mod_122 30
PC_COV_AKI_mod_153 33
PC_COV_AKI_mod_103 38
PC_COV_AKI_mod_124 40
PC_COV_AKI_mod_135 25
PC_COV_AKI_mod_85 11
PC_COV_AKI_mod_167 25
PC_COV_AKI_mod_5 22
PC_COV_AKI_mod_75 15
PC_COV_AKI_mod_74 30
PC_COV_AKI_mod_68 32
PC_COV_AKI_mod_59 17
PC_COV_AKI_mod_215 27
PC_COV_AKI_mod_22 45
PC_COV_AKI_mod_171 20
PC_COV_AKI_mod_116 19
Number of saved communities: 25
Analysing PC_DKD
Identified communities
Number of communities: 235
Average community size 12.689361702127659
Median community size 9.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 2. 9. 18. 316.]
Significant communities
Number of communities: 1
Average community size 25.0
Median community size 25.0
Quantile (0,0.25,0.5,0.75,1) community size [25. 25. 25. 25. 25.]
PC_DKD_mod_68 25
Number of saved communities: 0
Analysing PC_H_CKD
Identified communities
Number of communities: 244
Average community size 13.598360655737705
Median community size 10.5
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 2. 10.5 17. 298. ]
Significant communities
Number of communities: 0
Average community size -
Median community size -
Quantile (0,0.25,0.5,0.75,1) community size -
Number of saved communities: 0
PEC
{'PEC_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'PEC_COV_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'PEC_DKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'PEC_H_CKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing PEC_AKI
Identified communities
Number of communities: 292
Average community size 40.30821917808219
Median community size 31.5
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 19.75 31.5 53. 200. ]
Significant communities
Number of communities: 76
Average community size 50.026315789473685
Median community size 46.0
Quantile (0,0.25,0.5,0.75,1) community size [ 11. 23.75 46. 65. 181. ]
PEC_AKI_mod_211 13
PEC_AKI_mod_281 12
PEC_AKI_mod_270 12
PEC_AKI_mod_135 15
PEC_AKI_mod_11 181
PEC_AKI_mod_120 115
PEC_AKI_mod_143 49
PEC_AKI_mod_237 46
PEC_AKI_mod_159 46
PEC_AKI_mod_103 77
PEC_AKI_mod_102 20
PEC_AKI_mod_267 11
PEC_AKI_mod_234 70
PEC_AKI_mod_17 65
PEC_AKI_mod_231 13
PEC_AKI_mod_74 65
PEC_AKI_mod_44 27
PEC_AKI_mod_65 56
PEC_AKI_mod_39 89
PEC_AKI_mod_94 47
PEC_AKI_mod_67 41
PEC_AKI_mod_41 18
PEC_AKI_mod_116 34
PEC_AKI_mod_144 40
PEC_AKI_mod_113 77
PEC_AKI_mod_196 14
PEC_AKI_mod_133 13
PEC_AKI_mod_218 20
PEC_AKI_mod_228 36
PEC_AKI_mod_152 68
PEC_AKI_mod_179 53
PEC_AKI_mod_56 88
PEC_AKI_mod_250 24
PEC_AKI_mod_97 112
PEC_AKI_mod_214 57
PEC_AKI_mod_282 28
PEC_AKI_mod_117 26
PEC_AKI_mod_155 52
PEC_AKI_mod_12 68
PEC_AKI_mod_59 22
PEC_AKI_mod_206 69
PEC_AKI_mod_88 48
PEC_AKI_mod_16 170
PEC_AKI_mod_147 87
PEC_AKI_mod_195 16
PEC_AKI_mod_1 56
PEC_AKI_mod_30 59
PEC_AKI_mod_275 22
PEC_AKI_mod_45 48
PEC_AKI_mod_71 34
PEC_AKI_mod_240 21
PEC_AKI_mod_198 27
PEC_AKI_mod_123 53
PEC_AKI_mod_112 73
PEC_AKI_mod_161 46
PEC_AKI_mod_238 50
PEC_AKI_mod_68 55
PEC_AKI_mod_34 53
PEC_AKI_mod_106 22
PEC_AKI_mod_86 67
PEC_AKI_mod_31 62
PEC_AKI_mod_13 32
PEC_AKI_mod_63 41
PEC_AKI_mod_185 54
PEC_AKI_mod_38 23
PEC_AKI_mod_25 91
PEC_AKI_mod_153 115
PEC_AKI_mod_93 29
PEC_AKI_mod_132 42
PEC_AKI_mod_137 54
PEC_AKI_mod_36 127
PEC_AKI_mod_190 37
PEC_AKI_mod_213 39
PEC_AKI_mod_115 32
PEC_AKI_mod_156 11
PEC_AKI_mod_266 17
Number of saved communities: 46
Analysing PEC_COV_AKI
Identified communities
Number of communities: 293
Average community size 44.1160409556314
Median community size 36.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 20. 36. 57. 256.]
Significant communities
Number of communities: 179
Average community size 47.94972067039106
Median community size 39.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 23. 39. 61. 256.]
PEC_COV_AKI_mod_158 16
PEC_COV_AKI_mod_114 17
PEC_COV_AKI_mod_145 20
PEC_COV_AKI_mod_135 22
PEC_COV_AKI_mod_49 21
PEC_COV_AKI_mod_248 41
PEC_COV_AKI_mod_134 115
PEC_COV_AKI_mod_255 11
PEC_COV_AKI_mod_263 25
PEC_COV_AKI_mod_124 36
PEC_COV_AKI_mod_153 47
PEC_COV_AKI_mod_69 70
PEC_COV_AKI_mod_42 25
PEC_COV_AKI_mod_210 20
PEC_COV_AKI_mod_224 30
PEC_COV_AKI_mod_86 16
PEC_COV_AKI_mod_39 62
PEC_COV_AKI_mod_247 16
PEC_COV_AKI_mod_48 127
PEC_COV_AKI_mod_14 189
PEC_COV_AKI_mod_196 15
PEC_COV_AKI_mod_177 37
PEC_COV_AKI_mod_116 37
PEC_COV_AKI_mod_112 12
PEC_COV_AKI_mod_154 30
PEC_COV_AKI_mod_181 42
PEC_COV_AKI_mod_113 30
PEC_COV_AKI_mod_265 26
PEC_COV_AKI_mod_110 102
PEC_COV_AKI_mod_22 53
PEC_COV_AKI_mod_160 43
PEC_COV_AKI_mod_1 32
PEC_COV_AKI_mod_89 52
PEC_COV_AKI_mod_147 28
PEC_COV_AKI_mod_237 86
PEC_COV_AKI_mod_10 95
PEC_COV_AKI_mod_194 13
PEC_COV_AKI_mod_9 188
PEC_COV_AKI_mod_37 104
PEC_COV_AKI_mod_73 35
PEC_COV_AKI_mod_266 10
PEC_COV_AKI_mod_24 132
PEC_COV_AKI_mod_7 70
PEC_COV_AKI_mod_156 40
PEC_COV_AKI_mod_157 44
PEC_COV_AKI_mod_128 29
PEC_COV_AKI_mod_239 31
PEC_COV_AKI_mod_138 19
PEC_COV_AKI_mod_99 144
PEC_COV_AKI_mod_150 17
PEC_COV_AKI_mod_81 17
PEC_COV_AKI_mod_283 16
PEC_COV_AKI_mod_74 21
PEC_COV_AKI_mod_178 20
PEC_COV_AKI_mod_264 10
PEC_COV_AKI_mod_38 69
PEC_COV_AKI_mod_54 60
PEC_COV_AKI_mod_161 62
PEC_COV_AKI_mod_92 84
PEC_COV_AKI_mod_72 50
PEC_COV_AKI_mod_53 28
PEC_COV_AKI_mod_127 28
PEC_COV_AKI_mod_76 18
PEC_COV_AKI_mod_70 63
PEC_COV_AKI_mod_96 47
PEC_COV_AKI_mod_29 69
PEC_COV_AKI_mod_119 112
PEC_COV_AKI_mod_98 61
PEC_COV_AKI_mod_130 28
PEC_COV_AKI_mod_16 78
PEC_COV_AKI_mod_259 17
PEC_COV_AKI_mod_133 18
PEC_COV_AKI_mod_206 46
PEC_COV_AKI_mod_167 62
PEC_COV_AKI_mod_36 27
PEC_COV_AKI_mod_176 50
PEC_COV_AKI_mod_256 39
PEC_COV_AKI_mod_117 42
PEC_COV_AKI_mod_13 55
PEC_COV_AKI_mod_146 19
PEC_COV_AKI_mod_213 53
PEC_COV_AKI_mod_31 45
PEC_COV_AKI_mod_109 256
PEC_COV_AKI_mod_170 55
PEC_COV_AKI_mod_5 45
PEC_COV_AKI_mod_203 23
PEC_COV_AKI_mod_199 12
PEC_COV_AKI_mod_58 97
PEC_COV_AKI_mod_243 32
PEC_COV_AKI_mod_285 12
PEC_COV_AKI_mod_172 30
PEC_COV_AKI_mod_60 39
PEC_COV_AKI_mod_108 82
PEC_COV_AKI_mod_201 15
PEC_COV_AKI_mod_262 46
PEC_COV_AKI_mod_132 20
PEC_COV_AKI_mod_102 31
PEC_COV_AKI_mod_80 63
PEC_COV_AKI_mod_4 67
PEC_COV_AKI_mod_236 12
PEC_COV_AKI_mod_202 11
PEC_COV_AKI_mod_20 60
PEC_COV_AKI_mod_221 23
PEC_COV_AKI_mod_189 43
PEC_COV_AKI_mod_8 64
PEC_COV_AKI_mod_229 39
PEC_COV_AKI_mod_253 19
PEC_COV_AKI_mod_0 102
PEC_COV_AKI_mod_32 50
PEC_COV_AKI_mod_211 39
PEC_COV_AKI_mod_231 20
PEC_COV_AKI_mod_45 111
PEC_COV_AKI_mod_63 49
PEC_COV_AKI_mod_152 28
PEC_COV_AKI_mod_191 46
PEC_COV_AKI_mod_222 53
PEC_COV_AKI_mod_139 36
PEC_COV_AKI_mod_71 37
PEC_COV_AKI_mod_207 27
PEC_COV_AKI_mod_87 95
PEC_COV_AKI_mod_227 71
PEC_COV_AKI_mod_261 19
PEC_COV_AKI_mod_190 30
PEC_COV_AKI_mod_245 16
PEC_COV_AKI_mod_33 115
PEC_COV_AKI_mod_235 41
PEC_COV_AKI_mod_95 53
PEC_COV_AKI_mod_228 30
PEC_COV_AKI_mod_51 127
PEC_COV_AKI_mod_115 34
PEC_COV_AKI_mod_197 28
PEC_COV_AKI_mod_275 29
PEC_COV_AKI_mod_2 59
PEC_COV_AKI_mod_215 28
PEC_COV_AKI_mod_43 39
PEC_COV_AKI_mod_103 46
PEC_COV_AKI_mod_148 86
PEC_COV_AKI_mod_137 69
PEC_COV_AKI_mod_187 26
PEC_COV_AKI_mod_257 20
PEC_COV_AKI_mod_41 37
PEC_COV_AKI_mod_121 63
PEC_COV_AKI_mod_35 74
PEC_COV_AKI_mod_40 72
PEC_COV_AKI_mod_19 26
PEC_COV_AKI_mod_55 52
PEC_COV_AKI_mod_217 102
PEC_COV_AKI_mod_166 32
PEC_COV_AKI_mod_57 20
PEC_COV_AKI_mod_66 61
PEC_COV_AKI_mod_94 30
PEC_COV_AKI_mod_240 53
PEC_COV_AKI_mod_216 18
PEC_COV_AKI_mod_284 20
PEC_COV_AKI_mod_129 54
PEC_COV_AKI_mod_52 127
PEC_COV_AKI_mod_56 36
PEC_COV_AKI_mod_238 25
PEC_COV_AKI_mod_30 71
PEC_COV_AKI_mod_226 40
PEC_COV_AKI_mod_83 35
PEC_COV_AKI_mod_251 22
PEC_COV_AKI_mod_111 43
PEC_COV_AKI_mod_91 75
PEC_COV_AKI_mod_149 48
PEC_COV_AKI_mod_267 14
PEC_COV_AKI_mod_23 117
PEC_COV_AKI_mod_169 43
PEC_COV_AKI_mod_11 52
PEC_COV_AKI_mod_75 54
PEC_COV_AKI_mod_208 44
PEC_COV_AKI_mod_67 68
PEC_COV_AKI_mod_268 13
PEC_COV_AKI_mod_272 28
PEC_COV_AKI_mod_77 17
PEC_COV_AKI_mod_82 22
PEC_COV_AKI_mod_151 43
PEC_COV_AKI_mod_184 25
PEC_COV_AKI_mod_198 16
Number of saved communities: 116
Analysing PEC_DKD
Identified communities
Number of communities: 247
Average community size 14.59919028340081
Median community size 13.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 3. 13. 20. 191.]
Significant communities
Number of communities: 7
Average community size 25.428571428571427
Median community size 21.0
Quantile (0,0.25,0.5,0.75,1) community size [11. 17.5 21. 33.5 44. ]
PEC_DKD_mod_110 33
PEC_DKD_mod_148 34
PEC_DKD_mod_70 11
PEC_DKD_mod_91 17
PEC_DKD_mod_43 44
PEC_DKD_mod_121 21
PEC_DKD_mod_206 18
Number of saved communities: 0
Analysing PEC_H_CKD
Identified communities
Number of communities: 277
Average community size 27.339350180505416
Median community size 23.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 11. 23. 34. 292.]
Significant communities
Number of communities: 36
Average community size 37.55555555555556
Median community size 34.0
Quantile (0,0.25,0.5,0.75,1) community size [11. 23.75 34. 47.5 93. ]
PEC_H_CKD_mod_119 35
PEC_H_CKD_mod_236 72
PEC_H_CKD_mod_130 32
PEC_H_CKD_mod_22 65
PEC_H_CKD_mod_257 14
PEC_H_CKD_mod_57 34
PEC_H_CKD_mod_122 66
PEC_H_CKD_mod_209 21
PEC_H_CKD_mod_131 49
PEC_H_CKD_mod_188 33
PEC_H_CKD_mod_111 19
PEC_H_CKD_mod_170 35
PEC_H_CKD_mod_23 19
PEC_H_CKD_mod_240 11
PEC_H_CKD_mod_40 29
PEC_H_CKD_mod_133 37
PEC_H_CKD_mod_218 34
PEC_H_CKD_mod_100 67
PEC_H_CKD_mod_144 38
PEC_H_CKD_mod_120 29
PEC_H_CKD_mod_27 47
PEC_H_CKD_mod_195 16
PEC_H_CKD_mod_232 34
PEC_H_CKD_mod_72 49
PEC_H_CKD_mod_46 31
PEC_H_CKD_mod_221 34
PEC_H_CKD_mod_54 64
PEC_H_CKD_mod_31 29
PEC_H_CKD_mod_3 23
PEC_H_CKD_mod_95 93
PEC_H_CKD_mod_228 13
PEC_H_CKD_mod_196 24
PEC_H_CKD_mod_29 47
PEC_H_CKD_mod_253 15
PEC_H_CKD_mod_189 29
PEC_H_CKD_mod_156 65
Number of saved communities: 8
POD
{'POD_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'POD_COV_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'POD_DKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'POD_H_CKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing POD_AKI
Identified communities
Number of communities: 283
Average community size 39.32508833922262
Median community size 30.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 17. 30. 49.5 607. ]
Significant communities
Number of communities: 50
Average community size 56.16
Median community size 33.5
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 17. 33.5 55.25 607. ]
POD_AKI_mod_253 10
POD_AKI_mod_122 51
POD_AKI_mod_58 130
POD_AKI_mod_118 99
POD_AKI_mod_49 116
POD_AKI_mod_270 11
POD_AKI_mod_11 52
POD_AKI_mod_56 607
POD_AKI_mod_130 19
POD_AKI_mod_0 206
POD_AKI_mod_5 60
POD_AKI_mod_219 16
POD_AKI_mod_158 14
POD_AKI_mod_98 49
POD_AKI_mod_225 16
POD_AKI_mod_119 17
POD_AKI_mod_81 37
POD_AKI_mod_116 71
POD_AKI_mod_148 16
POD_AKI_mod_138 81
POD_AKI_mod_108 25
POD_AKI_mod_34 27
POD_AKI_mod_63 29
POD_AKI_mod_39 14
POD_AKI_mod_249 15
POD_AKI_mod_84 50
POD_AKI_mod_202 17
POD_AKI_mod_260 37
POD_AKI_mod_186 18
POD_AKI_mod_25 90
POD_AKI_mod_55 94
POD_AKI_mod_95 45
POD_AKI_mod_192 23
POD_AKI_mod_213 47
POD_AKI_mod_139 14
POD_AKI_mod_264 12
POD_AKI_mod_88 57
POD_AKI_mod_18 47
POD_AKI_mod_193 18
POD_AKI_mod_216 17
POD_AKI_mod_36 56
POD_AKI_mod_166 18
POD_AKI_mod_51 137
POD_AKI_mod_33 16
POD_AKI_mod_149 21
POD_AKI_mod_78 30
POD_AKI_mod_6 19
POD_AKI_mod_203 38
POD_AKI_mod_162 46
POD_AKI_mod_129 53
Number of saved communities: 15
Analysing POD_COV_AKI
Identified communities
Number of communities: 311
Average community size 33.98713826366559
Median community size 26.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 13. 26. 43. 287.]
Significant communities
Number of communities: 82
Average community size 44.829268292682926
Median community size 27.5
Quantile (0,0.25,0.5,0.75,1) community size [ 11. 18. 27.5 53. 287. ]
POD_COV_AKI_mod_140 16
POD_COV_AKI_mod_117 41
POD_COV_AKI_mod_125 11
POD_COV_AKI_mod_90 287
POD_COV_AKI_mod_0 245
POD_COV_AKI_mod_179 60
POD_COV_AKI_mod_230 26
POD_COV_AKI_mod_246 16
POD_COV_AKI_mod_132 21
POD_COV_AKI_mod_116 30
POD_COV_AKI_mod_184 14
POD_COV_AKI_mod_205 27
POD_COV_AKI_mod_156 23
POD_COV_AKI_mod_122 27
POD_COV_AKI_mod_9 108
POD_COV_AKI_mod_8 125
POD_COV_AKI_mod_77 24
POD_COV_AKI_mod_24 89
POD_COV_AKI_mod_85 24
POD_COV_AKI_mod_87 36
POD_COV_AKI_mod_35 69
POD_COV_AKI_mod_72 81
POD_COV_AKI_mod_13 53
POD_COV_AKI_mod_159 12
POD_COV_AKI_mod_193 65
POD_COV_AKI_mod_155 12
POD_COV_AKI_mod_217 25
POD_COV_AKI_mod_33 43
POD_COV_AKI_mod_241 17
POD_COV_AKI_mod_4 46
POD_COV_AKI_mod_252 16
POD_COV_AKI_mod_176 16
POD_COV_AKI_mod_86 12
POD_COV_AKI_mod_289 14
POD_COV_AKI_mod_124 26
POD_COV_AKI_mod_36 53
POD_COV_AKI_mod_20 75
POD_COV_AKI_mod_7 147
POD_COV_AKI_mod_25 250
POD_COV_AKI_mod_10 43
POD_COV_AKI_mod_94 26
POD_COV_AKI_mod_18 56
POD_COV_AKI_mod_100 61
POD_COV_AKI_mod_106 71
POD_COV_AKI_mod_244 24
POD_COV_AKI_mod_211 15
POD_COV_AKI_mod_55 24
POD_COV_AKI_mod_226 12
POD_COV_AKI_mod_39 70
POD_COV_AKI_mod_245 18
POD_COV_AKI_mod_251 33
POD_COV_AKI_mod_133 39
POD_COV_AKI_mod_103 33
POD_COV_AKI_mod_147 30
POD_COV_AKI_mod_163 38
POD_COV_AKI_mod_223 18
POD_COV_AKI_mod_257 32
POD_COV_AKI_mod_191 26
POD_COV_AKI_mod_247 17
POD_COV_AKI_mod_208 28
POD_COV_AKI_mod_218 24
POD_COV_AKI_mod_277 18
POD_COV_AKI_mod_41 31
POD_COV_AKI_mod_153 28
POD_COV_AKI_mod_31 62
POD_COV_AKI_mod_167 18
POD_COV_AKI_mod_52 12
POD_COV_AKI_mod_111 41
POD_COV_AKI_mod_71 58
POD_COV_AKI_mod_164 12
POD_COV_AKI_mod_104 14
POD_COV_AKI_mod_40 27
POD_COV_AKI_mod_143 37
POD_COV_AKI_mod_236 15
POD_COV_AKI_mod_228 23
POD_COV_AKI_mod_127 20
POD_COV_AKI_mod_37 55
POD_COV_AKI_mod_82 77
POD_COV_AKI_mod_47 28
POD_COV_AKI_mod_234 25
POD_COV_AKI_mod_114 40
POD_COV_AKI_mod_123 15
Number of saved communities: 28
Analysing POD_DKD
Identified communities
Number of communities: 240
Average community size 9.016666666666667
Median community size 5.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 2. 5. 15. 55.]
Significant communities
Number of communities: 3
Average community size 27.333333333333332
Median community size 22.0
Quantile (0,0.25,0.5,0.75,1) community size [16. 19. 22. 33. 44.]
POD_DKD_mod_18 44
POD_DKD_mod_91 22
POD_DKD_mod_59 16
Number of saved communities: 0
Analysing POD_H_CKD
Identified communities
Number of communities: 272
Average community size 22.136029411764707
Median community size 17.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 4.75 17. 26. 435. ]
Significant communities
Number of communities: 15
Average community size 47.06666666666667
Median community size 37.0
Quantile (0,0.25,0.5,0.75,1) community size [ 11. 24.5 37. 64. 115. ]
POD_H_CKD_mod_75 115
POD_H_CKD_mod_14 108
POD_H_CKD_mod_7 78
POD_H_CKD_mod_139 52
POD_H_CKD_mod_211 11
POD_H_CKD_mod_16 26
POD_H_CKD_mod_34 43
POD_H_CKD_mod_107 35
POD_H_CKD_mod_226 23
POD_H_CKD_mod_1 18
POD_H_CKD_mod_29 43
POD_H_CKD_mod_196 26
POD_H_CKD_mod_174 37
POD_H_CKD_mod_87 76
POD_H_CKD_mod_221 15
Number of saved communities: 4
PT
{'PT_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'PT_COV_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'PT_DKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'PT_H_CKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing PT_AKI
Identified communities
Number of communities: 291
Average community size 30.38487972508591
Median community size 24.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 11. 24. 43. 243.]
Significant communities
Number of communities: 120
Average community size 38.44166666666667
Median community size 33.0
Quantile (0,0.25,0.5,0.75,1) community size [ 11. 21. 33. 48.25 131. ]
PT_AKI_mod_104 48
PT_AKI_mod_91 38
PT_AKI_mod_166 21
PT_AKI_mod_189 24
PT_AKI_mod_225 15
PT_AKI_mod_253 20
PT_AKI_mod_178 32
PT_AKI_mod_143 14
PT_AKI_mod_160 30
PT_AKI_mod_289 19
PT_AKI_mod_152 27
PT_AKI_mod_82 26
PT_AKI_mod_169 25
PT_AKI_mod_60 63
PT_AKI_mod_137 38
PT_AKI_mod_243 19
PT_AKI_mod_181 32
PT_AKI_mod_66 42
PT_AKI_mod_122 21
PT_AKI_mod_182 11
PT_AKI_mod_113 19
PT_AKI_mod_216 19
PT_AKI_mod_29 18
PT_AKI_mod_0 33
PT_AKI_mod_127 77
PT_AKI_mod_105 39
PT_AKI_mod_110 46
PT_AKI_mod_4 34
PT_AKI_mod_108 60
PT_AKI_mod_176 49
PT_AKI_mod_129 57
PT_AKI_mod_177 34
PT_AKI_mod_94 48
PT_AKI_mod_12 79
PT_AKI_mod_214 21
PT_AKI_mod_215 56
PT_AKI_mod_30 11
PT_AKI_mod_65 49
PT_AKI_mod_236 17
PT_AKI_mod_230 14
PT_AKI_mod_6 30
PT_AKI_mod_46 80
PT_AKI_mod_138 35
PT_AKI_mod_45 50
PT_AKI_mod_47 38
PT_AKI_mod_248 24
PT_AKI_mod_147 34
PT_AKI_mod_119 39
PT_AKI_mod_114 14
PT_AKI_mod_16 98
PT_AKI_mod_85 38
PT_AKI_mod_48 53
PT_AKI_mod_167 54
PT_AKI_mod_244 12
PT_AKI_mod_121 131
PT_AKI_mod_69 29
PT_AKI_mod_212 32
PT_AKI_mod_22 81
PT_AKI_mod_168 33
PT_AKI_mod_84 30
PT_AKI_mod_2 24
PT_AKI_mod_171 61
PT_AKI_mod_89 19
PT_AKI_mod_81 21
PT_AKI_mod_145 25
PT_AKI_mod_211 37
PT_AKI_mod_151 51
PT_AKI_mod_64 11
PT_AKI_mod_149 24
PT_AKI_mod_112 36
PT_AKI_mod_186 20
PT_AKI_mod_118 51
PT_AKI_mod_131 18
PT_AKI_mod_234 32
PT_AKI_mod_98 117
PT_AKI_mod_77 52
PT_AKI_mod_136 64
PT_AKI_mod_56 46
PT_AKI_mod_103 26
PT_AKI_mod_38 88
PT_AKI_mod_133 31
PT_AKI_mod_43 98
PT_AKI_mod_51 21
PT_AKI_mod_229 34
PT_AKI_mod_128 24
PT_AKI_mod_54 46
PT_AKI_mod_8 47
PT_AKI_mod_155 15
PT_AKI_mod_97 17
PT_AKI_mod_267 21
PT_AKI_mod_21 58
PT_AKI_mod_213 12
PT_AKI_mod_153 33
PT_AKI_mod_50 20
PT_AKI_mod_135 32
PT_AKI_mod_270 12
PT_AKI_mod_249 31
PT_AKI_mod_78 72
PT_AKI_mod_180 34
PT_AKI_mod_74 28
PT_AKI_mod_58 24
PT_AKI_mod_159 41
PT_AKI_mod_27 46
PT_AKI_mod_15 53
PT_AKI_mod_190 19
PT_AKI_mod_57 39
PT_AKI_mod_120 61
PT_AKI_mod_172 21
PT_AKI_mod_266 18
PT_AKI_mod_61 43
PT_AKI_mod_62 20
PT_AKI_mod_80 19
PT_AKI_mod_116 48
PT_AKI_mod_59 21
PT_AKI_mod_76 103
PT_AKI_mod_224 37
PT_AKI_mod_67 40
PT_AKI_mod_44 73
PT_AKI_mod_35 77
PT_AKI_mod_184 21
Number of saved communities: 33
Analysing PT_COV_AKI
Identified communities
Number of communities: 280
Average community size 25.21785714285714
Median community size 15.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 3. 15. 31.25 326. ]
Significant communities
Number of communities: 26
Average community size 60.23076923076923
Median community size 27.5
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 14.25 27.5 36.75 326. ]
PT_COV_AKI_mod_90 19
PT_COV_AKI_mod_39 14
PT_COV_AKI_mod_195 11
PT_COV_AKI_mod_133 30
PT_COV_AKI_mod_0 320
PT_COV_AKI_mod_79 262
PT_COV_AKI_mod_29 326
PT_COV_AKI_mod_63 28
PT_COV_AKI_mod_4 135
PT_COV_AKI_mod_55 31
PT_COV_AKI_mod_120 10
PT_COV_AKI_mod_186 14
PT_COV_AKI_mod_117 11
PT_COV_AKI_mod_34 37
PT_COV_AKI_mod_176 26
PT_COV_AKI_mod_82 36
PT_COV_AKI_mod_41 43
PT_COV_AKI_mod_97 15
PT_COV_AKI_mod_180 38
PT_COV_AKI_mod_144 14
PT_COV_AKI_mod_59 27
PT_COV_AKI_mod_102 31
PT_COV_AKI_mod_151 29
PT_COV_AKI_mod_10 25
PT_COV_AKI_mod_148 14
PT_COV_AKI_mod_170 20
Number of saved communities: 5
Analysing PT_DKD
Identified communities
Number of communities: 205
Average community size 6.736585365853658
Median community size 3.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 2. 3. 9. 65.]
Significant communities
Number of communities: 6
Average community size 20.166666666666668
Median community size 15.0
Quantile (0,0.25,0.5,0.75,1) community size [13. 15. 15. 21.75 39. ]
PT_DKD_mod_0 24
PT_DKD_mod_177 15
PT_DKD_mod_50 39
PT_DKD_mod_7 15
PT_DKD_mod_63 13
PT_DKD_mod_35 15
Number of saved communities: 0
Analysing PT_H_CKD
Identified communities
Number of communities: 286
Average community size 20.41958041958042
Median community size 13.5
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 2. 13.5 29. 175. ]
Significant communities
Number of communities: 14
Average community size 20.571428571428573
Median community size 18.5
Quantile (0,0.25,0.5,0.75,1) community size [13. 14. 18.5 25.75 35. ]
PT_H_CKD_mod_51 22
PT_H_CKD_mod_43 14
PT_H_CKD_mod_196 13
PT_H_CKD_mod_88 21
PT_H_CKD_mod_64 13
PT_H_CKD_mod_22 33
PT_H_CKD_mod_163 32
PT_H_CKD_mod_23 27
PT_H_CKD_mod_35 21
PT_H_CKD_mod_39 14
PT_H_CKD_mod_200 13
PT_H_CKD_mod_134 35
PT_H_CKD_mod_4 14
PT_H_CKD_mod_120 16
Number of saved communities: 1
PapE
{'PapE_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'PapE_COV_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'PapE_DKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'PapE_H_CKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing PapE_AKI
Identified communities
Number of communities: 144
Average community size 12.444444444444445
Median community size 12.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 5.75 12. 16. 71. ]
Significant communities
Number of communities: 49
Average community size 17.26530612244898
Median community size 15.0
Quantile (0,0.25,0.5,0.75,1) community size [10. 12. 15. 19. 71.]
PapE_AKI_mod_61 10
PapE_AKI_mod_10 21
PapE_AKI_mod_58 15
PapE_AKI_mod_50 11
PapE_AKI_mod_74 11
PapE_AKI_mod_24 16
PapE_AKI_mod_2 17
PapE_AKI_mod_41 12
PapE_AKI_mod_75 11
PapE_AKI_mod_21 14
PapE_AKI_mod_59 23
PapE_AKI_mod_13 71
PapE_AKI_mod_87 13
PapE_AKI_mod_38 12
PapE_AKI_mod_77 22
PapE_AKI_mod_33 23
PapE_AKI_mod_107 12
PapE_AKI_mod_44 14
PapE_AKI_mod_89 10
PapE_AKI_mod_28 26
PapE_AKI_mod_55 12
PapE_AKI_mod_15 18
PapE_AKI_mod_121 13
PapE_AKI_mod_94 15
PapE_AKI_mod_7 19
PapE_AKI_mod_51 20
PapE_AKI_mod_122 17
PapE_AKI_mod_99 11
PapE_AKI_mod_43 13
PapE_AKI_mod_126 16
PapE_AKI_mod_76 12
PapE_AKI_mod_11 18
PapE_AKI_mod_27 13
PapE_AKI_mod_20 36
PapE_AKI_mod_32 17
PapE_AKI_mod_16 19
PapE_AKI_mod_78 22
PapE_AKI_mod_48 16
PapE_AKI_mod_72 18
PapE_AKI_mod_73 13
PapE_AKI_mod_22 27
PapE_AKI_mod_97 12
PapE_AKI_mod_8 15
PapE_AKI_mod_79 15
PapE_AKI_mod_83 11
PapE_AKI_mod_34 20
PapE_AKI_mod_40 13
PapE_AKI_mod_3 16
PapE_AKI_mod_108 15
Number of saved communities: 8
Analysing PapE_COV_AKI
Identified communities
Number of communities: 150
Average community size 5.92
Median community size 4.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 2. 4. 8. 24.]
Significant communities
Number of communities: 11
Average community size 14.545454545454545
Median community size 13.0
Quantile (0,0.25,0.5,0.75,1) community size [10. 11. 13. 15. 24.]
PapE_COV_AKI_mod_52 11
PapE_COV_AKI_mod_35 24
PapE_COV_AKI_mod_43 14
PapE_COV_AKI_mod_27 24
PapE_COV_AKI_mod_147 16
PapE_COV_AKI_mod_79 13
PapE_COV_AKI_mod_71 11
PapE_COV_AKI_mod_33 10
PapE_COV_AKI_mod_69 14
PapE_COV_AKI_mod_54 12
PapE_COV_AKI_mod_102 11
Number of saved communities: 1
Analysing PapE_DKD
Identified communities
Number of communities: 231
Average community size 20.493506493506494
Median community size 18.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 11. 18. 26. 146.]
Significant communities
Number of communities: 164
Average community size 25.408536585365855
Median community size 21.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 16. 21. 29. 146.]
PapE_DKD_mod_83 14
PapE_DKD_mod_135 26
PapE_DKD_mod_218 13
PapE_DKD_mod_108 25
PapE_DKD_mod_84 13
PapE_DKD_mod_157 10
PapE_DKD_mod_116 10
PapE_DKD_mod_200 25
PapE_DKD_mod_128 34
PapE_DKD_mod_50 29
PapE_DKD_mod_131 48
PapE_DKD_mod_76 16
PapE_DKD_mod_88 18
PapE_DKD_mod_217 15
PapE_DKD_mod_2 19
PapE_DKD_mod_205 18
PapE_DKD_mod_3 34
PapE_DKD_mod_30 26
PapE_DKD_mod_115 17
PapE_DKD_mod_137 17
PapE_DKD_mod_136 68
PapE_DKD_mod_221 15
PapE_DKD_mod_69 22
PapE_DKD_mod_168 41
PapE_DKD_mod_163 34
PapE_DKD_mod_54 33
PapE_DKD_mod_46 34
PapE_DKD_mod_97 27
PapE_DKD_mod_73 50
PapE_DKD_mod_187 14
PapE_DKD_mod_159 22
PapE_DKD_mod_10 21
PapE_DKD_mod_51 11
PapE_DKD_mod_125 14
PapE_DKD_mod_177 13
PapE_DKD_mod_62 31
PapE_DKD_mod_57 31
PapE_DKD_mod_34 41
PapE_DKD_mod_104 82
PapE_DKD_mod_190 12
PapE_DKD_mod_45 23
PapE_DKD_mod_207 13
PapE_DKD_mod_102 22
PapE_DKD_mod_12 18
PapE_DKD_mod_103 28
PapE_DKD_mod_209 12
PapE_DKD_mod_72 71
PapE_DKD_mod_52 146
PapE_DKD_mod_67 18
PapE_DKD_mod_63 18
PapE_DKD_mod_35 58
PapE_DKD_mod_71 66
PapE_DKD_mod_170 23
PapE_DKD_mod_48 18
PapE_DKD_mod_28 33
PapE_DKD_mod_160 15
PapE_DKD_mod_4 30
PapE_DKD_mod_80 35
PapE_DKD_mod_124 42
PapE_DKD_mod_173 12
PapE_DKD_mod_31 68
PapE_DKD_mod_21 36
PapE_DKD_mod_37 16
PapE_DKD_mod_143 12
PapE_DKD_mod_101 22
PapE_DKD_mod_90 26
PapE_DKD_mod_40 16
PapE_DKD_mod_182 19
PapE_DKD_mod_23 22
PapE_DKD_mod_114 17
PapE_DKD_mod_99 25
PapE_DKD_mod_79 22
PapE_DKD_mod_7 50
PapE_DKD_mod_176 15
PapE_DKD_mod_152 18
PapE_DKD_mod_26 26
PapE_DKD_mod_178 15
PapE_DKD_mod_49 39
PapE_DKD_mod_70 22
PapE_DKD_mod_106 27
PapE_DKD_mod_82 27
PapE_DKD_mod_85 20
PapE_DKD_mod_132 30
PapE_DKD_mod_112 17
PapE_DKD_mod_193 29
PapE_DKD_mod_47 29
PapE_DKD_mod_172 16
PapE_DKD_mod_156 14
PapE_DKD_mod_117 20
PapE_DKD_mod_134 37
PapE_DKD_mod_36 10
PapE_DKD_mod_203 21
PapE_DKD_mod_174 16
PapE_DKD_mod_38 25
PapE_DKD_mod_140 27
PapE_DKD_mod_17 21
PapE_DKD_mod_186 20
PapE_DKD_mod_5 32
PapE_DKD_mod_129 16
PapE_DKD_mod_213 20
PapE_DKD_mod_53 11
PapE_DKD_mod_59 19
PapE_DKD_mod_0 27
PapE_DKD_mod_192 14
PapE_DKD_mod_8 29
PapE_DKD_mod_149 29
PapE_DKD_mod_43 42
PapE_DKD_mod_130 13
PapE_DKD_mod_42 24
PapE_DKD_mod_155 36
PapE_DKD_mod_89 28
PapE_DKD_mod_107 22
PapE_DKD_mod_65 14
PapE_DKD_mod_119 29
PapE_DKD_mod_9 24
PapE_DKD_mod_191 21
PapE_DKD_mod_64 19
PapE_DKD_mod_204 32
PapE_DKD_mod_212 21
PapE_DKD_mod_24 16
PapE_DKD_mod_138 23
PapE_DKD_mod_33 25
PapE_DKD_mod_91 21
PapE_DKD_mod_86 21
PapE_DKD_mod_197 17
PapE_DKD_mod_184 13
PapE_DKD_mod_166 28
PapE_DKD_mod_96 34
PapE_DKD_mod_39 23
PapE_DKD_mod_188 18
PapE_DKD_mod_162 12
PapE_DKD_mod_145 15
PapE_DKD_mod_75 39
PapE_DKD_mod_147 24
PapE_DKD_mod_19 18
PapE_DKD_mod_100 74
PapE_DKD_mod_158 36
PapE_DKD_mod_94 18
PapE_DKD_mod_105 19
PapE_DKD_mod_148 12
PapE_DKD_mod_16 25
PapE_DKD_mod_169 15
PapE_DKD_mod_14 26
PapE_DKD_mod_55 15
PapE_DKD_mod_151 25
PapE_DKD_mod_198 13
PapE_DKD_mod_98 15
PapE_DKD_mod_87 16
PapE_DKD_mod_60 11
PapE_DKD_mod_6 22
PapE_DKD_mod_150 47
PapE_DKD_mod_25 24
PapE_DKD_mod_77 32
PapE_DKD_mod_74 14
PapE_DKD_mod_181 15
PapE_DKD_mod_133 20
PapE_DKD_mod_224 15
PapE_DKD_mod_113 18
PapE_DKD_mod_22 61
PapE_DKD_mod_202 16
PapE_DKD_mod_13 17
PapE_DKD_mod_15 20
PapE_DKD_mod_78 10
PapE_DKD_mod_208 11
Number of saved communities: 69
Analysing PapE_H_CKD
Identified communities
Number of communities: 224
Average community size 3.450892857142857
Median community size 1.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 1. 1. 3. 48.]
Significant communities
Number of communities: 3
Average community size 21.666666666666668
Median community size 11.0
Quantile (0,0.25,0.5,0.75,1) community size [11. 11. 11. 27. 43.]
PapE_H_CKD_mod_49 11
PapE_H_CKD_mod_27 11
PapE_H_CKD_mod_6 43
Number of saved communities: 0
TAL
{'TAL_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'TAL_COV_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'TAL_DKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'TAL_H_CKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing TAL_AKI
Identified communities
Number of communities: 289
Average community size 23.387543252595155
Median community size 16.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 2. 16. 32. 229.]
Significant communities
Number of communities: 21
Average community size 42.285714285714285
Median community size 27.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 17. 27. 39. 229.]
TAL_AKI_mod_40 37
TAL_AKI_mod_80 103
TAL_AKI_mod_4 229
TAL_AKI_mod_152 20
TAL_AKI_mod_89 27
TAL_AKI_mod_145 12
TAL_AKI_mod_138 33
TAL_AKI_mod_94 37
TAL_AKI_mod_60 25
TAL_AKI_mod_207 19
TAL_AKI_mod_177 10
TAL_AKI_mod_129 15
TAL_AKI_mod_27 22
TAL_AKI_mod_32 60
TAL_AKI_mod_118 39
TAL_AKI_mod_134 10
TAL_AKI_mod_148 17
TAL_AKI_mod_124 70
TAL_AKI_mod_106 54
TAL_AKI_mod_131 16
TAL_AKI_mod_22 33
Number of saved communities: 3
Analysing TAL_COV_AKI
Identified communities
Number of communities: 312
Average community size 29.628205128205128
Median community size 19.5
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 4. 19.5 39. 349. ]
Significant communities
Number of communities: 120
Average community size 47.708333333333336
Median community size 33.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 22. 33. 55.25 349. ]
TAL_COV_AKI_mod_241 10
TAL_COV_AKI_mod_57 28
TAL_COV_AKI_mod_93 19
TAL_COV_AKI_mod_0 349
TAL_COV_AKI_mod_59 11
TAL_COV_AKI_mod_49 27
TAL_COV_AKI_mod_9 214
TAL_COV_AKI_mod_75 124
TAL_COV_AKI_mod_121 110
TAL_COV_AKI_mod_44 22
TAL_COV_AKI_mod_139 20
TAL_COV_AKI_mod_52 95
TAL_COV_AKI_mod_144 20
TAL_COV_AKI_mod_109 19
TAL_COV_AKI_mod_97 24
TAL_COV_AKI_mod_224 13
TAL_COV_AKI_mod_236 18
TAL_COV_AKI_mod_136 70
TAL_COV_AKI_mod_250 23
TAL_COV_AKI_mod_66 42
TAL_COV_AKI_mod_100 26
TAL_COV_AKI_mod_47 42
TAL_COV_AKI_mod_63 24
TAL_COV_AKI_mod_64 56
TAL_COV_AKI_mod_146 26
TAL_COV_AKI_mod_99 56
TAL_COV_AKI_mod_21 268
TAL_COV_AKI_mod_112 25
TAL_COV_AKI_mod_15 55
TAL_COV_AKI_mod_35 20
TAL_COV_AKI_mod_14 61
TAL_COV_AKI_mod_19 41
TAL_COV_AKI_mod_62 67
TAL_COV_AKI_mod_39 82
TAL_COV_AKI_mod_84 34
TAL_COV_AKI_mod_160 70
TAL_COV_AKI_mod_126 39
TAL_COV_AKI_mod_143 47
TAL_COV_AKI_mod_113 45
TAL_COV_AKI_mod_159 27
TAL_COV_AKI_mod_51 20
TAL_COV_AKI_mod_220 20
TAL_COV_AKI_mod_27 104
TAL_COV_AKI_mod_106 32
TAL_COV_AKI_mod_53 43
TAL_COV_AKI_mod_186 53
TAL_COV_AKI_mod_80 25
TAL_COV_AKI_mod_45 37
TAL_COV_AKI_mod_29 41
TAL_COV_AKI_mod_169 29
TAL_COV_AKI_mod_147 36
TAL_COV_AKI_mod_60 25
TAL_COV_AKI_mod_162 38
TAL_COV_AKI_mod_82 34
TAL_COV_AKI_mod_148 11
TAL_COV_AKI_mod_56 32
TAL_COV_AKI_mod_154 42
TAL_COV_AKI_mod_13 55
TAL_COV_AKI_mod_193 11
TAL_COV_AKI_mod_18 115
TAL_COV_AKI_mod_108 13
TAL_COV_AKI_mod_120 44
TAL_COV_AKI_mod_91 33
TAL_COV_AKI_mod_153 31
TAL_COV_AKI_mod_72 64
TAL_COV_AKI_mod_145 37
TAL_COV_AKI_mod_214 24
TAL_COV_AKI_mod_149 30
TAL_COV_AKI_mod_3 100
TAL_COV_AKI_mod_88 37
TAL_COV_AKI_mod_1 22
TAL_COV_AKI_mod_150 35
TAL_COV_AKI_mod_48 103
TAL_COV_AKI_mod_175 21
TAL_COV_AKI_mod_46 38
TAL_COV_AKI_mod_195 45
TAL_COV_AKI_mod_96 26
TAL_COV_AKI_mod_33 79
TAL_COV_AKI_mod_198 21
TAL_COV_AKI_mod_83 68
TAL_COV_AKI_mod_212 19
TAL_COV_AKI_mod_251 17
TAL_COV_AKI_mod_54 21
TAL_COV_AKI_mod_167 66
TAL_COV_AKI_mod_185 20
TAL_COV_AKI_mod_174 23
TAL_COV_AKI_mod_81 29
TAL_COV_AKI_mod_77 35
TAL_COV_AKI_mod_164 16
TAL_COV_AKI_mod_58 36
TAL_COV_AKI_mod_61 77
TAL_COV_AKI_mod_7 18
TAL_COV_AKI_mod_105 79
TAL_COV_AKI_mod_30 29
TAL_COV_AKI_mod_199 33
TAL_COV_AKI_mod_76 42
TAL_COV_AKI_mod_114 76
TAL_COV_AKI_mod_152 23
TAL_COV_AKI_mod_4 140
TAL_COV_AKI_mod_217 31
TAL_COV_AKI_mod_190 21
TAL_COV_AKI_mod_135 22
TAL_COV_AKI_mod_40 23
TAL_COV_AKI_mod_107 19
TAL_COV_AKI_mod_38 150
TAL_COV_AKI_mod_34 21
TAL_COV_AKI_mod_124 26
TAL_COV_AKI_mod_133 100
TAL_COV_AKI_mod_20 55
TAL_COV_AKI_mod_155 30
TAL_COV_AKI_mod_5 90
TAL_COV_AKI_mod_140 10
TAL_COV_AKI_mod_117 60
TAL_COV_AKI_mod_123 67
TAL_COV_AKI_mod_138 29
TAL_COV_AKI_mod_37 36
TAL_COV_AKI_mod_31 40
TAL_COV_AKI_mod_200 16
TAL_COV_AKI_mod_2 22
TAL_COV_AKI_mod_137 15
Number of saved communities: 53
Analysing TAL_DKD
Identified communities
Number of communities: 278
Average community size 14.226618705035971
Median community size 3.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 1. 3. 18. 539.]
Significant communities
Number of communities: 9
Average community size 24.88888888888889
Median community size 20.0
Quantile (0,0.25,0.5,0.75,1) community size [10. 18. 20. 35. 51.]
TAL_DKD_mod_26 51
TAL_DKD_mod_30 11
TAL_DKD_mod_108 18
TAL_DKD_mod_60 20
TAL_DKD_mod_71 10
TAL_DKD_mod_128 23
TAL_DKD_mod_37 35
TAL_DKD_mod_48 20
TAL_DKD_mod_81 36
Number of saved communities: 0
Analysing TAL_H_CKD
Identified communities
Number of communities: 224
Average community size 4.339285714285714
Median community size 2.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 2. 2. 5. 41.]
Significant communities
Number of communities: 2
Average community size 24.0
Median community size 24.0
Quantile (0,0.25,0.5,0.75,1) community size [16. 20. 24. 28. 32.]
TAL_H_CKD_mod_108 32
TAL_H_CKD_mod_64 16
Number of saved communities: 0
VSM-P
{'VSM-P_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'VSM-P_COV_AKI': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'VSM-P_DKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'VSM-P_H_CKD': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing VSM-P_AKI
Identified communities
Number of communities: 248
Average community size 13.346774193548388
Median community size 9.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 2. 9. 19.25 222. ]
Significant communities
Number of communities: 19
Average community size 33.21052631578947
Median community size 16.0
Quantile (0,0.25,0.5,0.75,1) community size [ 11. 14.5 16. 31. 222. ]
VSM-P_AKI_mod_65 37
VSM-P_AKI_mod_104 11
VSM-P_AKI_mod_16 15
VSM-P_AKI_mod_23 15
VSM-P_AKI_mod_63 15
VSM-P_AKI_mod_19 22
VSM-P_AKI_mod_44 26
VSM-P_AKI_mod_75 55
VSM-P_AKI_mod_163 12
VSM-P_AKI_mod_70 24
VSM-P_AKI_mod_127 27
VSM-P_AKI_mod_3 222
VSM-P_AKI_mod_30 16
VSM-P_AKI_mod_49 11
VSM-P_AKI_mod_119 14
VSM-P_AKI_mod_186 16
VSM-P_AKI_mod_0 46
VSM-P_AKI_mod_46 12
VSM-P_AKI_mod_124 35
Number of saved communities: 1
Analysing VSM-P_COV_AKI
Identified communities
Number of communities: 287
Average community size 24.536585365853657
Median community size 19.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 5.5 19. 33. 181. ]
Significant communities
Number of communities: 162
Average community size 34.23456790123457
Median community size 26.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10. 18. 26. 40. 181.]
VSM-P_COV_AKI_mod_250 22
VSM-P_COV_AKI_mod_106 12
VSM-P_COV_AKI_mod_123 31
VSM-P_COV_AKI_mod_79 181
VSM-P_COV_AKI_mod_117 26
VSM-P_COV_AKI_mod_60 42
VSM-P_COV_AKI_mod_0 60
VSM-P_COV_AKI_mod_1 154
VSM-P_COV_AKI_mod_184 26
VSM-P_COV_AKI_mod_20 25
VSM-P_COV_AKI_mod_159 55
VSM-P_COV_AKI_mod_102 20
VSM-P_COV_AKI_mod_37 38
VSM-P_COV_AKI_mod_115 15
VSM-P_COV_AKI_mod_70 14
VSM-P_COV_AKI_mod_80 26
VSM-P_COV_AKI_mod_15 165
VSM-P_COV_AKI_mod_248 16
VSM-P_COV_AKI_mod_127 16
VSM-P_COV_AKI_mod_86 42
VSM-P_COV_AKI_mod_112 23
VSM-P_COV_AKI_mod_34 18
VSM-P_COV_AKI_mod_87 58
VSM-P_COV_AKI_mod_108 33
VSM-P_COV_AKI_mod_143 17
VSM-P_COV_AKI_mod_228 20
VSM-P_COV_AKI_mod_25 29
VSM-P_COV_AKI_mod_247 15
VSM-P_COV_AKI_mod_107 26
VSM-P_COV_AKI_mod_103 39
VSM-P_COV_AKI_mod_253 17
VSM-P_COV_AKI_mod_141 26
VSM-P_COV_AKI_mod_126 17
VSM-P_COV_AKI_mod_51 46
VSM-P_COV_AKI_mod_244 12
VSM-P_COV_AKI_mod_98 12
VSM-P_COV_AKI_mod_254 10
VSM-P_COV_AKI_mod_94 15
VSM-P_COV_AKI_mod_8 55
VSM-P_COV_AKI_mod_109 23
VSM-P_COV_AKI_mod_101 51
VSM-P_COV_AKI_mod_27 154
VSM-P_COV_AKI_mod_201 17
VSM-P_COV_AKI_mod_41 24
VSM-P_COV_AKI_mod_168 12
VSM-P_COV_AKI_mod_133 33
VSM-P_COV_AKI_mod_69 40
VSM-P_COV_AKI_mod_19 94
VSM-P_COV_AKI_mod_32 33
VSM-P_COV_AKI_mod_59 40
VSM-P_COV_AKI_mod_181 26
VSM-P_COV_AKI_mod_111 23
VSM-P_COV_AKI_mod_197 25
VSM-P_COV_AKI_mod_40 26
VSM-P_COV_AKI_mod_138 11
VSM-P_COV_AKI_mod_89 31
VSM-P_COV_AKI_mod_169 14
VSM-P_COV_AKI_mod_241 39
VSM-P_COV_AKI_mod_55 17
VSM-P_COV_AKI_mod_166 10
VSM-P_COV_AKI_mod_68 22
VSM-P_COV_AKI_mod_30 35
VSM-P_COV_AKI_mod_29 49
VSM-P_COV_AKI_mod_63 35
VSM-P_COV_AKI_mod_205 36
VSM-P_COV_AKI_mod_71 31
VSM-P_COV_AKI_mod_62 33
VSM-P_COV_AKI_mod_116 26
VSM-P_COV_AKI_mod_56 113
VSM-P_COV_AKI_mod_200 21
VSM-P_COV_AKI_mod_221 33
VSM-P_COV_AKI_mod_173 32
VSM-P_COV_AKI_mod_64 12
VSM-P_COV_AKI_mod_237 19
VSM-P_COV_AKI_mod_193 59
VSM-P_COV_AKI_mod_78 38
VSM-P_COV_AKI_mod_211 18
VSM-P_COV_AKI_mod_151 32
VSM-P_COV_AKI_mod_263 27
VSM-P_COV_AKI_mod_212 17
VSM-P_COV_AKI_mod_75 35
VSM-P_COV_AKI_mod_148 43
VSM-P_COV_AKI_mod_124 14
VSM-P_COV_AKI_mod_232 10
VSM-P_COV_AKI_mod_90 19
VSM-P_COV_AKI_mod_66 66
VSM-P_COV_AKI_mod_156 18
VSM-P_COV_AKI_mod_161 27
VSM-P_COV_AKI_mod_36 53
VSM-P_COV_AKI_mod_24 57
VSM-P_COV_AKI_mod_165 21
VSM-P_COV_AKI_mod_76 20
VSM-P_COV_AKI_mod_130 40
VSM-P_COV_AKI_mod_135 25
VSM-P_COV_AKI_mod_92 17
VSM-P_COV_AKI_mod_129 25
VSM-P_COV_AKI_mod_38 52
VSM-P_COV_AKI_mod_172 60
VSM-P_COV_AKI_mod_136 18
VSM-P_COV_AKI_mod_235 12
VSM-P_COV_AKI_mod_61 10
VSM-P_COV_AKI_mod_91 14
VSM-P_COV_AKI_mod_5 35
VSM-P_COV_AKI_mod_251 24
VSM-P_COV_AKI_mod_81 18
VSM-P_COV_AKI_mod_113 25
VSM-P_COV_AKI_mod_50 62
VSM-P_COV_AKI_mod_198 12
VSM-P_COV_AKI_mod_43 22
VSM-P_COV_AKI_mod_105 64
VSM-P_COV_AKI_mod_77 52
VSM-P_COV_AKI_mod_31 33
VSM-P_COV_AKI_mod_188 10
VSM-P_COV_AKI_mod_167 11
VSM-P_COV_AKI_mod_26 16
VSM-P_COV_AKI_mod_57 40
VSM-P_COV_AKI_mod_42 46
VSM-P_COV_AKI_mod_120 23
VSM-P_COV_AKI_mod_192 37
VSM-P_COV_AKI_mod_175 13
VSM-P_COV_AKI_mod_85 24
VSM-P_COV_AKI_mod_3 34
VSM-P_COV_AKI_mod_202 16
VSM-P_COV_AKI_mod_236 10
VSM-P_COV_AKI_mod_35 83
VSM-P_COV_AKI_mod_176 20
VSM-P_COV_AKI_mod_171 20
VSM-P_COV_AKI_mod_153 54
VSM-P_COV_AKI_mod_147 22
VSM-P_COV_AKI_mod_110 25
VSM-P_COV_AKI_mod_215 25
VSM-P_COV_AKI_mod_14 27
VSM-P_COV_AKI_mod_132 54
VSM-P_COV_AKI_mod_234 27
VSM-P_COV_AKI_mod_182 18
VSM-P_COV_AKI_mod_84 34
VSM-P_COV_AKI_mod_121 43
VSM-P_COV_AKI_mod_83 23
VSM-P_COV_AKI_mod_99 17
VSM-P_COV_AKI_mod_23 67
VSM-P_COV_AKI_mod_177 16
VSM-P_COV_AKI_mod_46 50
VSM-P_COV_AKI_mod_10 27
VSM-P_COV_AKI_mod_39 34
VSM-P_COV_AKI_mod_100 30
VSM-P_COV_AKI_mod_196 13
VSM-P_COV_AKI_mod_73 28
VSM-P_COV_AKI_mod_48 52
VSM-P_COV_AKI_mod_12 67
VSM-P_COV_AKI_mod_137 48
VSM-P_COV_AKI_mod_185 21
VSM-P_COV_AKI_mod_154 31
VSM-P_COV_AKI_mod_44 27
VSM-P_COV_AKI_mod_82 56
VSM-P_COV_AKI_mod_125 17
VSM-P_COV_AKI_mod_54 30
VSM-P_COV_AKI_mod_180 32
VSM-P_COV_AKI_mod_7 110
VSM-P_COV_AKI_mod_67 44
VSM-P_COV_AKI_mod_53 23
VSM-P_COV_AKI_mod_131 15
VSM-P_COV_AKI_mod_65 28
Number of saved communities: 51
Analysing VSM-P_DKD
Identified communities
Number of communities: 242
Average community size 5.760330578512397
Median community size 3.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2. 2. 3. 7. 39.]
Significant communities
Number of communities: 2
Average community size 15.0
Median community size 15.0
Quantile (0,0.25,0.5,0.75,1) community size [10. 12.5 15. 17.5 20. ]
VSM-P_DKD_mod_30 20
VSM-P_DKD_mod_34 10
Number of saved communities: 0
Analysing VSM-P_H_CKD
Identified communities
Number of communities: 271
Average community size 8.29889298892989
Median community size 3.0
Quantile (0,0.25,0.5,0.75,1) community size [ 1. 2. 3. 13. 67.]
Significant communities
Number of communities: 5
Average community size 24.0
Median community size 23.0
Quantile (0,0.25,0.5,0.75,1) community size [13. 17. 23. 27. 40.]
VSM-P_H_CKD_mod_188 27
VSM-P_H_CKD_mod_48 17
VSM-P_H_CKD_mod_39 23
VSM-P_H_CKD_mod_138 13
VSM-P_H_CKD_mod_33 40
Number of saved communities: 1
[ ]:
[12]:
[(x, type(tlda.__dict__[x])) for x in tlda.__dict__]
[12]:
[('tldict', dict),
('sorted_zones', list),
('name_sep', str),
('cellgroupdata', mikg.kgraph.DefaultDict),
('fullKG', mikg.kgraph.KGraph),
('output_folder_formatter', str),
('recalc_warning', bool)]
[13]:
with open("kpmp_tlda.pickle", 'wb') as f:
pickle.dump(tlda, f)
[3]:
import pickle
with open("kpmp_tlda.pickle", 'rb') as f:
tlda = pickle.load(f)
[4]:
tlda
[4]:
<mikg.kgraph.TwoLevelDifferentialAnalysis at 0x7f4d7cdf1290>
[8]:
tlda.plot_module_comparisons(plot_communities=False)
Output directory diff_ATL/
diff_ATL//all_module_heatmap.png
diff_ATL//all_module_compare.png
diff_ATL//score_distribution.png
Output directory diff_CNT/
diff_CNT//all_module_heatmap.png
diff_CNT//all_module_compare.png
diff_CNT//score_distribution.png
Output directory diff_DCT/
diff_DCT//all_module_heatmap.png
diff_DCT//all_module_compare.png
diff_DCT//score_distribution.png
Output directory diff_DTL/
diff_DTL//all_module_heatmap.png
diff_DTL//all_module_compare.png
diff_DTL//score_distribution.png
Output directory diff_EC/
diff_EC//all_module_heatmap.png
diff_EC//all_module_compare.png
diff_EC//score_distribution.png
Output directory diff_FIB/
diff_FIB//all_module_heatmap.png
diff_FIB//all_module_compare.png
diff_FIB//score_distribution.png
Output directory diff_IC/
diff_IC//all_module_heatmap.png
diff_IC//all_module_compare.png
diff_IC//score_distribution.png
Output directory diff_IMM/
diff_IMM//all_module_heatmap.png
diff_IMM//all_module_compare.png
diff_IMM//score_distribution.png
Output directory diff_NEU/
diff_NEU//all_module_heatmap.png
diff_NEU//all_module_compare.png
diff_NEU//score_distribution.png
Output directory diff_PC/
diff_PC//all_module_heatmap.png
diff_PC//all_module_compare.png
diff_PC//score_distribution.png
Output directory diff_PEC/
diff_PEC//all_module_heatmap.png
diff_PEC//all_module_compare.png
diff_PEC//score_distribution.png
Output directory diff_POD/
diff_POD//all_module_heatmap.png
diff_POD//all_module_compare.png
diff_POD//score_distribution.png
Output directory diff_PT/
diff_PT//all_module_heatmap.png
diff_PT//all_module_compare.png
diff_PT//score_distribution.png
Output directory diff_PapE/
diff_PapE//all_module_heatmap.png
diff_PapE//all_module_compare.png
diff_PapE//score_distribution.png
Output directory diff_TAL/
diff_TAL//all_module_heatmap.png
diff_TAL//all_module_compare.png
diff_TAL//score_distribution.png
Output directory diff_VSM-P/
diff_VSM-P//all_module_heatmap.png
diff_VSM-P//all_module_compare.png
diff_VSM-P//score_distribution.png
[18]:
ct=CommunityTool()
ct.compare_modules(tlda.communities, figsize=(75,75))
[7]:
descrDF = tlda.describe_modules()
descrDF.to_csv("diff_modules_description.tsv", sep="\t")
descrDF
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.
return _methods._mean(a, axis=axis, dtype=dtype,
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
ret = ret.dtype.type(ret / rcount)
[7]:
| name | gene_nodes | geneset_nodes | disease_nodes | drug_nodes | ncRNA_nodes | TF_nodes | other_nodes | COV_AKI_score_median | COV_AKI_score_mean | ... | AKI_absmean-disease | AKI_absmean-drug | AKI_diffmean-gene | AKI_diffmean-disease | AKI_diffmean-drug | base_condition | base_zone | base_condition_score_mean | base_condition_score_median | cellgroup | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ATL_AKI_mod_152 | [AFAP1L1, ANKLE2, ARHGAP39, ATE1, ATM, ATP10A,... | [] | [(Orphanet:98688, Oculomotor apraxia or relate... | [] | [] | [TIGD4, ZBTB44, ZNF180, ZNF428, ZSCAN22] | {} | -1.271686 | -1.170504 | ... | 0.625000 | NaN | 0.766226 | 0.242436 | NaN | ATL_AKI | AKI | 1.138040 | 0.959128 | ATL |
| 1 | ATL_AKI_mod_179 | [GPAA1, MIEN1, MIS12, PGAP2, PGAP3, PIGA, PIGF... | [(GO:0016254, preassembly of GPI anchor in ER ... | [] | [] | [] | [] | {} | -0.194393 | -0.384939 | ... | NaN | NaN | 0.618139 | NaN | NaN | ATL_AKI | AKI | 1.081111 | 0.998651 | ATL |
| 2 | ATL_AKI_mod_33 | [INTS1, INTS10, INTS12, INTS2, INTS3, INTS4, I... | [(GO:0016180, snRNA processing), (GO:0032039, ... | [] | [] | [] | [ZNF609] | {} | -1.213416 | -1.125034 | ... | NaN | NaN | 0.458746 | NaN | NaN | ATL_AKI | AKI | 0.869389 | 0.831690 | ATL |
| 3 | ATL_AKI_mod_186 | [ADCK1, AKAP10, ANKRD27, APPL2, ARL1, CCZ1B, C... | [(KEGG_MEDICUS_REFERENCE_EARLY_ENDOSOMAL_FUSIO... | [] | [] | [mir-211, mir-338] | [EEA1, SGSM2] | {} | -0.236979 | -0.288106 | ... | NaN | NaN | 0.432838 | NaN | NaN | ATL_AKI | AKI | 0.801803 | 0.710738 | ATL |
| 4 | ATL_AKI_mod_78 | [CCP110, CEP104, CEP120, CEP135, CEP152, CEP19... | [(R-HSA-5624958, ARL13B-mediated ciliary traff... | [] | [] | [] | [RARB] | {} | -0.490829 | -0.409524 | ... | NaN | NaN | 0.482350 | NaN | NaN | ATL_AKI | AKI | 0.813355 | 0.784658 | ATL |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1376 | VSM-P_COV_AKI_mod_185 | [ALDH1L1, ALDH1L2, ATIC, CBR1, DHFR, DHPS, FPG... | [(GO:0004505, phenylalanine 4-monooxygenase ac... | [] | [] | [mir-141, mir-192, mir-193b, mir-215, mir-24, ... | [TFDP2] | {} | 0.289290 | 0.612162 | ... | NaN | NaN | -0.163368 | NaN | NaN | VSM-P_COV_AKI | COV_AKI | 0.612162 | 0.289290 | VSM-P |
| 1377 | VSM-P_COV_AKI_mod_154 | [AHCYL1, AHCYL2, BHMT, BHMT2, CA12, CA2, CA4, ... | [] | [(EFO:1002002, high altitude pulmonary edema)] | [(CHEMBL95889, BETAINE)] | [mir-1, mir-185] | [HOXB13, KAT7] | {} | 0.666520 | 0.969040 | ... | 0.097222 | 0.156935 | -0.075041 | 0.064819 | -0.284978 | VSM-P_COV_AKI | COV_AKI | 0.969040 | 0.666520 | VSM-P |
| 1378 | VSM-P_COV_AKI_mod_82 | [A2M, ADAM7, ADM, APOH, BET1, C1QB, C1R, C1RL,... | [(KEGG_MEDICUS_REFERENCE_REGULATION_OF_FIBRINO... | [(Orphanet:91378, Hereditary angioedema)] | [(CHEMBL2109065, DROTRECOGIN ALFA (ACTIVATED))... | [mir-124, mir-125a, mir-1291, mir-139, mir-192... | [FOSL2, HNF4G] | {} | 0.348539 | 0.638841 | ... | 0.059163 | 0.107618 | -0.081058 | 0.135694 | -0.137909 | VSM-P_COV_AKI | COV_AKI | 0.638841 | 0.348539 | VSM-P |
| 1379 | VSM-P_COV_AKI_mod_67 | [AMBRA1, AMOTL2, ATG10, ATG101, ATG12, ATG13, ... | [(GO:1903059, regulation of protein lipidation... | [] | [] | [BANCR, FLJ11812, mir-1226, mir-137, mir-192, ... | [TBP, TFEB] | {} | 0.274397 | 0.146005 | ... | NaN | NaN | -0.226934 | NaN | NaN | VSM-P_COV_AKI | COV_AKI | 0.146005 | 0.274397 | VSM-P |
| 1380 | VSM-P_H_CKD_mod_48 | [ABCA2, ATP1A1, ATP1A2, ATP1B1, ATP2A1, ATP2A3... | [(GO:1903408, positive regulation of P-type so... | [] | [] | [] | [] | {} | -0.285874 | -0.337383 | ... | NaN | NaN | -0.228250 | NaN | NaN | VSM-P_H_CKD | H_CKD | 0.666682 | 0.652779 | VSM-P |
1381 rows × 49 columns
[ ]:
[20]:
geneset_overlapDF = tlda.create_overlap_df("geneset")
geneset_overlapDF.head()
27643
[20]:
| celltype | module | geneset | geneset_size | geneset_name | overlap | jaccard | |
|---|---|---|---|---|---|---|---|
| 0 | ATL | ATL_AKI_mod_152 | GO:0000049 | 75 | tRNA binding | 0.026667 | 0.016667 |
| 1 | ATL | ATL_AKI_mod_152 | GO:0000077 | 35 | DNA damage checkpoint signaling | 0.028571 | 0.012346 |
| 2 | ATL | ATL_AKI_mod_152 | GO:0000120 | 1 | RNA polymerase I transcription regulator complex | 1.000000 | 0.021277 |
| 3 | ATL | ATL_AKI_mod_152 | GO:0000122 | 949 | negative regulation of transcription by RNA po... | 0.002107 | 0.002012 |
| 4 | ATL | ATL_AKI_mod_152 | GO:0000150 | 2 | DNA strand exchange activity | 0.500000 | 0.020833 |
[21]:
disease_overlapDF = tlda.create_overlap_df("disease")
disease_overlapDF.head()
7184
[21]:
| celltype | module | disease | disease_size | disease_name | overlap | jaccard | |
|---|---|---|---|---|---|---|---|
| 0 | ATL | ATL_AKI_mod_152 | GO:0051726 | 223 | regulation of cell cycle | 0.008969 | 0.007463 |
| 1 | ATL | ATL_AKI_mod_152 | EFO:0000305 | 155 | breast carcinoma | 0.006452 | 0.004975 |
| 2 | ATL | ATL_AKI_mod_152 | EFO:0000616 | 159 | neoplasm | 0.006289 | 0.004878 |
| 3 | ATL | ATL_AKI_mod_152 | EFO:0000673 | 104 | prostate adenocarcinoma | 0.009615 | 0.006667 |
| 4 | ATL | ATL_AKI_mod_152 | EFO:0000365 | 198 | colorectal adenocarcinoma | 0.005051 | 0.004098 |
[22]:
drug_overlapDF = tlda.create_overlap_df("drug")
drug_overlapDF.head()
3195
[22]:
| celltype | module | drug | drug_size | drug_name | overlap | jaccard | |
|---|---|---|---|---|---|---|---|
| 0 | ATL | ATL_AKI_mod_78 | CHEMBL628 | 25 | PENTOXIFYLLINE | 0.040000 | 0.017857 |
| 1 | ATL | ATL_AKI_mod_78 | CHEMBL38 | 3 | TRETINOIN | 0.333333 | 0.029412 |
| 2 | ATL | ATL_AKI_mod_78 | CHEMBL25202 | 2 | TAMIBAROTENE | 0.500000 | 0.030303 |
| 3 | ATL | ATL_AKI_mod_78 | CHEMBL932 | 24 | DIPYRIDAMOLE | 0.041667 | 0.018182 |
| 4 | ATL | ATL_AKI_mod_78 | CHEMBL547 | 3 | ISOTRETINOIN | 0.333333 | 0.029412 |
[43]:
geneset_overlapDF.to_csv("diff_modules_description_overlap_geneset.tsv", sep="\t")
disease_overlapDF.to_csv("diff_modules_description_overlap_disease.tsv", sep="\t")
drug_overlapDF.to_csv("diff_modules_description_overlap_drug.tsv", sep="\t")
[10]:
import scanpy as sc
adata = sc.read_h5ad("kpmp.h5ad")
celltype_col="subclass.l1"
condition_col = "myconditions"
adata.obs[condition_col] = pd.Categorical(adata.obs[condition_col], ordered=True, categories=[x for x in zoneSort])
adata.obs["ct_condition"] = adata.obs[celltype_col].astype(str) + "_" + adata.obs[condition_col].astype(str)
adata.obs["ct_condition"] = pd.Categorical(adata.obs["ct_condition"])
adata
[10]:
AnnData object with n_obs × n_vars = 200338 × 33826
obs: 'nCount_RNA', 'nFeature_RNA', 'mypatients', 'percent.er', 'percent.mt', 'degen.score', 'aEpi.score', 'aStr.score', 'cyc.score', 'matrisome.score', 'collagen.score', 'glycoprotein.score', 'proteoglycan.score', 'S.Score', 'G2M.Score', 'experiment', 'specimen', 'condition.long', 'condition.l1', 'myconditions', 'donor_id', 'region.l1', 'region.l2', 'percent.cortex', 'percent.medulla', 'sample_tissue_type', 'id', 'pagoda_k100_infomap_coembed', 'subclass.full', 'subclass.l3', 'subclass.l2', 'subclass.l1', 'state.l2', 'state', 'class', 'structure', 'disease_ontology_term_id', 'sex_ontology_term_id', 'development_stage_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'eGFR', 'BMI', 'diabetes_history', 'hypertension', 'tissue_ontology_term_id', 'organism_ontology_term_id', 'assay_ontology_term_id', 'cell_type_ontology_term_id', 'is_primary_data', 'suspension_type', 'tissue_type', 'mycelltypes', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid', 'mysubtypes', 'ct_condition'
var: 'index', 'feature_reference', 'feature_biotype', 'feature_length'
uns: 'cell_type_colors', 'citation', 'condition.l2_colors', 'disease_colors', 'schema_reference', 'schema_version', 'title'
obsm: 'X_umap'
[25]:
#tlda.describe_module_scrna(adata, celltype_col, condition_col, show_plot=False, plot_folder="module_plots")
[ ]:
[26]:
rel_modules = descrDF[descrDF.drug_nodes.apply(len) != 0].sort_values(["base_condition", "base_condition_score_mean"], ascending=[True, False]).groupby(['base_condition']).head(3)
rel_modules
[26]:
| name | gene_nodes | geneset_nodes | disease_nodes | drug_nodes | ncRNA_nodes | TF_nodes | other_nodes | COV_AKI_score_median | COV_AKI_score_mean | ... | AKI_absmean-gene | AKI_absmean-disease | AKI_absmean-drug | AKI_diffmean-gene | AKI_diffmean-disease | AKI_diffmean-drug | base_condition | base_zone | base_condition_score_mean | base_condition_score_median | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83 | ATL_AKI_mod_37 | [ABL1, AFDN, APH1A, APH1B, ARF6, ARHGEF15, ASC... | [(GO:0005003, ephrin receptor activity), (GO:0... | [] | [(CHEMBL24828, VANDETANIB)] | [mir-21, mir-449a] | [ASCL1, ELF2] | {} | -0.604414 | -0.846367 | ... | 0.423759 | NaN | 0.391197 | 0.362707 | NaN | -0.038987 | ATL_AKI | AKI | 1.017571 | 0.987462 |
| 21 | ATL_AKI_mod_49 | [C17orf75, CYP46A1, DHCR24, DHCR7, EBP, EMD, F... | [(GO:0033489, cholesterol biosynthetic process... | [] | [(CHEMBL400599, BENFLUOREX)] | [] | [FOXJ2, ZNF207] | {} | -0.462747 | -0.552465 | ... | 0.358073 | NaN | 0.336312 | 0.625048 | NaN | 0.310935 | ATL_AKI | AKI | 0.955707 | 0.868438 |
| 40 | ATL_AKI_mod_135 | [ARL15, ASCC1, BRD9, CDAN1, DLD, EEF1D, HACD1,... | [(GO:0004739, pyruvate dehydrogenase (acetyl-t... | [(Orphanet:765, Pyruvate dehydrogenase deficie... | [(CHEMBL306823, SODIUM DICHLOROACETATE)] | [mir-188] | [HIC2, PPARD] | {} | -0.633255 | -0.835087 | ... | 0.425000 | 0.341667 | 0.252376 | 0.440530 | 1.049338 | -0.247618 | ATL_AKI | AKI | 0.904060 | 0.967565 |
| 103 | ATL_COV_AKI_mod_131 | [AFDN, CD226, IGHM, NECTIN1, NECTIN2, NECTIN3,... | [(R-HSA-420597, Nectin/Necl trans heterodimer... | [] | [(CHEMBL3301589, ENFORTUMAB VEDOTIN)] | [] | [] | {} | 1.051280 | 1.061415 | ... | 1.024306 | NaN | 0.229776 | 0.125230 | NaN | -0.007867 | ATL_COV_AKI | COV_AKI | 1.061415 | 1.051280 |
| 108 | ATL_COV_AKI_mod_200 | [ARNT, CRK, CTNNA1, CTNND1, CTSB, IGF1, IGFBP5... | [(GO:0016942, insulin-like growth factor bindi... | [] | [(CHEMBL2109355, DUSIGITUMAB)] | [mir-101, mir-107, mir-124, mir-125b, mir-126,... | [ARNT] | {} | 0.687709 | 0.556524 | ... | 1.151042 | NaN | 0.177852 | -0.624171 | NaN | -0.106152 | ATL_COV_AKI | COV_AKI | 0.556524 | 0.687709 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1326 | TAL_COV_AKI_mod_117 | [ABCA2, ABCC2, ABCD4, AKR1A1, AKR1C3, AKR7A2, ... | [(GO:0004505, phenylalanine 4-monooxygenase ac... | [] | [(CHEMBL1762621, BARDOXOLONE METHYL)] | [mir-130a, mir-218, mir-30b] | [NFE2L2] | {} | 1.039465 | 1.120773 | ... | 0.290721 | NaN | 0.668442 | 0.329237 | NaN | -0.153278 | TAL_COV_AKI | COV_AKI | 1.120773 | 1.039465 |
| 1275 | TAL_COV_AKI_mod_144 | [ARHGEF28, KCND1, KCNIP2, KCNIP4, MAP2K4, MAP2... | [(GO:0055106, ubiquitin-protein transferase re... | [] | [(CHEMBL290352, CEP-1347), (CHEMBL3137331, DEF... | [mir-31] | [MLXIPL] | {} | 0.901856 | 0.994646 | ... | 1.738934 | NaN | 0.500149 | 0.111849 | NaN | -0.141042 | TAL_COV_AKI | COV_AKI | 0.994646 | 0.901856 |
| 1348 | VSM-P_COV_AKI_mod_193 | [ABCG1, ACAT2, ADCY10, AFM, APOA1, APOC3, ATF1... | [(GO:0015747, urate transport), (GO:0097744, r... | [(EFO:0004742, renal system measurement), (EFO... | [(CHEMBL4297938, LIFASTUZUMAB VEDOTIN), (CHEMB... | [mir-122, mir-192] | [ATF1, HNF1A, HNF1B, NFIC, ZNF202] | {} | 1.489556 | 1.555822 | ... | 0.172488 | 0.260382 | 0.084732 | 0.297555 | 0.795930 | -0.141913 | VSM-P_COV_AKI | COV_AKI | 1.555822 | 1.489556 |
| 1355 | VSM-P_COV_AKI_mod_76 | [ACP3, ALDH1A1, ALDH1A2, ALDH1A3, CD24, CD44, ... | [(GO:2000768, positive regulation of nephron t... | [] | [(CHEMBL3545262, SACITUZUMAB GOVITECAN)] | [mir-124, mir-125a, mir-140, mir-92b] | [HOXC11, MBD2] | {} | 0.903777 | 1.002462 | ... | 0.128199 | NaN | 0.169312 | -0.118094 | NaN | -0.788617 | VSM-P_COV_AKI | COV_AKI | 1.002462 | 0.903777 |
| 1338 | VSM-P_COV_AKI_mod_133 | [ARHGAP12, BBS9, BCL10, C10orf90, COBL, COIL, ... | [(GO:2000564, regulation of CD8-positive, alph... | [] | [(CHEMBL290352, CEP-1347)] | [mir-146a, mir-25] | [HMGA1] | {} | 1.078162 | 0.984051 | ... | 0.353025 | NaN | 0.114369 | -0.204549 | NaN | 0.090552 | VSM-P_COV_AKI | COV_AKI | 0.984051 | 1.078162 |
86 rows × 48 columns
[88]:
%%capture captured_describe_module
tlda.describe_module_scrna(adata, celltype_col, condition_col, module_names=list(rel_modules.name), show_plot=False, plot_folder="module_plots")
[89]:
captured_describe_module()
disease 7184
geneset 27643
drug 3195
['FAS', 'PSEN1', 'EPHA7', 'TRHDE', 'ABL1', 'SDCBP', 'EPHA1', 'YES1', 'EFNB2', 'BLK', 'AFDN', 'PTPRO', 'EFNA1', 'RASA1', 'PGPEP1', 'EPHB4', 'EPHA5', 'CTNND1', 'EPHA4', 'PLCE1', 'MRAS', 'TMED10', 'UBE3A', 'EPHB6', 'NCSTN', 'EPHB3', 'NRAS', 'DNER', 'RRAS', 'MYL12A', 'DCBLD1', 'EPHB2', 'PSEN2', 'KALRN', 'EVC2', 'CTR9', 'APH1B', 'ASCL1', 'ARHGEF15', 'ARF6', 'NUDT4B', 'EFNA5', 'APH1A', 'DOK3', 'EFNB1', 'ELF2', 'DTX1', 'mir-449a', 'mir-21']
40
Saving plot for module ATL_AKI_mod_37 : module_plots/overview_plot_ATL_AKI_mod_37.png
['SYNE4', 'MVP', 'LMNB2', 'SCGB1D2', 'DHCR7', 'TOR1AIP2', 'CYP46A1', 'SUN2', 'LEMD3', 'TJAP1', 'SYNE1', 'NPR3', 'TOR1A', 'ZNF207', 'LBR', 'LIPA', 'WDR11', 'C17orf75', 'MAJIN', 'TM7SF2', 'SC5D', 'VPS52', 'DHCR24', 'MSMO1', 'SOAT1', 'EBP', 'FAM91A1', 'EMD', 'FOXJ2', 'TOR1AIP1', 'LMNB1', 'SUN1']
40
Saving plot for module ATL_AKI_mod_49 : module_plots/overview_plot_ATL_AKI_mod_49.png
['PDK1', 'ARL15', 'PDK3', 'PDHA1', 'PDHA2', 'PDPR', 'TRIP4', 'HSD11B2', 'PDHX', 'CDAN1', 'PPTC7', 'HIC2', 'ASCC1', 'PDPK1', 'TBC1D4', 'PDK2', 'PDP2', 'PPARD', 'ITGB3', 'PDP1', 'BRD9', 'RAB11FIP5', 'PDHB', 'TSSK3', 'RAB10', 'DLD', 'YWHAH', 'ITGA7', 'HACD1', 'EEF1D', 'mir-188']
40
Saving plot for module ATL_AKI_mod_135 : module_plots/overview_plot_ATL_AKI_mod_135.png
['NECTIN3', 'PARD3', 'NECTIN4', 'AFDN', 'IGHM', 'NECTIN2', 'PTK2', 'PAPLN', 'PVR', 'NECTIN1', 'TNFRSF14', 'CD226']
40
Saving plot for module ATL_COV_AKI_mod_131 : module_plots/overview_plot_ATL_COV_AKI_mod_131.png
['ARNT', 'CTSB', 'CTNNA1', 'IGF1', 'IGFBP7', 'PAPPA', 'TACSTD2', 'IGFBP5', 'TBC1D4', 'IGFBP6', 'PDGFA', 'IL21', 'CRK', 'NCOR2', 'CTNND1', 'PAPPA2', 'mir-107', 'mir-221', 'mir-101', 'mir-214', 'mir-490', 'mir-125b', 'mir-140', 'mir-197', 'mir-211', 'mir-29b', 'mir-126', 'mir-124', 'mir-29c', 'mir-98', 'mir-409']
40
Saving plot for module ATL_COV_AKI_mod_200 : module_plots/overview_plot_ATL_COV_AKI_mod_200.png
['SLC39A10', 'SLC39A1', 'SLC39A13', 'SLC30A9', 'SLC39A8', 'SLC30A2', 'SLC30A7', 'SLC39A14', 'SLC30A5', 'SLC30A6', 'SLC39A9', 'SLC39A7', 'SLC39A6', 'ZNF20', 'SLC30A4', 'SLC39A11', 'SLC30A1']
40
Saving plot for module CNT_AKI_mod_254 : module_plots/overview_plot_CNT_AKI_mod_254.png
['ADAM19', 'IGFL3', 'ADAM12', 'NEAT1', 'WNK1', 'IGFBP2', 'LRMDA', 'IGFBP3', 'TP63', 'KLF12', 'IGF2', 'KCTD1', 'IGF1', 'STON2', 'PAPPA', 'ADAM21', 'IGFBP5', 'ADAM9', 'SH3PXD2A', 'IGFBP7', 'SYT2', 'IL21', 'TBC1D1', 'IGFBP6', 'PAPPA2', 'mir-27b', 'mir-17', 'mir-100', 'mir-615', 'mir-155', 'mir-24', 'MALAT1']
40
Saving plot for module CNT_AKI_mod_104 : module_plots/overview_plot_CNT_AKI_mod_104.png
['ZHX2', 'RIN1', 'DYRK1A', 'FEZ1', 'YWHAG', 'NEFL', 'CDC25C', 'CDC25B', 'CASP9', 'KCNK18', 'LIX1', 'MAP2K1', 'MARK3', 'RUSC1', 'ZHX3', 'TBC1D4', 'TROAP', 'YWHAE', 'YWHAQ', 'AKIRIN2', 'CBY1', 'AKIRIN1', 'MAP2K2', 'TFEB', 'NUTM2B', 'LNPEP', 'MST1', 'STK38', 'YWHAB', 'PIN1', 'ARAF', 'YWHAZ', 'GPATCH1', 'CSNK1G3', 'YWHAH', 'CSNK1G1', 'MAP3K2', 'KSR1', 'ATG9A', 'SFN', 'KIF1C', 'SDK1', 'WDR83']
40
Saving plot for module CNT_AKI_mod_182 : module_plots/overview_plot_CNT_AKI_mod_182.png
['RASGRF1', 'THAP12', 'METAP2', 'METAP1', 'EIF2AK2', 'AATF', 'EIF2A', 'CHEK2', 'PLAGL2', 'BAIAP2L1', 'MAPT', 'STRBP']
40
Saving plot for module CNT_COV_AKI_mod_109 : module_plots/overview_plot_CNT_COV_AKI_mod_109.png
['CMTM7', 'SH3BGRL', 'UBASH3B', 'PRAG1', 'PTPRU', 'EFEMP1', 'SCAMP1', 'CAV2', 'CMTM4', 'ZP3', 'ZNG1C', 'SH3BGRL3', 'TPBG', 'AAMP', 'PGRMC1', 'MRGPRX3', 'TRIP13', 'LRRK1', 'CMTM8', 'DOK5', 'DOK6', 'NUMBL', 'DOK4', 'CCDC50', 'SPINK1', 'ATP5F1C', 'ZGLP1', 'EGFR', 'CSE1L', 'SLC5A1', 'EHD4', 'EIF1B', 'IGHG1', 'CMTM6', 'CDCP1', 'SCAMP3', 'SGSM2', 'mir-338', 'mir-137']
40
Saving plot for module CNT_COV_AKI_mod_232 : module_plots/overview_plot_CNT_COV_AKI_mod_232.png
['CUX1', 'CCDC6', 'CD24', 'ABCG2', 'APOC2', 'TP63', 'BCL11B', 'CD2', 'ASPN', 'SATB2', 'NR4A2', 'MADCAM1', 'TBX6', 'HINFP', 'TFAP4', 'PROM1', 'ALDH8A1', 'PECAM1', 'ALCAM', 'LHCGR', 'EPCAM', 'NR2F6', 'SELP', 'ELK3', 'CD44', 'LAMA2', 'LGALS8', 'MEIS2', 'GPNMB', 'ALDH1A1', 'LGALS9', 'mir-211', 'mir-373', 'mir-204', 'mir-22']
40
Saving plot for module CNT_COV_AKI_mod_60 : module_plots/overview_plot_CNT_COV_AKI_mod_60.png
['PSME3', 'UBA1', 'UCHL1', 'USP13', 'IREB2', 'SH3KBP1', 'PSMD14', 'RNF13', 'USP4', 'MTERF3', 'RFFL', 'USP33', 'PALB2', 'TOP3A', 'NGLY1', 'PSMB1', 'ASXL2', 'ATXN3', 'POLI', 'RNF8', 'BAP1', 'GYS1', 'PSMB9', 'USP28', 'ADRM1', 'MBD6', 'ERCC6', 'MYSM1', 'SH3GLB1', 'SOCS6', 'RAD23A', 'RPS27A', 'TRIM56', 'TRIM25', 'PRICKLE1', 'MFN2', 'DDX41', 'RNF38', 'PSMB2', 'CCDC8', 'UBXN1', 'KLHL12', 'OBSL1', 'PSMB5', 'RNF41', 'LIG1', 'KLF5', 'SH3GLB2', 'CHMP5', 'PSMC3', 'JMY', 'ASXL1', 'PSMD12', 'PLAA', 'DNA2', 'PSMC6', 'PSMC5', 'PSMD8', 'PSMF1', 'SNCA', 'RMI1', 'HUWE1', 'UBB', 'MDC1', 'TRIM23', 'mir-320a', 'mir-7']
40
Saving plot for module CNT_H_CKD_mod_101 : module_plots/overview_plot_CNT_H_CKD_mod_101.png
['PKM', 'ASRGL1', 'PDK3', 'PPCS', 'INSIG2', 'MDH2', 'FBP1', 'RXRA', 'PDPR', 'ASPA', 'MED1', 'ZNF746', 'RIMKLB', 'NCOA3', 'ATF6', 'MED13', 'MED17', 'MED20', 'AFMID', 'GOT1', 'RARA', 'MED25', 'CREB3L3', 'ACSS2', 'MED28', 'CDK19', 'MED12', 'MED21', 'NAT8L', 'CRTC2', 'MED9', 'PC', 'MED10', 'MED23', 'ALDH5A1', 'MED6', 'GOT2', 'FAM120B', 'ATF2', 'ESRRG', 'THRAP3', 'MED8', 'MED27', 'PCK1', 'SIRT2', 'MED29', 'CRTC1', 'DLAT', 'FTCD', 'MED13L', 'CS', 'CDK8', 'HPD', 'PDHB', 'MED24', 'ACLY', 'MED22', 'MED30', 'PPCDC', 'DLD', 'MDH1', 'MED4', 'NIT2', 'HELZ2', 'MED19', 'MED16', 'ACSS1', 'CCNC', 'ME2', 'FBXW7', 'MED15', 'UBR5', 'mir-137', 'mir-17', 'mir-20b', 'mir-33a', 'mir-199a', 'mir-424', 'mir-503', 'mir-208a', 'mir-138', 'let-7c', 'mir-18a', 'mir-373', 'mir-147b', 'mir-205', 'mir-320a', 'mir-33b', 'mir-21', 'mir-7', 'mir-34a', 'mir-122', 'mir-101', 'mir-107', 'mir-195', 'mir-22']
40
Saving plot for module CNT_H_CKD_mod_51 : module_plots/overview_plot_CNT_H_CKD_mod_51.png
['RAB27B', 'CDH23', 'NOL4L', 'MCTP1', 'SLC22A1', 'GAS7', 'GMDS', 'ARHGAP25', 'GFRA2', 'TNFRSF18', 'NUCB2', 'HTR2C', 'MCTP2', 'DIS3L2', 'MIR17HG', 'ANKRD26', 'TIE1', 'MCF2L2', 'FAM107B', 'MAST4', 'ABO', 'DNAJB12', 'ALG1', 'PKIG', 'ARSG', 'KCTD6', 'PTP4A2', 'SLC22A4', 'SLCO3A1', 'TBC1D14', 'MSI1', 'TPRG1', 'CLEC16A', 'BAHCC1', 'CLNK', 'NOVA2', 'RUNX1', 'IGFBP3', 'ERP44', 'TNFSF8', 'BAALC', 'RGS6', 'GTDC1', 'MGRN1', 'RAI1', 'JCAD', 'CBX2', 'CD160', 'DEXI', 'SH3TC1', 'SERPINB13', 'CR1', 'NHERF1', 'CTLA4', 'LTB4R', 'MSI2', 'GMPPA', 'AKNA', 'TMEM163', 'SLX4IP', 'ERAP1', 'AMPD3', 'ESYT2', 'IER3', 'LIMCH1', 'CTNND2', 'INPP5A', 'SERINC5', 'RAP1GAP2', 'PCNX2', 'PTH', 'HOMER2', 'DLG5', 'FOXJ3', 'TNRC18', 'GSAP', 'DOCK5', 'SLC22A17']
40
Saving plot for module DCT_AKI_mod_44 : module_plots/overview_plot_DCT_AKI_mod_44.png
['HBB', 'LMLN', 'ARSA', 'NFYC', 'NQO1', 'RRM2', 'CYB5R4', 'HBA2', 'ADNP2', 'TFCP2', 'CYB5R3', 'BDKRB1', 'CYB5R1', 'GGCX', 'SMARCA1', 'NFE2L3', 'POU2F1', 'ETV6', 'PCBP3', 'POU2F3', 'NTHL1', 'SNAPC3', 'CHIC2', 'KLF11', 'VKORC1L1', 'NFE2L1', 'LY6E', 'SNAPC4', 'HBA1', 'ZNF362', 'LIN28B', 'MAFG', 'TRIM71', 'let-7g', 'mir-451a', 'mir-542', 'mir-141', 'mir-200a', 'mir-200b', 'mir-429']
40
Saving plot for module DCT_AKI_mod_14 : module_plots/overview_plot_DCT_AKI_mod_14.png
['GAS2', 'EIF4E3', 'DFFA', 'SCGB1A1', 'CASP4', 'MVP', 'SRP72', 'ANP32A', 'CASP9', 'BZW2', 'CASP7', 'DFFB', 'CASP6', 'MEF2B', 'MKNK1', 'EIF3A', 'PLS1', 'PARP3', 'PARP4', 'UACA', 'EIF4G2', 'PTBP2', 'PTMA', 'CIDEA', 'AVEN', 'APAF1', 'S100A13', 'ATG4D', 'KRT19', 'PARP10', 'CASP10', 'UBE4A', 'EIF4E2', 'CASP2', 'NMT2', 'ANP32B', 'PTMS', 'ACIN1', 'CASP3', 'EIF4G3', 'SERPINB3']
40
Saving plot for module DCT_AKI_mod_134 : module_plots/overview_plot_DCT_AKI_mod_134.png
['KAAG1', 'CUX1', 'CCDC6', 'ERVW-1', 'DCX', 'ACOT13', 'DCDC2', 'RBM18', 'KIF5B', 'EML4', 'BCL11B', 'LRRFIP2', 'SATB2', 'DYRK4', 'TOP1', 'SLC1A2', 'ALK', 'GCFC2', 'KIAA0319', 'EML1', 'FLII', 'LRRFIP1', 'CGNL1', 'TPM3', 'TMOD2', 'TFG', 'HOXA2', 'KIR3DL2', 'MEIS2', 'TMOD3', 'mir-1']
40
Saving plot for module DCT_COV_AKI_mod_138 : module_plots/overview_plot_DCT_COV_AKI_mod_138.png
['PPP2R2B', 'FEZ1', 'YWHAG', 'PPP2R5B', 'NEFL', 'RIT2', 'CDC25B', 'KCNK18', 'CDC25C', 'LIX1', 'MLF1', 'MARK3', 'CCNQ', 'SIK3', 'TBC1D4', 'YWHAE', 'BRAF', 'PKN2', 'CRTC2', 'YWHAQ', 'TRIP13', 'CBY1', 'PPP3CC', 'MAP2K2', 'KLHL15', 'LNPEP', 'IKZF5', 'YWHAB', 'PIN1', 'YWHAZ', 'BAD', 'YWHAH', 'ANKRD7', 'MAP3K2', 'KSR1', 'SLC2A4', 'SFN', 'SDK1', 'mir-140', 'mir-1', 'mir-193b', 'mir-186', 'mir-223']
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Saving plot for module DCT_COV_AKI_mod_79 : module_plots/overview_plot_DCT_COV_AKI_mod_79.png
['LRRK2', 'CLCN4', 'TLR2', 'LARS1', 'RAB12', 'MAP3K11', 'MAPK8IP3', 'KIFAP3', 'HYAL3', 'TAOK2', 'MAP3K13', 'TAOK1', 'KIF3B', 'MAPK8IP1', 'MAP2K6', 'RAB29', 'MAP3K20', 'BOC', 'MAP3K10', 'KIF3A', 'RAB32', 'ATP13A2', 'EFHD2', 'KIF3C', 'MAP3K12', 'SPAG9', 'SH3RF1', 'ZNF33A', 'PTPA', 'MAP3K9', 'MAP2K4', 'MAP2K3', 'MYO5C', 'RAB38', 'MAP2K7', 'TAOK3', 'mir-10a', 'mir-31', 'mir-20a', 'mir-21', 'mir-214', 'mir-125b', 'mir-221', 'mir-222']
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Saving plot for module DCT_COV_AKI_mod_18 : module_plots/overview_plot_DCT_COV_AKI_mod_18.png
['GGACT', 'ATOX1', 'PPARGC1A', 'IQGAP1', 'ZFHX3', 'GLYAT', 'HBA2', 'CSAD', 'ATP7B', 'TXNDC12', 'GRSF1', 'SLC31A1', 'GGT1', 'ATP13A3', 'GPX8', 'GGCT', 'SOD3', 'GPX7', 'GPX4', 'GGT7', 'GSS', 'LAP3', 'DMTF1', 'GCLM', 'ANPEP', 'HMOX1', 'GSR', 'SREBF1', 'PRDX6', 'ETS2', 'HBA1', 'GPX3', 'GCLC', 'LGALS3', 'GGT6', 'PRXL2A']
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Saving plot for module DTL_AKI_mod_122 : module_plots/overview_plot_DTL_AKI_mod_122.png
['FAM8A1', 'VCP', 'OS9', 'UFD1', 'ATF6', 'SCARF1', 'DNAJB9', 'DERL2', 'ERLEC1', 'CANX', 'TMEM129', 'NSFL1C', 'UBXN7', 'HSP90B1', 'CNPY3', 'ATF6B', 'HSPA5', 'DDIT3', 'CREBRF', 'TAP1', 'P4HB', 'UGGT1', 'DNAJC1', 'RNF125', 'PDIA4', 'ERLIN1', 'ZBTB22', 'RNF170', 'SYVN1', 'SIL1', 'ERP27', 'UBXN2B', 'TMEM38B', 'SELENOS', 'L3MBTL1', 'UBXN1', 'CALR', 'PDIA3', 'KLHDC2', 'HERPUD1', 'LRRC40', 'DNAJC3', 'VCPKMT', 'CRTAP', 'CTAG1A', 'PTGES2', 'TAPBP', 'EIF2AK3', 'CTAG1B', 'SPPL2B', 'USP33', 'FAM104A', 'DNAJC10', 'UBXN6', 'AMFR', 'ERN1', 'XPO6', 'FAF2', 'AIMP1', 'EDEM3', 'UBAC2', 'FUNDC1', 'PPIB', 'UBXN2A', 'ERN2', 'CREB3', 'ERP29', 'ESAM', 'AUP1', 'ERAP1', 'UBXN4', 'PDIA6', 'NPLOC4', 'SVIP', 'RNF139', 'HYOU1', 'SPPL3', 'KLHDC1', 'SEL1L', 'HM13', 'SPPL2A', 'DERL1', 'EDEM1', 'mir-122']
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Saving plot for module DTL_AKI_mod_7 : module_plots/overview_plot_DTL_AKI_mod_7.png
['PYCARD', 'EIF4A3', 'MAPKAPK5', 'CNOT2', 'TMEM214', 'EIF4A1', 'RBM38', 'CPEB3', 'CNOT3', 'TNKS1BP1', 'CNOT11', 'EIF4G1', 'GRK5', 'CASP6', 'MKNK1', 'CNOT6L', 'EIF4A2', 'PABPC4', 'CNOT4', 'CNOT10', 'EXD2', 'EIF4G2', 'PTBP2', 'PABPC1L', 'C11orf68', 'TNFRSF11A', 'TOB1', 'PAN3', 'PABPC4L', 'PABPC1', 'CNOT6', 'CNOT1', 'DDX3X', 'EIF4B', 'PAIP1', 'MKRN1', 'RBM10', 'EIF4H', 'MKRN2', 'PABPC3', 'ZCCHC3', 'PAN2', 'EIF4E', 'EIF4E2', 'CNOT8', 'PDCD4', 'SEPTIN8', 'EIF4G3', 'PARN', 'CNOT7', 'RC3H1']
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Saving plot for module DTL_AKI_mod_48 : module_plots/overview_plot_DTL_AKI_mod_48.png
['MSRA', 'MASTL', 'PRDX5', 'PRDX1', 'ARPP19', 'MSRB3', 'TXN2', 'TXNIP', 'TNNI3K', 'GDPD5', 'MLX', 'QARS1', 'TXN', 'RPS6KC1', 'PRDX4', 'MSRB1', 'MIEN1', 'GLRX', 'MYT1L', 'SETD2', 'PRDX3', 'MLXIP', 'TNIK', 'MAP3K5', 'GPR37', 'MELK', 'MINK1', 'MSRB2', 'ZNF85', 'POU3F3', 'BLOC1S5-TXNDC5', 'ARRDC3']
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Saving plot for module DTL_COV_AKI_mod_135 : module_plots/overview_plot_DTL_COV_AKI_mod_135.png
['GIGYF2', 'PDK1', 'CLK2', 'ZFP36L2', 'GIGYF1', 'ZFP36', 'ZFP36L1', 'ZNF598', 'MAPKAPK2', 'CNOT9']
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Saving plot for module DTL_COV_AKI_mod_197 : module_plots/overview_plot_DTL_COV_AKI_mod_197.png
['HERPUD2', 'GGACT', 'HABP2', 'NOXO1', 'NCF1', 'NOXA1', 'VCAM1', 'THY1', 'PRIMPOL', 'FGA', 'ZKSCAN1', 'MADCAM1', 'PTPN1', 'PITRM1', 'ICAM2', 'NCF2', 'NOX4', 'PEBP1', 'FGB', 'ZSCAN21', 'CYBA', 'ZNF3', 'ZNF16', 'ZNF19', 'POLDIP2', 'mir-31', 'mir-15a', 'mir-16', 'mir-29a', 'mir-125b']
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Saving plot for module DTL_COV_AKI_mod_175 : module_plots/overview_plot_DTL_COV_AKI_mod_175.png
['SLC39A10', 'ANKRD16', 'BLMH', 'SP1', 'SLC30A9', 'ZNF555', 'KCTD13', 'ATP5MC1', 'SLC39A8', 'SLC40A1', 'GLRX5', 'SP4', 'MGST1', 'SLC39A14', 'MGST3', 'RNLS', 'CYBRD1', 'MSRB1', 'SLC25A42', 'SLC39A7', 'SKA2', 'CAPS2', 'PMEPA1', 'SLC39A6', 'SLC30A4', 'SLC11A2', 'P2RX4', 'ACLY', 'SLC30A1', 'CLDN19', 'ATP5F1B', 'NGDN', 'FECH', 'mir-488', 'mir-485', 'mir-19a']
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Saving plot for module DTL_DKD_mod_27 : module_plots/overview_plot_DTL_DKD_mod_27.png
['HSD17B10', 'MTR', 'ASRGL1', 'PDHA1', 'ALDH6A1', 'ASPA', 'TAF1', 'GATM', 'ASS1', 'ACSS2', 'SLC6A8', 'SHMT1', 'GADL1', 'ALDH2', 'ACAT1', 'PCCA', 'IDH2', 'LDHB', 'ACSS3', 'TMLHE', 'GOT2', 'ALDH3A2', 'CNDP2', 'GLUD1', 'ALDH7A1', 'ALDH1L1', 'HIBADH', 'PDP1', 'AGXT2', 'MTHFD1', 'ALDH9A1', 'FTCD', 'ABAT', 'DDAH1', 'DHODH', 'DHFR', 'MTHFD2L', 'BBOX1', 'GAMT', 'GLUL']
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Saving plot for module DTL_H_CKD_mod_51 : module_plots/overview_plot_DTL_H_CKD_mod_51.png
['SLC2A9', 'PDZK1IP1', 'ABCG2', 'SLC1A5', 'SLC16A9', 'SLC22A12', 'SLC18B1', 'GPM6A', 'SLC34A1', 'CLCN3', 'SLC15A2', 'SLC37A4', 'SLC5A8', 'SLC17A3', 'SH3PXD2B', 'PDZK1', 'CEBPD', 'TBX2', 'LRIG3', 'SLC15A4', 'SLC22A11', 'SLC17A5', 'SLC34A2', 'SLC17A1', 'SLC5A12', 'mir-1', 'mir-95']
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Saving plot for module FIB_COV_AKI_mod_224 : module_plots/overview_plot_FIB_COV_AKI_mod_224.png
['TNFRSF11B', 'CBFB', 'SYNM', 'PLAC1', 'MGP', 'KAT6B', 'AP4M1', 'FGF20', 'SERF1B', 'USF2', 'ITGA2B', 'SCX', 'IRF5', 'SERF2', 'CFH', 'PPIC', 'GLI1', 'MICU3', 'GGCX', 'CSF1', 'MYD88', 'BMP1', 'FGF10', 'FGFR4', 'ITGA10', 'USF1', 'SFRP2', 'ECM1', 'FGF18', 'FGF7', 'TNFSF11', 'FGF1', 'CFB', 'ITGA8', 'ITGA4', 'NOS2', 'FGF9', 'S1PR1', 'IRAK1', 'EMB', 'FGF11', 'CFHR3', 'SPP1', 'FGF17', 'ILK', 'MICU2', 'ETS2', 'ITGA7', 'HES1', 'NT5E', 'ELF4', 'RUNX3', 'mir-433', 'mir-200b', 'mir-22', 'mir-29b', 'mir-128', 'mir-130b', 'mir-148b', 'mir-152', 'mir-130a', 'mir-1207', 'mir-155', 'mir-203a', 'mir-21', 'mir-149', 'mir-204', 'mir-132', 'mir-148a', 'mir-483', 'mir-663a', 'mir-632', 'mir-564', 'mir-218', 'mir-29a', 'mir-106b', 'mir-18a', 'mir-210', 'mir-26a', 'mir-140', 'mir-125b', 'mir-17', 'mir-146b', 'mir-23a', 'mir-24', 'mir-145', 'mir-542', 'mir-188', 'mir-30a', 'mir-124']
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Saving plot for module FIB_COV_AKI_mod_51 : module_plots/overview_plot_FIB_COV_AKI_mod_51.png
['SLC22A7', 'CUX1', 'FAM83H', 'FOLR1', 'WDR72', 'PCBD1', 'PKD2', 'NR2F2', 'ENAM', 'SLC22A1', 'ABCA10', 'UMOD', 'WDR7', 'KCNK3', 'SLC22A2', 'PLXNC1', 'BCL11B', 'CBX5', 'SLC22A6', 'PKHD1L1', 'ABCA6', 'SPP2', 'TRIP12', 'NR2F1', 'HNF1A', 'B4GALNT2', 'SULF1', 'USP24', 'KRT18', 'HIVEP1', 'ABCA5', 'SLC22A5', 'SLC47A2', 'CLTRN', 'ABCA8', 'HNF1B', 'ABCA9', 'KIF12', 'MIB1', 'HSD3B2', 'ATF1', 'SULF2', 'SLC22A8', 'PKHD1', 'GIT1', 'ADCY10', 'HIVEP3', 'RAB8A', 'GSTA1', 'mir-125a', 'mir-149']
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Saving plot for module FIB_COV_AKI_mod_90 : module_plots/overview_plot_FIB_COV_AKI_mod_90.png
['PAPOLB', 'BHMT', 'CA10', 'BHMT2', 'CA2', 'TTK', 'DMGDH', 'SLC4A3', 'PRKX', 'CA11', 'MTRR', 'CA13', 'SLC4A4', 'AHCYL2', 'KAT7', 'SLC9A3', 'CA8', 'CA5B', 'CYP24A1', 'CA12', 'CA3', 'AHCYL1', 'NR1I3', 'NCOR2', 'JADE3', 'SELENBP1', 'CA4', 'SLC9A1', 'CA5A', 'SLC4A11', 'mir-137', 'mir-185']
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Saving plot for module IC_COV_AKI_mod_203 : module_plots/overview_plot_IC_COV_AKI_mod_203.png
['PCYOX1', 'FBP1', 'RXRA', 'AEBP1', 'ABCG1', 'MYLIP', 'NR1H2', 'KDM3A', 'LIPG', 'LSR', 'AKR1B1', 'HMGCR', 'TMEM106B', 'APOL1', 'LRP4', 'SURF6', 'LDLR', 'ENHO', 'APOC1', 'NPM1', 'VLDLR', 'MAP6', 'RXRB', 'ABCA7', 'TXLNG', 'DBI', 'PLAUR', 'LDLRAP1', 'DNMT1', 'LRRC4C', 'HSD11B1', 'LRP3', 'APOE', 'LRP1B', 'NR1H3', 'VN1R1', 'SCARB1', 'LCAT', 'EEPD1', 'TFAP2B', 'DEK', 'ABCD2', 'mir-106a', 'mir-20b', 'mir-143', 'mir-145', 'mir-27a', 'mir-29a', 'mir-29b', 'mir-29c', 'mir-335', 'mir-622', 'mir-124', 'mir-223', 'mir-96', 'mir-185', 'mir-592']
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Saving plot for module IC_COV_AKI_mod_224 : module_plots/overview_plot_IC_COV_AKI_mod_224.png
['RALGAPA2', 'TPST1', 'BEND7', 'CCDC6', 'ATG2B', 'MAFF', 'H2AX', 'MANBA', 'EYA3', 'RABL3', 'MCPH1', 'AIG1', 'ATG2A', 'EYA4', 'LRRC58', 'SREK1IP1', 'OLA1', 'FAM171A1', 'EYA2', 'PODXL2', 'TPST2', 'RALGAPB', 'TMCC1', 'TLK2', 'PTCHD4', 'mir-224', 'mir-124']
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Saving plot for module IC_COV_AKI_mod_213 : module_plots/overview_plot_IC_COV_AKI_mod_213.png
['TAX1BP1', 'BIRC2', 'SIVA1', 'TRAF4', 'TRAF5', 'TANK', 'CASP9', 'SHARPIN', 'CASP7', 'THOC2', 'TNFRSF13B', 'USP4', 'TBK1', 'MAVS', 'RIGI', 'RIPK2', 'CYLD', 'DIABLO', 'IFIH1', 'OPTN', 'IL17RA', 'CHUK', 'TLR3', 'IKBKG', 'RNF135', 'SARM1', 'RNF216', 'RIPK1', 'CRADD', 'STRAP', 'IRAK4', 'TRAF3', 'TAB3', 'TRAF3IP2', 'MAP4K2', 'NOD2', 'OTUD5', 'TICAM1', 'TRIM25', 'XIAP', 'APAF1', 'BIRC3', 'TAB1', 'IL17RC', 'NLRP12', 'DSTYK', 'NSMAF', 'TRAF1', 'CSNK1E', 'TAB2', 'ZC3H12A', 'OTUD7B', 'BAG4', 'TNFSF13B', 'RBCK1', 'TRADD', 'MALT1', 'NBR1', 'MAP3K7', 'IKBKE', 'KLF5', 'mir-29a', 'mir-203a', 'mir-342', 'MEG3', 'mir-26b', 'mir-9']
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Saving plot for module IMM_AKI_mod_45 : module_plots/overview_plot_IMM_AKI_mod_45.png
['SLC22A7', 'SLC2A9', 'RGN', 'NFIC', 'SLC16A9', 'SLC22A2', 'SLC22A12', 'SLC18B1', 'SLC34A1', 'CLCN3', 'SLC22A6', 'SLC15A2', 'SLC5A8', 'SLC17A3', 'SH3PXD2B', 'PDZK1', 'CEBPD', 'SLCO2B1', 'LRIG3', 'SLC22A11', 'SLC17A5', 'SLC34A2', 'SLC17A1', 'AKAP10', 'SLC5A12', 'mir-95']
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Saving plot for module IMM_COV_AKI_mod_93 : module_plots/overview_plot_IMM_COV_AKI_mod_93.png
['AMBP', 'TNFRSF8', 'ATP5PF', 'COL11A1', 'CD24', 'RAG1', 'KIR3DL1', 'MATK', 'CD79B', 'CLEC7A', 'HINFP', 'ZNF354C', 'PROM1', 'SIGLEC10', 'PTPRC', 'PECAM1', 'ALCAM', 'CD68', 'EPCAM', 'NR2F6', 'G3BP1', 'CD44', 'PRKCG', 'CD7', 'LGALS3', 'CD27', 'COL14A1', 'LGALS8', 'E4F1', 'ALDH1A1', 'LGALS9']
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Saving plot for module IMM_COV_AKI_mod_5 : module_plots/overview_plot_IMM_COV_AKI_mod_5.png
['DNASE1L1', 'PRNP', 'HSPH1', 'ATF6', 'SCARF1', 'DNAJB9', 'DNAJC7', 'NUP155', 'SELENOF', 'ERLEC1', 'CANX', 'SIGMAR1', 'HSP90B1', 'CNPY3', 'UGGT2', 'HOOK1', 'HSPA5', 'CAMLG', 'DDIT3', 'TAP1', 'P4HB', 'GANAB', 'UGGT1', 'DNAJC1', 'TSHR', 'PDIA4', 'NUP107', 'ZBTB22', 'EIF2AK4', 'TLR1', 'NPTN', 'SIL1', 'ERP27', 'AJAP1', 'SLC16A7', 'CALR', 'PDIA3', 'HERPUD1', 'ST13', 'PRKCSH', 'DNAJC3', 'SLC16A3', 'DNAJB6', 'CRTAP', 'STX4', 'TSN', 'CTAG1A', 'TAPBP', 'BSG', 'CTAG1B', 'SEC63', 'NUP37', 'RAE1', 'ERN1', 'SLC16A6', 'HSPA4', 'NUP50', 'NUP35', 'AIMP1', 'PPIB', 'ERN2', 'TTF1', 'ERP29', 'NUP43', 'CAV2', 'CCDC88B', 'NFYB', 'ERAP1', 'ASGR1', 'PDIA6', 'DERL3', 'DNAJB1', 'DNAJB11', 'DNAJB2', 'HYOU1', 'NUP188', 'GNPDA1', 'BAG3', 'EDEM1', 'mir-15a', 'mir-16', 'mir-1', 'mir-30c']
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Saving plot for module IMM_COV_AKI_mod_8 : module_plots/overview_plot_IMM_COV_AKI_mod_8.png
['ABCC4', 'PXDN', 'SLC22A1', 'ABCA10', 'SLC22A2', 'ABCC1', 'ABCA6', 'SLC47A1', 'CRYZ', 'ABCC5', 'NFE2L2', 'SLC22A4', 'ABCA8', 'SLC22A8', 'PLA2G4A']
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Saving plot for module NEU_AKI_mod_152 : module_plots/overview_plot_NEU_AKI_mod_152.png
['PXN', 'NRXN1', 'SYNM', 'DTNA', 'ANKS1A', 'DMD', 'PGM5', 'PLG', 'MAST2', 'SNTB1', 'UTRN', 'SGCD', 'NAA25', 'NEB', 'CAP1', 'DAG1', 'SVIL', 'FGF7', 'WIPF1', 'LAMB1', 'SPTAN1', 'MAL', 'GIT2', 'DCP1A', 'FH', 'SGCE', 'GSN', 'POMGNT1', 'SH3PXD2A', 'TMOD2', 'ACTC1', 'FKTN', 'OPHN1', 'ACTA1', 'MYLK3', 'LARP1', 'DTNB', 'ARHGEF6', 'MTSS1', 'TNNC1', 'PAK4', 'MEX3D', 'PARVB', 'LUC7L2', 'CFL1', 'SNTB2', 'ZBTB6', 'mir-16', 'mir-128', 'mir-320a', 'mir-26b', 'mir-139', 'mir-143', 'mir-15b']
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Saving plot for module NEU_AKI_mod_46 : module_plots/overview_plot_NEU_AKI_mod_46.png
['SELENBP1', 'USP33', 'SLC19A1', 'MMADHC', 'MTR', 'BHMT', 'LMTK3', 'DMGDH', 'AHCYL2', 'MTHFR', 'MAT2A', 'AHCYL1', 'ZBTB16', 'MTRR', 'BHMT2']
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Saving plot for module NEU_AKI_mod_181 : module_plots/overview_plot_NEU_AKI_mod_181.png
['MEF2C', 'IL20RB', 'TSC22D1', 'BCAS4', 'CASP9', 'CLNK', 'TCF12', 'BCAS3', 'PLS1', 'KRT19', 'RUBCNL', 'MAP3K12', 'EPCAM', 'LDLRAD4', 'OLFM4', 'SERPINB3', 'SMAGP', 'MYO3B', 'UBALD2']
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Saving plot for module NEU_COV_AKI_mod_143 : module_plots/overview_plot_NEU_COV_AKI_mod_143.png
['MGLL', 'GPN1', 'ACSL4', 'FAAH', 'FAAH2', 'PRDM5', 'MBD2', 'TRPV1', 'CNR1', 'ABHD6', 'AMMECR1', 'ACSL1']
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Saving plot for module NEU_COV_AKI_mod_100 : module_plots/overview_plot_NEU_COV_AKI_mod_100.png
['NFKBIZ', 'LRFN5', 'PTPRD', 'TRIB1', 'LRFN2', 'EXOSC4', 'BTBD9', 'C1R', 'PTPRS', 'F12', 'STAT3', 'MASP2', 'DDX3X', 'GSDMD', 'MASP1', 'FAM83B', 'SRFBP1', 'C1D', 'KLKB1', 'C1S', 'SERPING1', 'mir-135a', 'mir-141', 'mir-200a', 'mir-200b', 'mir-200c', 'mir-429', 'mir-181a']
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Saving plot for module NEU_COV_AKI_mod_28 : module_plots/overview_plot_NEU_COV_AKI_mod_28.png
['MTR', 'PAPOLB', 'TRDMT1', 'BHMT', 'HOXB3', 'FHIT', 'MAT2A', 'HLCS', 'BHMT2', 'AHCY', 'DLC1', 'TNFRSF10B', 'BIRC5', 'FGFR3', 'CDKN2D', 'DMGDH', 'SIM1', 'KLF4', 'IL32', 'SLC4A4', 'AHCYL2', 'DPP6', 'SLC9A3', 'MT1F', 'DNMT3B', 'DNMT1', 'CADM1', 'ESRRG', 'CXCR4', 'CD8A', 'AHCYL1', 'FOXP3', 'BMP2', 'MTSS1', 'DNMT3A', 'MGMT', 'GSTM2', 'IL3', 'SETD7', 'GAD1', 'CDKN2B', 'mir-205', 'mir-29b', 'mir-15b']
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Saving plot for module NEU_DKD_mod_174 : module_plots/overview_plot_NEU_DKD_mod_174.png
['MXI1', 'TFDP2', 'MTHFD1L', 'SPI1', 'CDC25A', 'SERPINE1', 'RBL2', 'TFDP1', 'GIN1', 'QDPR', 'BMF', 'TBP', 'CES4A', 'TK1', 'UMPS', 'SPARC', 'E2F3', 'ALDH1L1', 'ZNF76', 'HUWE1', 'DHODH', 'DHFR', 'MTHFD2L', 'UXT', 'MCL1', 'mir-141', 'mir-106a', 'mir-17', 'mir-20a', 'mir-146a', 'mir-542', 'mir-24', 'mir-25', 'mir-192', 'mir-215']
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Saving plot for module NEU_DKD_mod_178 : module_plots/overview_plot_NEU_DKD_mod_178.png
['MAP3K11', 'NOD1', 'ZNF397', 'SEPTIN7', 'TAOK2', 'IRAK2', 'CHUK', 'LMO7', 'MAP3K13', 'IRS2', 'MAP2K6', 'AATF', 'SUGT1', 'TAB3', 'NOL10', 'MAP3K10', 'DAPK2', 'ZRANB2', 'SPAG9', 'SH3RF1', 'DAPK3', 'MAP2K4', 'MAP2K3', 'MAP3K2', 'MAP2K7', 'TAOK3', 'TAOK1', 'mir-155', 'mir-125b']
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Saving plot for module NEU_DKD_mod_109 : module_plots/overview_plot_NEU_DKD_mod_109.png
['IL6R', 'ATOSA', 'JAK2', 'IL13RA1', 'AXL', 'IL2', 'KIAA1549', 'EOMES', 'A1BG', 'PRMT5', 'ELMO2', 'TYK2', 'SLC6A8', 'JAK1', 'FXR1', 'ELMO1', 'IFNAR1', 'MAP3K5', 'TNS2', 'ELP2', 'PRKD2', 'IL11RA']
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Saving plot for module NEU_H_CKD_mod_205 : module_plots/overview_plot_NEU_H_CKD_mod_205.png
['NRP1', 'ANKH', 'FGF2', 'FGF7', 'TPM1', 'FGF1', 'TYMS', 'MFGE8', 'SALL1', 'PLXNA1', 'SCNN1A', 'CCK', 'IHH', 'FGF13', 'RFX3', 'SERPINH1', 'PDGFRB', 'mir-21']
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Saving plot for module NEU_H_CKD_mod_23 : module_plots/overview_plot_NEU_H_CKD_mod_23.png
['MLYCD', 'ACACA', 'DBT', 'ACOX1', 'ACSF3', 'ACACB', 'MECR', 'ZNF208', 'ACSS2', 'ECHDC1', 'IVD', 'HAO2', 'HIBADH', 'PDP1', 'TP73', 'FDX1', 'ACADVL', 'MCEE', 'BBX', 'PECR', 'DAO', 'EHHADH', 'LONP2', 'AUH', 'ECI1', 'ACYP2', 'HIBCH', 'ALDH2', 'MCCC1', 'HADHA', 'PCCA', 'SUCLA2', 'SCP2', 'CAT', 'ACSL3', 'BNIP3', 'CBR4', 'ACAA2', 'OPA3', 'TMTC1', 'DDO', 'GRHPR', 'ALDH3A2', 'SLC25A20', 'ECI2', 'MID1IP1', 'CROT', 'MCC', 'ACSS1', 'ACAA1', 'ALDH6A1', 'ADH5', 'HSD11B2', 'HSD17B4', 'ACOT8', 'ACAT1', 'HMGCLL1', 'SUCLG1', 'AGXT', 'PPARD', 'ALDH9A1', 'HYI', 'BBOX1', 'MCCC2', 'TYSND1']
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Saving plot for module NEU_H_CKD_mod_31 : module_plots/overview_plot_NEU_H_CKD_mod_31.png
['PRKCQ', 'STX17', 'CTSH', 'RFXAP', 'SH3BP2', 'CTSA', 'CTSC', 'TLR1', 'CIITA', 'CTSS', 'DDT', 'HLA-DQB1', 'ZCWPW1', 'BET1', 'HLA-DRB5', 'CLIP1', 'RFX5', 'CTSK', 'CD274', 'HLA-DRB1', 'RFXANK', 'ROS1', 'CD74', 'HLA-DPB1', 'HLA-DMB', 'CTSF', 'HLA-DPA1', 'CTSB', 'CTSL', 'CTSO', 'HLA-DRA', 'LGMN']
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Saving plot for module PC_AKI_mod_49 : module_plots/overview_plot_PC_AKI_mod_49.png
['NFKBIZ', 'CCL5', 'ZXDC', 'NFKBID', 'NFKB2', 'IL15', 'IRX2', 'AKIRIN2', 'NKRF', 'CCL2', 'TET2', 'LRBA', 'ETV6']
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Saving plot for module PC_COV_AKI_mod_198 : module_plots/overview_plot_PC_COV_AKI_mod_198.png
['IL6R', 'LEPR', 'JAK2', 'IL20RB', 'GH1', 'MAP2K5', 'HP', 'SH2B1', 'BTBD9', 'PRLR', 'OSMR', 'TYK2', 'JAK1', 'EMC1', 'IMPG2', 'STAP2', 'GHR', 'RNF7', 'CISH', 'IL6ST', 'CD79A', 'ZNF148', 'SPSB1', 'SOX18', 'SKOR1', 'CHRNA4']
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Saving plot for module PC_COV_AKI_mod_115 : module_plots/overview_plot_PC_COV_AKI_mod_115.png
['MAPK11', 'MAP3K13', 'DIPK2A', 'SH3RF1', 'SPAG9', 'MAPK8IP1', 'ETV1', 'ARHGEF28', 'MAP3K10', 'MAP3K9', 'MAP2K4', 'MAPK8IP3']
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Saving plot for module PC_COV_AKI_mod_243 : module_plots/overview_plot_PC_COV_AKI_mod_243.png
['THAP12', 'METAP1', 'METAP2', 'EIF2AK2', 'PEBP1', 'EIF2A', 'MAP2K3', 'TPT1', 'CNKSR2', 'STRBP', 'NFKBIA']
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Saving plot for module PEC_AKI_mod_156 : module_plots/overview_plot_PEC_AKI_mod_156.png
['PIK3C2A', 'MTMR2', 'MTMR3', 'LAMP2', 'FIG4', 'INPP5B', 'SH3D19', 'PTEN', 'INPP5K', 'MTMR4', 'MTMR1', 'PIK3R1', 'PIK3C2G', 'ITSN1', 'OCRL', 'MTMR10', 'SYNJ2', 'INPP4B', 'SACM1L', 'SYNJ1', 'CDIPT', 'MTMR9', 'MTMR6', 'INPP5F', 'INPP5D', 'PUM1', 'INPP4A', 'PIKFYVE', 'VAC14', 'INPPL1', 'PIK3C2B', 'ADM', 'INPP5A', 'FCHO1', 'IPMK', 'PI4KB', 'MTM1', 'EPN1', 'MYO1E', 'PI4KA', 'ITPK1', 'NAAA', 'CLINT1', 'MTMR8', 'ITPKB', 'IMPA2']
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Saving plot for module PEC_AKI_mod_161 : module_plots/overview_plot_PEC_AKI_mod_161.png
['UBE2G2', 'UBA3', 'UBE2D1', 'UBA1', 'MKKS', 'CDC34', 'UBE2D3', 'ATG7', 'RNF40', 'RNF20', 'MRE11', 'ARIH1', 'PDZRN3', 'UBE2U', 'NBN', 'TRIM21', 'UBE2D4', 'TRIM17', 'ZNRF2', 'LIG3', 'TRIM5', 'UBE2V2', 'HERC5', 'DIO2', 'LNX2', 'UBE2J2', 'MPG', 'STUB1', 'ZNRF1', 'UBE2A', 'UBE2R2', 'MID1', 'UBE2K', 'EGLN1', 'UBE2Q2', 'TMLHE', 'UBE2E2', 'UBE2N', 'UBE2T', 'UBE2S', 'UBE2E3', 'UBA5', 'XRCC1', 'UBE2M', 'UBE2D2', 'UBE2Q1', 'LNX1', 'RNF25', 'UBE2E1', 'UBE2B', 'PDZRN4', 'C2CD4A', 'UBE2G1', 'UBE2J1', 'RNF7', 'UBE2L6', 'TRIM22', 'SHPRH', 'UBE2H', 'UBE2L3', 'UBE2O', 'UBTD1', 'BBX', 'UBE2Z', 'OTUB1', 'BBOX1', 'UBA6', 'MUL1', 'RNF19A', 'mir-155', 'mir-7', 'mir-21']
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Saving plot for module PEC_AKI_mod_206 : module_plots/overview_plot_PEC_AKI_mod_206.png
['SOAT2', 'SLC35A2', 'SLC22A1', 'FAT1', 'UMOD', 'SLC22A2', 'FOXA3', 'PHTF1', 'CLCN3', 'SLC15A2', 'SLC37A4', 'SLC47A1', 'SLC22A5', 'KIF12', 'SLC34A2', 'PRODH2', 'SLC17A2', 'SLC2A2', 'SLC18B1', 'SLC34A1', 'SLC22A10', 'HNF1A', 'SLC5A8', 'CIDEA', 'UCP1', 'SLC47A2', 'SLCO1A2', 'LRIG3', 'CLTRN', 'HNF1B', 'SLC22A11', 'ADCY10', 'DEDD', 'DNAJB6', 'GPR39', 'GSTA1', 'SLC2A9', 'FOLR1', 'PDZK1IP1', 'KCNK3', 'SLC22A12', 'UGT2B7', 'B4GALNT2', 'PDZK1', 'CEBPD', 'KRT18', 'CDH15', 'KLHL17', 'BCL6', 'ZNF202', 'SLC22A7', 'SLC16A9', 'APOC3', 'FEM1B', 'SLC23A1', 'SLC22A6', 'SERPINA6', 'SLC17A3', 'APOA1', 'SLC17A4', 'SLC22A8', 'SLC17A1', 'SLC5A12', 'mir-1', 'mir-15a', 'mir-122', 'mir-192', 'mir-95']
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Saving plot for module PEC_COV_AKI_mod_121 : module_plots/overview_plot_PEC_COV_AKI_mod_121.png
['SYCP2', 'THAP12', 'ADAL', 'USP11', 'TRIM25', 'METAP1', 'METAP2', 'EIF2AK2', 'EIF2A', 'ADAT1', 'ARIH1', 'TPT1', 'FLNB', 'ADARB1', 'STRBP', 'USP15']
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Saving plot for module PEC_COV_AKI_mod_158 : module_plots/overview_plot_PEC_COV_AKI_mod_158.png
['SLC14A2', 'WEE1', 'SLC6A6', 'NFAT5', 'SLC6A13', 'SLC6A12', 'ITGAM', 'RNLS', 'IL7', 'CSNK1A1L', 'SLC14A1', 'KLF3', 'NFATC2', 'ALPI', 'CISH', 'ENPP2', 'IAPP', 'S100A4', 'LTB', 'ZNF148', 'C1QTNF12', 'THOP1']
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Saving plot for module PEC_COV_AKI_mod_251 : module_plots/overview_plot_PEC_COV_AKI_mod_251.png
['NXF1', 'NUP107', 'PARP1', 'NUP54', 'NUP160', 'SENP6', 'HSPA2', 'NUP98', 'NUP210', 'PHC2', 'RALGAPA1', 'SENP5', 'RNF111', 'NUP43', 'NUP153', 'PIAS2', 'SUMO1', 'NSMCE1', 'NUTF2', 'POM121C', 'NUP155', 'CPSF6', 'PHC1', 'XPO1', 'HERC2', 'NUP133', 'PHC3', 'NUP85', 'PHAX', 'TPR', 'GEMIN7', 'NEURL4', 'RGPD2', 'DNAJB14', 'RNF4', 'NUP88', 'SMC5', 'SUMO3', 'RPA1', 'NUP205', 'ATF7', 'SMC6', 'TNPO3', 'GLE1', 'KALRN', 'PIAS4', 'WDR33', 'SENP7', 'XPOT', 'SENP2', 'SCMH1', 'SAFB', 'NUP93', 'POM121', 'RANBP2', 'NUP214', 'IPO8', 'NUP50', 'RANGAP1', 'TDG', 'MUC1', 'NES', 'SAE1', 'UBA2', 'MDC1', 'UBL3', 'mir-133b', 'mir-29a', 'mir-940', 'mir-432', 'mir-133a']
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Saving plot for module PEC_H_CKD_mod_122 : module_plots/overview_plot_PEC_H_CKD_mod_122.png
['MUS81', 'PRKDC', 'RAD17', 'ERCC5', 'PHF21A', 'ERCC1', 'XPC', 'ZSWIM7', 'MSH2', 'GEN1', 'XRCC4', 'TOP3A', 'XRCC5', 'SLX4IP', 'RAD23B', 'XPA', 'RAD52', 'TOPBP1', 'WRN', 'ERCC6', 'DCLRE1C', 'MSH3', 'SMARCAL1', 'KDM1A', 'ERCC2', 'USP1', 'MSH6', 'SLX4', 'UVSSA', 'BARD1', 'ERCC3', 'RMI1', 'RAD1', 'ERCC4', 'RCOR1', 'mir-101', 'mir-106a', 'mir-503', 'mir-21', 'mir-3163', 'mir-31', 'mir-526b', 'mir-623', 'mir-373', 'mir-494', 'mir-145', 'mir-7', 'mir-137', 'mir-708', 'mir-329', 'mir-568', 'mir-375', 'mir-432']
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Saving plot for module PEC_H_CKD_mod_170 : module_plots/overview_plot_PEC_H_CKD_mod_170.png
['FGF2', 'MAP2', 'MECOM', 'ANGPTL1', 'COPS9', 'TIE1', 'TEK', 'ANGPT1', 'API5', 'MDS2', 'IHH', 'CCK', 'ANGPT2', 'ELF2', 'PIK3CA', 'ZBTB18', 'mir-1']
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Saving plot for module POD_AKI_mod_219 : module_plots/overview_plot_POD_AKI_mod_219.png
['NPPA', 'IRAG1', 'SLC1A1', 'PPP1R17', 'SLC8A2', 'KCNMB2', 'CD5', 'NCF1', 'GUCY1A2', 'SMTNL1', 'DAP', 'MOCS1', 'PRKG2', 'TMEM184C', 'KCNMB1', 'SLC8A3', 'TNNI2', 'TAF7', 'TNNT3', 'C2CD5', 'TPM2', 'CCDC80', 'TNNT2', 'C1QA', 'TNNI1', 'ERG28', 'TPM4', 'PFKFB1', 'SLC8A1', 'MIP', 'ZER1', 'PRKG1', 'MAPKBP1', 'TRPC7', 'SEPTIN3', 'TNNT1', 'SRF', 'TPM3', 'TPM1', 'RGS18', 'YIPF5', 'YIPF4', 'TNNC1', 'ZYG11A', 'ARL6IP5', 'PAIP2B', 'TNNC2']
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Saving plot for module POD_AKI_mod_213 : module_plots/overview_plot_POD_AKI_mod_213.png
['KCNN4', 'ANK3', 'MAGED1', 'GAS2L1', 'NEXN', 'CNTN2', 'SCN1B', 'NEDD4L', 'KCNE4', 'NDFIP2', 'KCNQ1', 'AKAP7', 'SLC22A18AS', 'KCNQ3', 'SCNN1A', 'KCNJ2', 'KCNJ12', 'KCNJ14', 'FGFR4', 'SCNN1G', 'NEDD4', 'KCNJ13', 'KCNE3', 'NAV3', 'KCNK5', 'MAPRE1', 'NAV1', 'SCN3A', 'SCN3B', 'DBN1', 'NFASC', 'SCN8A', 'SCN7A', 'WWP2', 'ARRDC3', 'SCN2A', 'MAPRE3', 'SCN9A', 'mir-497', 'mir-1', 'mir-7', 'mir-10b', 'mir-92a']
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Saving plot for module POD_AKI_mod_203 : module_plots/overview_plot_POD_AKI_mod_203.png
['PTPN21', 'ZCCHC10', 'HIPK1', 'BMX', 'CABP1', 'MYNN', 'TRIB1', 'PQBP1', 'CNBP', 'CELF1', 'HLCS', 'GRK5', 'NRBP1', 'MBNL1', 'STK40', 'TRIB2', 'YIPF7', 'ATXN1', 'MAP3K13', 'RPS6KL1', 'DMPK', 'MBNL2', 'RUFY2', 'RUFY1', 'RALY', 'SND1', 'CABP5', 'CELF3', 'MAP4K2', 'MAP3K10', 'KIF3A', 'SH3RF1', 'TCF3', 'MAP3K9', 'MAP2K4', 'MBNL3', 'PAXBP1', 'MAP2K7', 'mir-224', 'mir-125b']
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Saving plot for module POD_COV_AKI_mod_163 : module_plots/overview_plot_POD_COV_AKI_mod_163.png
['PDCD6IP', 'CC2D1B', 'TUBB8', 'KIF5B', 'HDAC6', 'SMCHD1', 'DPYSL2', 'VPS37D', 'VPS4A', 'FEZ2', 'MAPT', 'CHMP3', 'TUBB6', 'LEMD2', 'MARVELD2', 'VPS37A', 'TSG101', 'STAM2', 'SPAST', 'CHMP2A', 'CHMP1A', 'ARL13B', 'ZFYVE19', 'TUBB2A', 'TUBB2B', 'TOM1L1', 'VPS37C', 'VPS36', 'VPS25', 'CHMP4C', 'SETD2', 'IST1', 'CC2D1A', 'CAMSAP1', 'PLIN3', 'ARHGEF2', 'VPS37B', 'TUBA4A', 'STAM', 'CHMP4B', 'MVB12B', 'MARK1', 'HGS', 'TUBB4B', 'NUMA1', 'TUBA1A', 'MAP9', 'RNF103-CHMP3', 'CHMP6', 'VPS28', 'TTC19', 'CHMP5', 'PTPN23', 'IFT88', 'UBAP1', 'CHMP2B', 'VPS4B', 'FEZ1', 'TUBA1C', 'VASH1', 'SNF8', 'TTL', 'MITD1', 'TUBB', 'CHMP7', 'MVB12A', 'PARK7', 'VTA1', 'TUBA1B', 'SCAMP3', 'mir-433', 'mir-34c', 'mir-124']
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Saving plot for module POD_COV_AKI_mod_39 : module_plots/overview_plot_POD_COV_AKI_mod_39.png
['PSMC4', 'PSMG3', 'PSMD11', 'PTTG2', 'KBTBD7', 'PSMD6', 'PHC1', 'PSMC1', 'UBQLN1', 'CCNF', 'NF1', 'PSMD1', 'PSMB8', 'PRICKLE1', 'PSME1', 'ANAPC5', 'PSMA8', 'NUMB', 'PSME4', 'SUFU', 'PSMC3', 'PSMD12', 'PSMD4', 'PSMA1', 'HNRNPD', 'PSMD13', 'USP14', 'PSMD14', 'UBR1', 'PSME2', 'ADRM1', 'OAZ1', 'CDC27', 'SPRED1', 'PSMB3', 'PSMA4', 'ELOC', 'PSMA7', 'GLI3', 'OAZ2', 'SPRED2', 'AZIN1', 'POMP', 'CDC16', 'ANAPC11', 'PSMB4', 'PSMB6', 'ODC1', 'FZR1', 'ITCH', 'PSMC2', 'PSMA3', 'PSMB5', 'GMNN', 'CUL1', 'PAAF1', 'PSMC5', 'PSMD8', 'PSMF1', 'ANAPC1', 'CCDC92', 'PSMD10', 'CUL5', 'ANAPC10', 'CDC25A', 'USP47', 'PSMD7', 'PSMB1', 'ANAPC15', 'PSMB7', 'BTRC', 'PSMD2', 'PSMB2', 'LGI3', 'UCHL5', 'ELOB', 'PSMC6', 'PSMD3', 'UBQLN4', 'ANAPC7', 'FBXL7', 'mir-424', 'mir-433', 'mir-214']
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Saving plot for module POD_COV_AKI_mod_72 : module_plots/overview_plot_POD_COV_AKI_mod_72.png
['KAAG1', 'AP3M1', 'TCF4', 'ACOT13', 'DCDC2', 'ZNF563', 'NEUROG2', 'PTBP3', 'CMIP', 'GRHL1', 'HDAC3', 'GCFC2', 'ID2', 'KIAA0319', 'SKA2', 'PLGRKT', 'TMX4', 'ZBTB18', 'HNRNPLL', 'mir-125b']
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Saving plot for module PT_AKI_mod_80 : module_plots/overview_plot_PT_AKI_mod_80.png
['CSTB', 'CTSH', 'REL', 'CPPED1', 'RFXAP', 'SH3BP2', 'CTSA', 'CTSC', 'CTSS', 'DDT', 'HLA-DQB1', 'CSK', 'HLA-DRB5', 'CLIP1', 'CTSK', 'HLA-DRB1', 'TRAPPC10', 'HRH1', 'GNAI1', 'BCL11A', 'CST3', 'CD74', 'HLA-DPB1', 'FYB1', 'CTSF', 'HLA-DPA1', 'HBP1', 'PTPRJ', 'CTSB', 'CTSL', 'NR2C1', 'CTSO', 'DBNL', 'BGLAP', 'HLA-DRA', 'ACKR3', 'LGMN', 'RRP1B', 'mir-15b', 'mir-21', 'mir-192', 'mir-100']
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Saving plot for module PT_AKI_mod_224 : module_plots/overview_plot_PT_AKI_mod_224.png
['SSBP4', 'DLG4', 'LHX1', 'KCNB1', 'PAX8', 'LMO4', 'PTPRE', 'TYK2', 'IFNAR2', 'JAK1', 'INIP', 'THRB', 'SSBP3', 'LDB2', 'USP18', 'ANGPTL3', 'SSBP2', 'TSHR', 'SSBP1', 'CALCB', 'LDB1']
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Saving plot for module PT_AKI_mod_51 : module_plots/overview_plot_PT_AKI_mod_51.png
['SLC22A7', 'ZC3H12C', 'PDZK1IP1', 'NFIC', 'SLC22A1', 'SLC1A5', 'SLC16A9', 'SLC22A2', 'NPHS2', 'SLC22A12', 'SLC18B1', 'PCNX4', 'SLC22A13', 'SLC34A1', 'SLC22A6', 'SLC22A10', 'SLC37A4', 'SLC5A8', 'SLC17A3', 'TINF2', 'CEBPB', 'PDZK1', 'CEBPD', 'SLCO2B1', 'SLC16A1', 'SLC47A2', 'SLCO1A2', 'TINAG', 'LRIG3', 'NR1H4', 'SLC34A3', 'SLC22A11', 'SLC22A8', 'SLC34A2', 'SLC17A1', 'SLC5A12', 'mir-95']
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Saving plot for module PT_COV_AKI_mod_82 : module_plots/overview_plot_PT_COV_AKI_mod_82.png
['CSTB', 'NEU1', 'CTSH', 'CPPED1', 'CTSA', 'CTSC', 'GALNS', 'DDT', 'COPG2', 'HAVCR1', 'HLA-DRB1', 'TRAPPC10', 'PDGFB', 'CD74', 'CST3', 'PAG1', 'CTSB', 'CTSL', 'HLA-DRA', 'LGMN', 'mir-204', 'mir-184', 'mir-29b']
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Saving plot for module PT_COV_AKI_mod_170 : module_plots/overview_plot_PT_COV_AKI_mod_170.png
['IFITM3', 'HLA-A', 'TAPBP', 'VAMP8', 'HLA-E', 'HLA-DRB5', 'HLA-C', 'HIVEP2', 'CD86', 'CD28', 'HLA-B', 'CALR', 'ADAR', 'TAP1', 'HLA-DPA1', 'CD80']
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Saving plot for module PapE_AKI_mod_24 : module_plots/overview_plot_PapE_AKI_mod_24.png
['IL6R', 'EML4', 'CSF2', 'IL2RB', 'CCND3', 'PIAS1', 'LRPPRC', 'IL2RA', 'IL4R', 'SMPD1', 'IFNGR2', 'HEBP2', 'JAK3']
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Saving plot for module PapE_AKI_mod_27 : module_plots/overview_plot_PapE_AKI_mod_27.png
['C6orf47', 'PARD6B', 'CRTC3', 'C12orf57', 'BCL10', 'SRPK2', 'CITED2', 'ZCCHC7', 'VEZF1', 'TPM4', 'CD74', 'CTCF', 'ZFP36L1', 'TRIM69', 'IL23A', 'SCFD1', 'CCDC85B', 'BCL3', 'TPM3', 'TPM1', 'NPNT', 'MYL6']
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Saving plot for module PapE_AKI_mod_78 : module_plots/overview_plot_PapE_AKI_mod_78.png
['TNNT3', 'RANBP9', 'HTT', 'GLI1', 'CDKN1B', 'SGK1', 'SMO', 'KCNJ1', 'MAP3K3', 'MAPK1', 'TNNI2', 'PPP2R5C', 'PMM2', 'KCNJ16', 'GAS5', 'mir-324']
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Saving plot for module PapE_COV_AKI_mod_69 : module_plots/overview_plot_PapE_COV_AKI_mod_69.png
['ECT2', 'WDR72', 'IQGAP1', 'SVOPL', 'PRSS16', 'GAS1', 'SLC22A1', 'UMOD', 'SLC22A2', 'RNPEPL1', 'SLC22A6', 'DPP4', 'SLC28A1', 'DPP6', 'DNPEP', 'FN1', 'RNPEP', 'CARD11', 'PAM', 'DPP10', 'DMTF1', 'ANPEP', 'GIP', 'ADA', 'KIF12', 'HNF1B', 'PRCP', 'LGALS3', 'GSTA1']
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Saving plot for module PapE_DKD_mod_193 : module_plots/overview_plot_PapE_DKD_mod_193.png
['KDELR1', 'BACE1', 'EWSR1', 'STMN3', 'ABHD12', 'PLD1', 'APOL1', 'ZDHHC3', 'CUTA', 'RTN3', 'PICALM', 'PLD2', 'PISD', 'ABCA1', 'SCAMP2', 'NAPEPLD', 'APOL6', 'ZDHHC7', 'GOLM1', 'GGA1', 'DVL2', 'APP', 'APOE', 'LRP1B', 'ZDHHC2', 'SCARB1', 'ZDHHC15', 'PLD3', 'GPLD1', 'LCAT', 'ZDHHC8', 'BACE2', 'ARF4', 'ABHD6', 'SRSF11', 'CLU']
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Saving plot for module PapE_DKD_mod_21 : module_plots/overview_plot_PapE_DKD_mod_21.png
['INSIG1', 'INSIG2', 'FBP1', 'LMF1', 'LIPG', 'ACSF2', 'LIPC', 'CCL24', 'ZFX', 'PNPLA3', 'SCD5', 'PGRMC1', 'ENHO', 'LIPE', 'AMFR', 'CD4', 'G6PC1', 'HSD11B1', 'CCK', 'ENPEP', 'PNLIPRP3', 'HSD17B12', 'HSD17B2', 'NR1H3', 'FOXM1', 'MGLL', 'SOD1', 'NCEH1', 'SCAP', 'SREBF2', 'LSS', 'SREBF1', 'ELOVL6', 'ABCD2', 'mir-98', 'mir-376a', 'mir-505', 'mir-33b', 'mir-206', 'mir-211']
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Saving plot for module PapE_DKD_mod_96 : module_plots/overview_plot_PapE_DKD_mod_96.png
['TMEM258', 'CUL5', 'PSMC4', 'PSMD14', 'DDOST', 'UBC', 'PSMD11', 'PSMB4', 'PSMA5', 'PSMD6', 'STT3A', 'PSMB6', 'TRAM1', 'SEC11C', 'PSMB1', 'RPN1', 'VANGL1', 'TUSC2', 'VANGL2', 'RPN2', 'OSTC', 'PSME2', 'ITCH', 'SPCS2', 'TFF3', 'STT3B', 'SRPRB', 'PSMB7', 'DVL3', 'PSMB8', 'PRICKLE1', 'PSME1', 'SPRED1', 'NUMB', 'DVL2', 'SMO', 'DAD1', 'PSMA8', 'PSMB3', 'PAK2', 'PSMA4', 'PSMB2', 'MLEC', 'TUSC3', 'SPCS3', 'SRPRA', 'OST4', 'PSMB5', 'MINK1', 'ELOC', 'ELOB', 'SUFU', 'PSMD5', 'SPCS1', 'PSMA7', 'PSMD12', 'PSMC6', 'PRICKLE2', 'PSMD8', 'PSMD3', 'PSMD4', 'PSMF1', 'OAZ2', 'SNCA', 'ANAPC7', 'UBQLN4', 'SEC11A', 'AZIN1', 'MAGT1', 'POMP', 'mir-320a']
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Saving plot for module TAL_AKI_mod_124 : module_plots/overview_plot_TAL_AKI_mod_124.png
['NEK1', 'ABHD5', 'CDC25A', 'CDC25C', 'HBG2', 'PNPLA4', 'VEGFA', 'DGAT1', 'PNPLA2', 'CUBN', 'AATF', 'FHL2', 'CENPX', 'NOSTRIN', 'MAPKAPK2', 'PLIN5', 'ZDHHC2', 'NOSIP', 'PDE4A', 'VEGFC', 'TSC1', 'BHLHE40', 'CKAP4', 'G0S2', 'YBX3', 'PNPLA8', 'mir-128', 'mir-451a', 'RRP1B']
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Saving plot for module TAL_COV_AKI_mod_124 : module_plots/overview_plot_TAL_COV_AKI_mod_124.png
['MT3', 'ATP2A2', 'AKR7A2', 'MT1A', 'HBB', 'ABCD4', 'CRBN', 'CASP1', 'NQO2', 'CLDN16', 'DLL4', 'PCBD1', 'ENC1', 'TXNIP', 'ATP1B3', 'HBA2', 'OSGIN1', 'LTB4R', 'TFB2M', 'MGST1', 'PGAM5', 'PRX', 'ATP1A1', 'MGST3', 'AVP', 'SLC31A1', 'DPP3', 'NEIL2', 'TOMM34', 'RNF123', 'AOX1', 'MGST2', 'KIF17', 'CRYZ', 'PCBD2', 'MTHFD2', 'TFB1M', 'PPARGC1B', 'ABCC2', 'EIF2AK1', 'CPOX', 'ABCA2', 'NFE2L2', 'HMOX2', 'SLC15A1', 'VAMP1', 'AKR1C3', 'AKR1A1', 'POR', 'HMOX1', 'ATP1B2', 'CLDN19', 'OGG1', 'ATP2A1', 'HBA1', 'THOC1', 'CYP2J2', 'FXYD2', 'SLC12A3', 'SLC7A11', 'mir-130a', 'mir-30b', 'mir-218']
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Saving plot for module TAL_COV_AKI_mod_117 : module_plots/overview_plot_TAL_COV_AKI_mod_117.png
['NAP1L3', 'TRIB1', 'STK40', 'TRIB2', 'KCNIP4', 'NAP1L1', 'MAPK8IP1', 'ARHGEF28', 'MLXIPL', 'PTK2B', 'KCNIP2', 'UBTD2', 'MAP4K2', 'NAP1L2', 'SH3RF1', 'KCND1', 'PTPA', 'MAP3K9', 'MAP2K4', 'MAP2K7', 'mir-31']
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Saving plot for module TAL_COV_AKI_mod_144 : module_plots/overview_plot_TAL_COV_AKI_mod_144.png
['SLC22A7', 'SLC2A9', 'CSRP1', 'FAM83H', 'FOLR1', 'WDR72', 'PCBD1', 'PDZK1IP1', 'ABCG1', 'NFIC', 'UMOD', 'WDR7', 'APOC3', 'SLC22A2', 'SLC16A9', 'KCNK3', 'SLC22A12', 'SLC18B1', 'PLXNC1', 'SLC34A1', 'NNMT', 'PHTF1', 'SLC22A6', 'ACAT2', 'SLC15A2', 'SLC22A10', 'TRIP12', 'HNF1A', 'SLC47A1', 'SLC17A3', 'SULF1', 'B3GALT5', 'PROS1', 'PDZK1', 'ST6GAL1', 'KRT18', 'SLC47A2', 'APOA1', 'AFM', 'LRIG3', 'CLTRN', 'POMGNT1', 'HNF1B', 'KIF12', 'MIB1', 'SLC22A11', 'ZNF202', 'SLC17A5', 'ATF1', 'SLC17A4', 'POLR3GL', 'PKHD1', 'SLC34A2', 'DEDD', 'ADCY10', 'SLC22A8', 'SLC17A1', 'SLC5A12', 'GSTA1', 'mir-122', 'mir-192']
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Saving plot for module VSM-P_COV_AKI_mod_193 : module_plots/overview_plot_VSM-P_COV_AKI_mod_193.png
['ACP3', 'CD24', 'CYP26B1', 'ERBB2', 'HOXC11', 'TACSTD2', 'KDM5A', 'PROM1', 'PTPRC', 'ITGA6', 'ALDH1A2', 'EPCAM', 'MBD2', 'G3BP1', 'CD44', 'CYP3A5', 'RDH11', 'ALDH1A3', 'PGAP3', 'ALDH1A1', 'mir-140', 'mir-92b', 'mir-124', 'mir-125a']
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Saving plot for module VSM-P_COV_AKI_mod_76 : module_plots/overview_plot_VSM-P_COV_AKI_mod_76.png
['COBL', 'ARHGAP12', 'KNDC1', 'IQGAP1', 'BBS9', 'USP47', 'RAC2', 'WASF2', 'IL2', 'EPS8', 'DIPK2A', 'MAP3K13', 'MAPK8IP1', 'BCL10', 'GTF3C4', 'PIKFYVE', 'C10orf90', 'PRRC2A', 'COIL', 'KCNAB2', 'IL27RA', 'DOCK9', 'RBM6', 'HMGA1', 'SH3RF1', 'TIAM2', 'FZD2', 'DOCK1', 'MAP3K9', 'MAP2K4', 'MAPK10', 'WDR41', 'PCYT1B', 'mir-146a', 'mir-25']
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Saving plot for module VSM-P_COV_AKI_mod_133 : module_plots/overview_plot_VSM-P_COV_AKI_mod_133.png
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fig.tight_layout()
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[ ]:
[15]:
spec_mods = descrDF[descrDF.name.str.startswith("EC_")].name
tlda.describe_module_scrna(adata, celltype_col, condition_col, module_names=list(spec_mods), show_plot=False, plot_folder="module_plots_spec")
disease 7184
geneset 27643
drug 3195
['SYNE4', 'CLCN4', 'IDH3A', 'PLEC', 'TAL1', 'ZPLD1', 'SYNE2', 'LMNA', 'CROCC', 'CEBPA', 'KIF3B', 'SYNE1', 'MUSK', 'OSGEP', 'RBMS2', 'TP73', 'S100Z', 'KIAA1217', 'EMCN', 'RBMS1', 'EMD', 'LMNB1', 'CLMN', 'FHOD1']
40
/mnt/raidbio/extproj/projekte/regulatory_networks/kpmp/../mikg/kgraph.py:3987: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
fig.tight_layout()
Saving plot for community EC_COV_AKI_mod_34 : module_plots_spec/overview_plot_EC_COV_AKI_mod_34.png
['H4C14', 'ANO6', 'ANO10', 'APOL4', 'TCF4', 'TNFRSF19', 'ABHD12B', 'LINC03040', 'LGR5', 'RHBG', 'CTXN1', 'BEST1', 'H4C2', 'C2CD4A', 'STAR', 'ANO9', 'RTN4', 'SECTM1', 'ACVR1C', 'DCXR', 'ANO1']
40
/mnt/raidbio/extproj/projekte/regulatory_networks/kpmp/../mikg/kgraph.py:3987: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
fig.tight_layout()
Saving plot for community EC_COV_AKI_mod_183 : module_plots_spec/overview_plot_EC_COV_AKI_mod_183.png
['IFITM3', 'SAMHD1', 'HLA-A', 'OAS3', 'ZMPSTE24', 'BIRC2', 'VAMP3', 'IRF3', 'IFI44', 'IFI27L2', 'BST2', 'IFI44L', 'MX2', 'MAVS', 'PARP12', 'ISG20', 'IFIH1', 'HLA-E', 'OAS2', 'IP6K2', 'GBP1', 'OASL', 'SP110', 'IRF2', 'HLA-C', 'SAMD9L', 'IFI27L1', 'IFIT1', 'LAMTOR5', 'DDX60', 'TRIM25', 'ISG15', 'BIRC3', 'HERC6', 'IFIT2', 'RNASEL', 'MX1', 'IFITM2', 'HLA-F', 'HLA-B', 'IFIT5', 'ADAR', 'XAF1', 'KLRD1', 'IFI35', 'IL12B', 'VAPA', 'GBP2', 'IFI6', 'IFIT3', 'RSAD2', 'OAS1', 'IRF7', 'IFI27', 'mir-141', 'mir-203a', 'mir-34a', 'mir-16']
40
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fig.tight_layout()
Saving plot for community EC_COV_AKI_mod_71 : module_plots_spec/overview_plot_EC_COV_AKI_mod_71.png
['LHX1', 'SLC3A1', 'PAX8', 'NBN', 'GEN1', 'BEX1', 'WRN', 'CAMKMT', 'PPM1B', 'SSBP3', 'LDB2', 'BARD1', 'SSBP2', 'RAD51B', 'LMO2', 'MAP3K7', 'RBBP8']
40
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fig.tight_layout()
Saving plot for community EC_COV_AKI_mod_46 : module_plots_spec/overview_plot_EC_COV_AKI_mod_46.png
['MNX1', 'CUZD1', 'ADM2', 'HOXA3', 'GNG2', 'GAP43', 'HOXB3', 'CCDC28B', 'TRAF4', 'H2AC11', 'EMC9', 'TCF12', 'GIMAP4', 'TRIB2', 'MDP1', 'CALCR', 'CHI3L1', 'GIMAP8', 'AP1G2', 'GNB1', 'CALM1', 'LY86', 'DMRT2', 'RAMP3', 'VIPR1', 'PTMA', 'SCTR', 'TAC1', 'BSND', 'WASL', 'MZT2B', 'DMRT1', 'RUBCNL', 'LILRB1', 'VWDE', 'VGF', 'ATG5', 'LHX2', 'RAMP2', 'NXF3', 'GABRB3', 'HEPHL1', 'TBX5', 'GIMAP5', 'CALCB', 'ABHD11', 'CALCRL', 'UBALD2', 'mir-200a', 'mir-363', 'mir-206']
40
/mnt/raidbio/extproj/projekte/regulatory_networks/kpmp/../mikg/kgraph.py:3987: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
fig.tight_layout()
Saving plot for community EC_COV_AKI_mod_74 : module_plots_spec/overview_plot_EC_COV_AKI_mod_74.png
['ARRB2', 'TLR2', 'FASN', 'CEACAM1', 'CXCL8', 'POLDIP2', 'NFKBIE', 'HAVCR2', 'PRKCD', 'ADRB2']
40
/mnt/raidbio/extproj/projekte/regulatory_networks/kpmp/../mikg/kgraph.py:3987: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
fig.tight_layout()
Saving plot for community EC_COV_AKI_mod_148 : module_plots_spec/overview_plot_EC_COV_AKI_mod_148.png
['CALM3', 'SPTBN2', 'TRPV6', 'PDE3B', 'UNC13B', 'RIC1', 'RAC2', 'RAPGEF3', 'TBK1', 'PKIB', 'GRM7', 'KCNQ1', 'MYLK', 'AMPD3', 'MYO10', 'ELMO1', 'AK3', 'ATP2B4', 'ADK', 'DRD2', 'PDE1A', 'PDE3A', 'SLFN12', 'SCN8A', 'PDE1C', 'DOCK1', 'AKAP5', 'RAB3A', 'CAMP', 'mir-203a']
40
/mnt/raidbio/extproj/projekte/regulatory_networks/kpmp/../mikg/kgraph.py:3987: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
fig.tight_layout()
Saving plot for community EC_COV_AKI_mod_156 : module_plots_spec/overview_plot_EC_COV_AKI_mod_156.png
['ACSL4', 'RPS6KB1', 'HTATIP2', 'ACOX1', 'MRE11', 'EEF2K', 'ELOVL1', 'ACSM2A', 'HADHA', 'ACADS', 'RXRB', 'ACSL1', 'SH3GLB1', 'ACSM2B', 'ACADVL', 'ACSL3', 'ELOVL6', 'AMMECR1', 'mir-16', 'mir-200c', 'mir-34a']
40
/mnt/raidbio/extproj/projekte/regulatory_networks/kpmp/../mikg/kgraph.py:3987: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
fig.tight_layout()
Saving plot for community EC_COV_AKI_mod_172 : module_plots_spec/overview_plot_EC_COV_AKI_mod_172.png
['RANBP10', 'TLN2', 'HDLBP', 'MAX', 'EHBP1', 'ANK2', 'EHD3', 'FDPS', 'VPS45', 'RAB11FIP2', 'PACSIN2', 'SERPINE1', 'MYOF', 'YY1', 'EPS15', 'EHD1', 'mir-122']
40
/mnt/raidbio/extproj/projekte/regulatory_networks/kpmp/../mikg/kgraph.py:3987: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
fig.tight_layout()
Saving plot for community EC_COV_AKI_mod_3 : module_plots_spec/overview_plot_EC_COV_AKI_mod_3.png
['BTBD7', 'CLK2', 'FOXA1', 'MTA3', 'BRCA2', 'ZEB2', 'SNAI2', 'OXTR', 'TCF15', 'CLDN1', 'ESRP2', 'LPAR2', 'ITGB4', 'COL2A1', 'CXADR', 'CDH17', 'MEOX2', 'CAPN1', 'ARHGAP31', 'HPGD', 'MMP17', 'ACAN', 'ALPL', 'CLDN4', 'ZBTB17', 'PRSS8', 'mir-495', 'let-7b', 'mir-17', 'mir-1245a', 'mir-451a', 'mir-155', 'mir-29a', 'mir-143', 'mir-122', 'mir-21']
40
/mnt/raidbio/extproj/projekte/regulatory_networks/kpmp/../mikg/kgraph.py:3987: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
fig.tight_layout()
Saving plot for community EC_COV_AKI_mod_108 : module_plots_spec/overview_plot_EC_COV_AKI_mod_108.png
['HAP1', 'GNB4', 'CACNA1C', 'CDK13', 'ATP1A1', 'GNG5', 'CDK17', 'PPP1R1B', 'RCAN2', 'PPP3CC', 'NOS1', 'PPP3CA', 'GUK1', 'MYL12A', 'KSR2', 'CACNA2D1', 'ZNF804A', 'KALRN', 'CATSPER2', 'GNG7', 'GNG11', 'RARB', 'CACNA1D', 'BIN1', 'PPP3CB', 'CACNB4', 'NOS1AP', 'CACNB2', 'PPP1CB', 'GNG10']
40
/mnt/raidbio/extproj/projekte/regulatory_networks/kpmp/../mikg/kgraph.py:3987: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
fig.tight_layout()
Saving plot for community EC_COV_AKI_mod_114 : module_plots_spec/overview_plot_EC_COV_AKI_mod_114.png
['ESRRB', 'RIMKLB', 'DNASE1', 'BGLAP', 'ENO1', 'ATF6', 'PNRC2', 'MED13', 'AFMID', 'CDK19', 'SUZ12', 'WDR5', 'MED23', 'ESRRG', 'THRAP3', 'MED27', 'PPARGC1B', 'PCK1', 'CREBZF', 'NRIP1', 'MED4', 'ACADM', 'MED14', 'IBSP', 'CCNC', 'MED15', 'mir-125b', 'mir-199a', 'mir-424', 'mir-208a', 'mir-18a', 'HOTTIP', 'mir-205', 'mir-320a', 'mir-33b', 'mir-221']
40
/mnt/raidbio/extproj/projekte/regulatory_networks/kpmp/../mikg/kgraph.py:3987: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
fig.tight_layout()
Saving plot for community EC_COV_AKI_mod_33 : module_plots_spec/overview_plot_EC_COV_AKI_mod_33.png
['FOXN3', 'KCNIP1', 'WEE1', 'KCND2', 'MEN1', 'STMN1', 'KCND3', 'KCND1', 'PAK1', 'KCNIP2', 'KCNIP4']
40
/mnt/raidbio/extproj/projekte/regulatory_networks/kpmp/../mikg/kgraph.py:3987: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
fig.tight_layout()
Saving plot for community EC_COV_AKI_mod_120 : module_plots_spec/overview_plot_EC_COV_AKI_mod_120.png
['DTNBP1', 'CHD7', 'GRIN1', 'PSEN1', 'ERBB2', 'NRG3', 'ERBB3', 'PIK3C3', 'DOCK7', 'ATP2C1', 'AUTS2', 'ADAM17', 'PRKCB', 'NRAS', 'SORT1', 'ADGRL1', 'HRAS', 'NRG4', 'CADPS2', 'NRG2', 'NRG1', 'PAX2', 'CDC37', 'mir-193a', 'mir-21', 'mir-9', 'mir-96', 'mir-149', 'mir-122', 'mir-152', 'mir-26a', 'mir-184']
40
/mnt/raidbio/extproj/projekte/regulatory_networks/kpmp/../mikg/kgraph.py:3987: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
fig.tight_layout()
Saving plot for community EC_COV_AKI_mod_29 : module_plots_spec/overview_plot_EC_COV_AKI_mod_29.png
['WNK4', 'KCNJ10', 'MTOR', 'PRKAA1', 'KCNJ1', 'NFAT5', 'KCNJ16', 'ITSN1', 'SLC12A4', 'TRPC3', 'SLC12A1', 'SLC4A4', 'WNK3', 'SLC9A3', 'BSND', 'PDZK1', 'CLCNKB', 'CLCNKA', 'SGK3', 'AQP2', 'OXSR1', 'LTB', 'SLC4A2', 'KCNJ15']
40
/mnt/raidbio/extproj/projekte/regulatory_networks/kpmp/../mikg/kgraph.py:3987: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
fig.tight_layout()
Saving plot for community EC_H_CKD_mod_14 : module_plots_spec/overview_plot_EC_H_CKD_mod_14.png
[18]:
xTotalCount = 0
for x in tlda.cellgroupdata:
xcommcount = len(tlda.cellgroupdata[x]["communities"])
print(x, xcommcount)
xTotalCount += xcommcount
xTotalCount
ATL 109
CNT 155
DCT 148
DTL 148
EC 15
FIB 96
IC 74
IMM 32
NEU 130
PC 31
PEC 170
POD 47
PT 39
PapE 78
TAL 56
VSM-P 53
[18]:
1381
[16]:
aid = AIDescriptor()
[17]:
spec_mods
[17]:
560 EC_COV_AKI_mod_34
561 EC_COV_AKI_mod_183
562 EC_COV_AKI_mod_71
563 EC_COV_AKI_mod_46
564 EC_COV_AKI_mod_74
565 EC_COV_AKI_mod_148
566 EC_COV_AKI_mod_156
567 EC_COV_AKI_mod_172
568 EC_COV_AKI_mod_3
569 EC_COV_AKI_mod_108
570 EC_COV_AKI_mod_114
571 EC_COV_AKI_mod_33
572 EC_COV_AKI_mod_120
573 EC_COV_AKI_mod_29
574 EC_H_CKD_mod_14
Name: name, dtype: object
[ ]:
context = "endothelial cells in kidney"
for modname in spec_mods:
print(modname)
res=aid.query_genelist(tlda.communities[modname], context, verbose=True, word_cloud=True)
plt.show()
plt.close()
print(res)
print()
EC_COV_AKI_mod_34
Use the following pieces of information to answer the user's question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Question: The following genes are dysregulated in endothelial cells in kidney: KIAA1217, ZPLD1, S100Z, PLEC, IDH3A, TAL1, CEBPA, EMD, SYNE2, RBMS2, CLCN4, TP73, FHOD1, OSGEP, CLMN, LMNB1, EMCN, RBMS1, MUSK, LMNA, SYNE1, SYNE4, CROCC, KIF3B. How are these genes connected and which molecular functions are altered?
Do not repeat functions of single genes.
Only return the helpful answer. Answer must be concise, detailed and well explained.
Helpful answer:
These genes are dysregulated in endothelial cells in kidney, indicating that they may play a role in the development or progression of kidney disease. The specific molecular functions altered by these gene dysregulations are not known, but some of them have been implicated in various biological processes such as cell adhesion, cytoskeleton organization, and DNA repair. For example, KIAA1217 is involved in the regulation of actin filament organization, while ZPLD1 is involved in the regulation of cell adhesion. S100Z is involved in the regulation of calcium ion homeostasis, while PLEC is involved in the regulation of cytoskeleton organization. IDH3A is involved in the regulation of DNA repair, while TAL1 and CEBPA are involved in the regulation of cell differentiation. EMD is involved in the regulation of cell adhesion, while SYNE2 is involved in the regulation of cytoskeleton organization. RBMS2 is involved in the regulation of cell division, while CLCN4 is involved in the regulation of ion transport. TP73 is involved in the regulation of apoptosis, while FHOD1 is involved in the regulation of actin filament organization. OSGEP is involved in the regulation of protein localization, while CLMN is involved in the regulation of cell division. LMNB1 is involved in the regulation of nuclear structure, while EMCN is involved in the regulation of cytoskeleton organization. RBMS1 is involved in the regulation of cell division, while MUSK is involved in the regulation of cell adhesion. LMNA is involved in the regulation of nuclear structure, while SYNE1 and SYNE4 are involved in the regulation of cytoskeleton organization. CROCC is involved in the regulation of protein localization, while KIF3B is involved in the regulation of microtubule dynamics. These genes may interact with each other to regulate various biological processes in endothelial cells in kidney, but further research is needed to understand their exact roles and interactions.
EC_COV_AKI_mod_183
Use the following pieces of information to answer the user's question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Question: The following genes are dysregulated in endothelial cells in kidney: RHBG, RTN4, TCF4, C2CD4A, H4C14, ANO10, ACVR1C, BEST1, DCXR, CTXN1, H4C2, LGR5, STAR, ANO6, ANO9, APOL4, TNFRSF19, ABHD12B, LINC03040, SECTM1, ANO1. How are these genes connected and which molecular functions are altered?
Do not repeat functions of single genes.
Only return the helpful answer. Answer must be concise, detailed and well explained.
Helpful answer:
The dysregulated genes in endothelial cells in kidney include RHBG, RTN4, TCF4, C2CD4A, H4C14, ANO10, ACVR1C, BEST1, DCXR, CTXN1, H4C2, LGR5, STAR, ANO6, ANO9, APOL4, TNFRSF19, ABHD12B, LINC03040, SECTM1, and ANO1. These genes are connected through their involvement in various molecular functions such as cell adhesion, ion transport, signal transduction, and metabolic processes. The dysregulation of these genes may lead to alterations in the structure and function of endothelial cells, which can contribute to kidney diseases.
EC_COV_AKI_mod_71
Use the following pieces of information to answer the user's question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Question: The following genes are dysregulated in endothelial cells in kidney: RSAD2, OAS3, IFI6, SAMHD1, HERC6, MAVS, IRF2, DDX60, IFI44, IFIT5, ISG15, IFI44L, IFITM2, HLA-E, IFITM3, TRIM25, BIRC3, IP6K2, KLRD1, SP110, IFI27L2, ISG20, IL12B, BIRC2, GBP1, ADAR, IFI35, ZMPSTE24, IFIT3, VAMP3, PARP12, RNASEL, XAF1, HLA-F, IFI27L1, IRF3, VAPA, GBP2, HLA-C, LAMTOR5, IRF7, IFIT2, MX1, MX2, SAMD9L, HLA-B, OAS2, OAS1, IFI27, IFIT1, IFIH1, OASL, HLA-A, BST2. How are these genes connected and which molecular functions are altered?
Do not repeat functions of single genes.
Only return the helpful answer. Answer must be concise, detailed and well explained.
Helpful answer:
These genes are involved in various biological processes such as innate immune response, antiviral defense, interferon signaling pathway, protein processing, endoplasmic reticulum stress response, apoptosis, and cellular metabolism. They are also associated with different diseases including cancer, autoimmune disorders, and viral infections. The genes are connected through their involvement in the same biological processes and molecular functions.
EC_COV_AKI_mod_46
Use the following pieces of information to answer the user's question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Question: The following genes are dysregulated in endothelial cells in kidney: NBN, CAMKMT, PPM1B, BARD1, SLC3A1, WRN, RBBP8, BEX1, SSBP2, MAP3K7, PAX8, LDB2, RAD51B, GEN1, LHX1, LMO2, SSBP3. How are these genes connected and which molecular functions are altered?
Do not repeat functions of single genes.
Only return the helpful answer. Answer must be concise, detailed and well explained.
Helpful answer:
The dysregulated genes in endothelial cells in kidney are involved in various biological processes such as DNA repair, cell cycle regulation, protein phosphorylation, chromatin remodeling, transcriptional regulation, and apoptosis. These genes are connected through their involvement in these common pathways.
EC_COV_AKI_mod_74
Use the following pieces of information to answer the user's question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Question: The following genes are dysregulated in endothelial cells in kidney: SCTR, DMRT2, VIPR1, NXF3, H2AC11, GIMAP8, GIMAP4, GNG2, LILRB1, TRIB2, DMRT1, CUZD1, ADM2, GNB1, VWDE, RAMP2, WASL, ABHD11, HOXA3, AP1G2, MDP1, CALCR, CALM1, TAC1, VGF, GIMAP5, HOXB3, BSND, MZT2B, TCF12, LHX2, TBX5, GABRB3, LY86, RUBCNL, RAMP3, UBALD2, ATG5, CHI3L1, MNX1, CCDC28B, CALCRL, EMC9, GAP43, CALCB, PTMA, TRAF4, HEPHL1. How are these genes connected and which molecular functions are altered?
Do not repeat functions of single genes.
Only return the helpful answer. Answer must be concise, detailed and well explained.
Helpful answer:
The dysregulated genes in endothelial cells in kidney are involved in various biological processes such as cellular signaling, transcription regulation, protein transport, and metabolism. These genes are connected through their involvement in the same pathways or molecular functions. For example, SCTR, DMRT2, VIPR1, NXF3, H2AC11, GIMAP8, GIMAP4, GNG2, LILRB1, TRIB2, DMRT1, CUZD1, ADM2, GNB1, VWDE, RAMP2, WASL, ABHD11, HOXA3, AP1G2, MDP1, CALCR, CALM1, TAC1, VGF, GIMAP5, HOXB3, BSND, MZT2B, TCF12, LHX2, TBX5, GABRB3, LY86, RUBCNL, RAMP3, UBALD2, ATG5, CHI3L1, MNX1, CCDC28B, CALCRL, EMC9, GAP43, CALCB, PTMA, TRAF4, HEPHL1.
EC_COV_AKI_mod_148
Use the following pieces of information to answer the user's question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Question: The following genes are dysregulated in endothelial cells in kidney: ADRB2, HAVCR2, ARRB2, CEACAM1, NFKBIE, PRKCD, TLR2, FASN, CXCL8, POLDIP2. How are these genes connected and which molecular functions are altered?
Do not repeat functions of single genes.
Only return the helpful answer. Answer must be concise, detailed and well explained.
Helpful answer:
The dysregulated genes in endothelial cells in kidney are involved in various biological processes such as regulation of cellular response to stress, regulation of cell proliferation, regulation of apoptosis, regulation of immune system, regulation of inflammatory response, regulation of cell adhesion and migration. These genes are connected through their involvement in these biological processes.
EC_COV_AKI_mod_156
Use the following pieces of information to answer the user's question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Question: The following genes are dysregulated in endothelial cells in kidney: ELMO1, RAPGEF3, AKAP5, SCN8A, CAMP, RAB3A, TRPV6, CALM3, AK3, MYO10, ADK, SPTBN2, AMPD3, MYLK, DRD2, KCNQ1, PDE3B, PDE1A, TBK1, RIC1, UNC13B, PDE1C, DOCK1, RAC2, PKIB, SLFN12, ATP2B4, GRM7, PDE3A. How are these genes connected and which molecular functions are altered?
Do not repeat functions of single genes.
Only return the helpful answer. Answer must be concise, detailed and well explained.
Helpful answer:
These genes are dysregulated in endothelial cells in kidney, indicating that they may play a role in the development or progression of kidney disease. The functions of these genes are diverse and include regulation of cell signaling, ion transport, metabolism, and gene expression. Some of the genes are involved in the regulation of calcium homeostasis, such as TRPV6, ATP2B4, and CALM3. Others are involved in the regulation of cAMP levels, such as CAMP, PDE1A, PDE3A, and PDE3B. The dysregulation of these genes may lead to disruptions in calcium homeostasis and cAMP signaling, which can contribute to the development or progression of kidney disease.
EC_COV_AKI_mod_172
Use the following pieces of information to answer the user's question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Question: The following genes are dysregulated in endothelial cells in kidney: ACSL3, ACOX1, HTATIP2, RPS6KB1, ACADS, RXRB, ACSL1, ACSM2B, ELOVL6, ACSL4, ACSM2A, ELOVL1, SH3GLB1, AMMECR1, ACADVL, HADHA, EEF2K, MRE11. How are these genes connected and which molecular functions are altered?
Do not repeat functions of single genes.
Only return the helpful answer. Answer must be concise, detailed and well explained.
Helpful answer:
These genes are involved in various biological processes such as fatty acid metabolism, lipid biosynthesis, protein processing, and cellular signaling. The dysregulation of these genes may lead to the development of kidney diseases such as diabetic nephropathy, hypertensive nephropathy, and chronic kidney disease.
EC_COV_AKI_mod_3
Use the following pieces of information to answer the user's question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Question: The following genes are dysregulated in endothelial cells in kidney: ANK2, PACSIN2, EHD3, FDPS, RAB11FIP2, SERPINE1, EPS15, EHD1, YY1, RANBP10, MYOF, HDLBP, MAX, VPS45, EHBP1, TLN2. How are these genes connected and which molecular functions are altered?
Do not repeat functions of single genes.
Only return the helpful answer. Answer must be concise, detailed and well explained.
Helpful answer:
The dysregulated genes in endothelial cells in kidney are involved in various biological processes such as cellular component organization or biogenesis, protein transport, vesicle-mediated transport, protein localization, and protein binding. These genes are also associated with different molecular functions including GTPase activity, protein kinase activity, protein phosphatase activity, and transporter activity. The dysregulation of these genes may lead to alterations in the structure and function of endothelial cells, which can contribute to kidney diseases such as diabetic nephropathy, hypertensive nephropathy, and chronic kidney disease.
EC_COV_AKI_mod_108
Use the following pieces of information to answer the user's question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Question: The following genes are dysregulated in endothelial cells in kidney: HPGD, CLK2, ARHGAP31, BTBD7, ESRP2, ACAN, COL2A1, ZEB2, CLDN4, ZBTB17, OXTR, MMP17, ITGB4, BRCA2, SNAI2, CXADR, CAPN1, CLDN1, PRSS8, ALPL, CDH17, MEOX2, FOXA1, LPAR2, MTA3, TCF15. How are these genes connected and which molecular functions are altered?
Do not repeat functions of single genes.
Only return the helpful answer. Answer must be concise, detailed and well explained.
Helpful answer:
These genes are dysregulated in endothelial cells in kidney, indicating that they may play a role in the development or progression of kidney disease. The specific molecular functions altered by these gene dysregulations are not clear from this information alone. However, some of these genes have been previously implicated in various biological processes such as cell adhesion, extracellular matrix organization, and regulation of gene expression. For example, HPGD is involved in the metabolism of prostaglandins, which play important roles in regulating inflammation and pain. CLK2 is a kinase that regulates alternative splicing of pre-mRNA. ARHGAP31 is a GTPase that interacts with actin filaments and regulates cell adhesion and migration. BTBD7 is a transcriptional repressor that binds to DNA and recruits histone deacetylases. ESRP2 is an RNA-binding protein that regulates alternative splicing of pre-mRNA. ACAN and COL2A1 are components of the extracellular matrix, which provides structural support for cells and tissues. ZEB2 is a transcription factor that represses the expression of genes involved in epithelial differentiation. CLDN4 is a tight junction protein that regulates cell adhesion and permeability. OXTR is a neuropeptide receptor that plays important roles in social bonding and maternal behavior. MMP17 is an enzyme that degrades extracellular matrix proteins. ITGB4 is an integrin subunit that binds to extracellular matrix proteins and regulates cell adhesion and migration. BRCA2 is a tumor suppressor gene that plays important roles in DNA repair. SNAI2 is a transcription factor that represses the expression of genes involved in epithelial differentiation. CXADR is an adhesion molecule that interacts with integrins and regulates cell adhesion and migration. CAPN1 is a protease that degrades extracellular matrix proteins. CLDN1 is a tight junction protein that regulates cell adhesion and permeability. PRSS8 is an enzyme that hydrolyzes trypsin inhibitors. ALPL is an enzyme that catalyzes the hydrolysis of phosphate esters. CDH17 is a cadherin that interacts with other cadherins and regulates cell adhesion. MEOX2 is a transcription factor that regulates embryonic development. FOXA1 is a transcription factor that plays important roles in organ development and differentiation. LPAR2 is a G protein-coupled receptor that binds to lysophosphatidic acid and regulates cell adhesion, migration, and proliferation. MTA3 is a histone chaperone that interacts with DNA and regulates chromatin structure. TCF15 is a transcription factor that regulates gene expression in response to Wnt signaling.
EC_COV_AKI_mod_114
Use the following pieces of information to answer the user's question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Question: The following genes are dysregulated in endothelial cells in kidney: CDK13, KSR2, CACNA1C, NOS1AP, HAP1, PPP3CB, GNG5, CDK17, CACNB2, GNB4, RARB, ZNF804A, CACNB4, GNG11, PPP1R1B, MYL12A, CATSPER2, GNG7, GNG10, CACNA1D, KALRN, NOS1, PPP1CB, CACNA2D1, BIN1, ATP1A1, RCAN2, GUK1, PPP3CA, PPP3CC. How are these genes connected and which molecular functions are altered?
Do not repeat functions of single genes.
Only return the helpful answer. Answer must be concise, detailed and well explained.
Helpful answer:
These genes are dysregulated in endothelial cells in kidney, indicating that they may play a role in the development or progression of kidney disease. The specific molecular functions altered by these gene dysregulations are not known, but some potential connections can be made based on their biological roles. For example, CDK13 and KSR2 are involved in cell cycle regulation, CACNA1C and NOS1AP are involved in calcium signaling, RARB is involved in hormone receptor activity, ZNF804A is involved in transcription factor activity, and PPP3CB is involved in protein phosphorylation. These genes may work together to regulate various cellular processes that are important for maintaining kidney function.
EC_COV_AKI_mod_33
Use the following pieces of information to answer the user's question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Question: The following genes are dysregulated in endothelial cells in kidney: AFMID, CCNC, MED13, WDR5, ESRRB, IBSP, ACADM, BGLAP, MED27, DNASE1, NRIP1, MED23, MED4, MED14, CDK19, RIMKLB, MED15, PNRC2, ATF6, THRAP3, PCK1, ESRRG, CREBZF, ENO1, PPARGC1B, SUZ12. How are these genes connected and which molecular functions are altered?
Do not repeat functions of single genes.
Only return the helpful answer. Answer must be concise, detailed and well explained.
Helpful answer:
These genes are dysregulated in endothelial cells in kidney, indicating that they may play a role in the development or progression of kidney disease. The genes are connected through their involvement in various molecular functions such as transcription regulation, chromatin modification, and metabolic processes. Specifically, these genes are involved in the regulation of gene expression through the binding of transcription factors to DNA, the modification of histones to alter chromatin structure, and the regulation of cellular metabolism. The dysregulation of these genes may lead to changes in gene expression patterns that contribute to the development or progression of kidney disease.
EC_COV_AKI_mod_120
Use the following pieces of information to answer the user's question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Question: The following genes are dysregulated in endothelial cells in kidney: FOXN3, WEE1, STMN1, PAK1, KCNIP2, KCND3, KCNIP4, MEN1, KCND1, KCNIP1, KCND2. How are these genes connected and which molecular functions are altered?
Do not repeat functions of single genes.
Only return the helpful answer. Answer must be concise, detailed and well explained.
Helpful answer:
The dysregulated genes in endothelial cells in kidney are involved in various biological processes such as cell cycle regulation, ion transport, signal transduction, and protein phosphorylation. FOXN3 is a transcription factor that regulates the expression of genes involved in cell cycle regulation and differentiation. WEE1 is also a transcription factor that regulates the cell cycle by inhibiting CDK1 activity. STMN1 is a protein that regulates microtubule dynamics, which are important for cell division and migration. PAK1 is a serine/threonine kinase that regulates various signaling pathways involved in cell proliferation, differentiation, and survival. KCNIP2, KCND3, KCND1, and KCND2 are voltage-gated potassium channels that regulate ion transport across the plasma membrane. KCND1 is also involved in signal transduction. KCND2 is involved in cell proliferation and differentiation. KCNIP4 is a protein that regulates the activity of voltage-gated potassium channels. MEN1 is a tumor suppressor gene that regulates various signaling pathways involved in cell proliferation, differentiation, and survival.
EC_COV_AKI_mod_29
Use the following pieces of information to answer the user's question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Question: The following genes are dysregulated in endothelial cells in kidney: NRG1, PAX2, PRKCB, CADPS2, ADGRL1, ERBB2, CDC37, ATP2C1, NRG4, ERBB3, DTNBP1, CHD7, ADAM17, DOCK7, AUTS2, HRAS, NRAS, PSEN1, GRIN1, SORT1, PIK3C3, NRG3, NRG2. How are these genes connected and which molecular functions are altered?
Do not repeat functions of single genes.
Only return the helpful answer. Answer must be concise, detailed and well explained.
Helpful answer:
The dysregulated genes in endothelial cells in kidney are involved in various molecular functions such as cell adhesion, signal transduction, protein binding, and ion transport. These genes are connected through different pathways including the ErbB signaling pathway, Wnt signaling pathway, MAPK signaling pathway, and PI3K-Akt signaling pathway. The ErbB signaling pathway is involved in cell growth, proliferation, and survival. The Wnt signaling pathway is involved in cell differentiation, proliferation, and apoptosis. The MAPK signaling pathway is involved in cell proliferation, differentiation, and apoptosis. The PI3K-Akt signaling pathway is involved in cell growth, proliferation, and survival.
EC_H_CKD_mod_14
Use the following pieces of information to answer the user's question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Question: The following genes are dysregulated in endothelial cells in kidney: PDZK1, LTB, KCNJ15, ITSN1, CLCNKB, MTOR, OXSR1, SLC4A4, KCNJ10, TRPC3, KCNJ16, SLC4A2, WNK3, KCNJ1, BSND, SLC9A3, SLC12A1, PRKAA1, SLC12A4, AQP2, CLCNKA, NFAT5, WNK4, SGK3. How are these genes connected and which molecular functions are altered?
Do not repeat functions of single genes.
Only return the helpful answer. Answer must be concise, detailed and well explained.
Helpful answer:
[6]:
dcolors = {"AKI": "red", "COV_AKI": "orange", "H_CKD": "green", "DKD": "blue"}
tlda.plot_module_description(nrow=3, figsize=(20,7), dcolors=dcolors)
plt.savefig("kpmp_community_overview.png")
6 3 18 16
[ ]:
[19]:
kg
[19]:
KGraph KGraph with 111032 nodes and 1617389 edges
[22]:
kg.plot_node_types(show_threshold=0.01)
[ ]:
[52]:
len(all_modalities) //3
[52]:
5
[58]:
import matplotlib.pyplot as plt
all_modalities = set(adata.obs[celltype_col])
fig, axs = plt.subplots(3,int(np.ceil(len(all_modalities) / 3)), figsize=(21, 14))
faxs = axs.flatten()
for gi, group in enumerate(sorted(all_modalities)):
sc.pl.umap(adata[adata.obs[celltype_col] == group], color=celltype_col, ax=faxs[gi], show=False, size=1)
faxs[gi].set_title(group)
for i in range(gi+1, len(faxs)):
faxs[i].set_visible(False)
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/scanpy/plotting/_utils.py:471: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/scanpy/plotting/_utils.py:471: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/scanpy/plotting/_utils.py:471: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/scanpy/plotting/_utils.py:471: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/scanpy/plotting/_utils.py:471: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/scanpy/plotting/_utils.py:471: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/scanpy/plotting/_utils.py:471: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/scanpy/plotting/_utils.py:471: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/scanpy/plotting/_utils.py:471: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/scanpy/plotting/_utils.py:471: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/scanpy/plotting/_utils.py:471: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/scanpy/plotting/_utils.py:471: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/scanpy/plotting/_utils.py:471: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/scanpy/plotting/_utils.py:471: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/scanpy/plotting/_utils.py:471: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/scanpy/plotting/_utils.py:471: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
[98]:
adata.obs[adata.obs[celltype_col] == "DTL"]["mycelltypes"]
[98]:
index
KB1_AAACGCTGTGGTTCTA kidney loop of Henle thin descending limb epit...
KB1_AAAGGGCCACACACGC kidney loop of Henle thin descending limb epit...
KB1_AAAGGGCTCGCATTGA kidney loop of Henle thin descending limb epit...
KB1_AAAGGTAAGCACACAG kidney loop of Henle thin descending limb epit...
KB1_AAATGGACAGTGGTGA kidney loop of Henle thin descending limb epit...
...
KBCVD4_TATTGCTCATCGTCCT-1 kidney loop of Henle thin descending limb epit...
KBCVD4_TATTGGGAGCTTCATG-1 kidney loop of Henle thin descending limb epit...
KBCVD4_TCAAGCAAGTGGATTA-1 kidney loop of Henle thin descending limb epit...
KBCVD4_TCACGGGTCTCACCCA-1 kidney loop of Henle thin descending limb epit...
KBCVD4_TGAGCATAGAGGCCAT-1 kidney loop of Henle thin descending limb epit...
Name: mycelltypes, Length: 7968, dtype: category
Categories (26, object): ['endothelial cell', 'podocyte', 'epithelial cell of proximal tubule', 'neural cell', ..., 'kidney interstitial alternatively activated m..., 'conventional dendritic cell', 'non-classical monocyte', 'lymphocyte']
[90]:
ukg = kg.kg.to_undirected()
[ ]:
for gene in ["SCARA5", "COL1A1", "POSTN", "FN1", "RUNX2", "MEOX1", "TEAD4", "COL1A1", "NFATC4", "STAT3", "HSF2", "RELA", "BACH1", "GLI2", "RELB", "TEAD1", "TGFB1", "TBX20", "KLF2", "KLF4", "ATF4", "SP2", "SP3", "FOXP2", "MEF2C", "ETV6", "NFIA"]:
#for gene in ["TWIST2", "LHX9", "MKX", "PRRX1", "PRRX2", "MSX1", "TWIST1", "OSR1", "CITED2", "RUNX1T1", "FOXD1", "TBX3", "NFIX", "HOXB5", "HOXC8", "MEIS2", "HOXC6", "HOXC4"]:
#for gene in ["HIF1A"]:
targetNodes = [x for x in nx.ego_graph(kg.kg, gene, radius=1, center=True, undirected=False, distance=None).nodes]
hasDrug = False
hasDisease = False
hasGeneset = False
for node in targetNodes:
ntypes = kg.node_types(node)
if "drug" in ntypes:
hasDrug = True
if "disease" in ntypes:
hasDisease = True
if "geneset" in ntypes:
hasGeneset = True
print(gene, len(targetNodes), hasDrug , hasDisease , hasGeneset)
#if len(targetNodes) < 200:
# targetKG = kg.subset_kg(targetNodes)
# _=targetKG.plot_graph(figsize=(10,10))
[ ]:
targetNodes = [x for x in nx.ego_graph(kg.kg, "BACH1", radius=1, center=True, undirected=False, distance=None).nodes]
print(len(targetNodes))
targetKG = kg.subset_kg(targetNodes)
_=targetKG.plot_graph(figsize=(10,10))
[15]:
kg.kg.nodes["NRG3"]
[15]:
{'type': {'gene'}, 'source': 'STRING', 'name': 'NRG3', 'score': 0}
[12]:
kg.kg.edges[('NFAT5', 'LTB')]
[12]:
{'type': 'activates',
'source': 'omnipath',
'omnipath_evidences': ['CollecTRI:11485737', 'DoRothEA:11485737'],
'omnipath_type': 'transcriptional'}
[17]:
kg.kg.in_edges("NRG3")
[17]:
InEdgeDataView([('MAPK1', 'NRG3'), ('CDC37', 'NRG3'), ('PTPN12', 'NRG3'), ('KRAS', 'NRG3'), ('MAPK3', 'NRG3'), ('ERBB3', 'NRG3'), ('EGFR', 'NRG3'), ('PRKCB', 'NRG3'), ('ADAM17', 'NRG3'), ('PSEN1', 'NRG3'), ('HSP90AA1', 'NRG3'), ('ERBB4', 'NRG3'), ('AUTS2', 'NRG3'), ('NRG2', 'NRG3'), ('NRAS', 'NRG3'), ('NRG4', 'NRG3'), ('NRG1', 'NRG3'), ('CADPS2', 'NRG3'), ('HRAS', 'NRG3'), ('PRKCA', 'NRG3'), ('ERBB2IP', 'NRG3'), ('YES1', 'NRG3'), ('FOXA1', 'NRG3'), ('GATA2', 'NRG3'), ('MITF', 'NRG3'), ('AR', 'NRG3'), ('FOXP3', 'NRG3')])
[32]:
mc = ModuleCompare()
[78]:
allSigKG = dict(tlda.cellgroupdata["EC"]["communities_enhanced"], **tlda.cellgroupdata["FIB"]["communities_enhanced"],)
[81]:
jaccardSims = mc.network_compare_modules(allSigKG, measure="jaccard", borderWeightQuantile=0.99)
[82]:
mc.plot_dendrogram(jaccardSims, figsize=(16, 4), color_threshold=0.9)
[83]:
ns = NETSIM(kg)
[95]:
modNetSims_all = mc.network_compare_netsim(allSigKG, ns=ns, max_terms=2, borderWeightQuantile=0.99)
|#########################################################| 100% Time: 1:23:5213
[96]:
mc.plot_dendrogram(modNetSims_all, figsize=(18,4))
[97]:
emb = mc.module_pca(fibKGs, kg)
[ ]:
[ ]:
%%capture captured_plot_module_comparisons
tlda.plot_module_comparisons(plot_communities=True)
[ ]:
captured_plot_module_comparisons()
[ ]:
[ ]:
[ ]:
[ ]:
[ ]: