myocardial_picaso_analysis

%load_ext autoreload

import os, sys
import networkx as nx

sys.path.insert(0, "../")
sys.path.insert(0, "/home/j/joppich/.local/lib/python3.11/site-packages/")
#otherwise leidenalg is used in an outdated version ...

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

la 0.10.2

kg = KGraph()
kg.load_kgraph("../data/initial_base_graph.out")

kg

KGraph KGraph with 111032 nodes and 1617389 edges

exprKGs = None
exprKGs = pickle.load(open("myocardial_exprkgs.pickle", "rb"))

tissue2zone2kg = defaultdict(lambda : dict())
for x in exprKGs:
    tissue, zone = x.split("_")

    tissue2zone2kg[tissue]["{}_{}".format(tissue, zone)] = exprKGs[x]

for x in tissue2zone2kg:
    print(x, len(tissue2zone2kg[x]), [x for x in tissue2zone2kg[x]])

Adipocyte 5 ['Adipocyte_BZ', 'Adipocyte_CTRL', 'Adipocyte_FZ', 'Adipocyte_IZ', 'Adipocyte_RZ']
Cardiomyocyte 5 ['Cardiomyocyte_BZ', 'Cardiomyocyte_CTRL', 'Cardiomyocyte_FZ', 'Cardiomyocyte_IZ', 'Cardiomyocyte_RZ']
Cycling cells 5 ['Cycling cells_BZ', 'Cycling cells_CTRL', 'Cycling cells_FZ', 'Cycling cells_IZ', 'Cycling cells_RZ']
Endothelial 5 ['Endothelial_BZ', 'Endothelial_CTRL', 'Endothelial_FZ', 'Endothelial_IZ', 'Endothelial_RZ']
Fibroblast 5 ['Fibroblast_BZ', 'Fibroblast_CTRL', 'Fibroblast_FZ', 'Fibroblast_IZ', 'Fibroblast_RZ']
Lymphoid 5 ['Lymphoid_BZ', 'Lymphoid_CTRL', 'Lymphoid_FZ', 'Lymphoid_IZ', 'Lymphoid_RZ']
Mast 5 ['Mast_BZ', 'Mast_CTRL', 'Mast_FZ', 'Mast_IZ', 'Mast_RZ']
Myeloid 5 ['Myeloid_BZ', 'Myeloid_CTRL', 'Myeloid_FZ', 'Myeloid_IZ', 'Myeloid_RZ']
Neuronal 5 ['Neuronal_BZ', 'Neuronal_CTRL', 'Neuronal_FZ', 'Neuronal_IZ', 'Neuronal_RZ']
Pericyte 5 ['Pericyte_BZ', 'Pericyte_CTRL', 'Pericyte_FZ', 'Pericyte_IZ', 'Pericyte_RZ']
vSMCs 5 ['vSMCs_BZ', 'vSMCs_CTRL', 'vSMCs_FZ', 'vSMCs_IZ', 'vSMCs_RZ']

zoneSort = {
        "CTRL": 0,
        "RZ": 1,
        "BZ": 2,
        "IZ": 3,
        "FZ": 4
    }

tlda = TwoLevelDifferentialAnalysis(tissue2zone2kg, [x for x in zoneSort], output_folder_formatter="diff_{}/", fullKG=kg)

[(x, type(tlda.__dict__[x])) for x in tlda.__dict__]

[('tldict', dict),
 ('sorted_zones', list),
 ('name_sep', str),
 ('cellgroupdata', mikg.kgraph.DefaultDict),
 ('fullKG', mikg.kgraph.KGraph),
 ('output_folder_formatter', str),
 ('recalc_warning', bool)]

#%%capture captured_plot_module_calc
tlda.calculate_modules(relevant_cellgroups=None, reference_formatter="{}_CTRL")

Adipocyte

/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

{'Adipocyte_RZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Adipocyte_BZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Adipocyte_IZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Adipocyte_FZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing Adipocyte_RZ
Identified communities
Number of communities: 279
Average community size 41.053763440860216
Median community size 32.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.   20.   32.   53.5 317. ]
Significant communities
Number of communities: 4
Average community size 23.0
Median community size 23.0
Quantile (0,0.25,0.5,0.75,1) community size [14.  15.5 23.  30.5 32. ]
Adipocyte_RZ_mod_254 16
Adipocyte_RZ_mod_252 32
Adipocyte_RZ_mod_218 14
Adipocyte_RZ_mod_37 30
Number of saved communities: 0
Analysing Adipocyte_BZ
Identified communities
Number of communities: 270
Average community size 33.662962962962965
Median community size 27.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.    16.    27.    43.75 161.  ]
Significant communities
Number of communities: 11
Average community size 38.90909090909091
Median community size 24.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10.   18.   24.   37.5 161. ]
Adipocyte_BZ_mod_216 19
Adipocyte_BZ_mod_74 161
Adipocyte_BZ_mod_156 14
Adipocyte_BZ_mod_242 10
Adipocyte_BZ_mod_226 27
Adipocyte_BZ_mod_62 63
Adipocyte_BZ_mod_146 26
Adipocyte_BZ_mod_194 18
Adipocyte_BZ_mod_71 48
Adipocyte_BZ_mod_228 18
Adipocyte_BZ_mod_231 24
Number of saved communities: 3
Analysing Adipocyte_IZ
Identified communities
Number of communities: 289
Average community size 31.42560553633218
Median community size 24.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.  15.  24.  40. 193.]
Significant communities
Number of communities: 13
Average community size 24.53846153846154
Median community size 22.0
Quantile (0,0.25,0.5,0.75,1) community size [10. 18. 22. 34. 42.]
Adipocyte_IZ_mod_227 18
Adipocyte_IZ_mod_161 35
Adipocyte_IZ_mod_287 10
Adipocyte_IZ_mod_193 22
Adipocyte_IZ_mod_84 12
Adipocyte_IZ_mod_49 12
Adipocyte_IZ_mod_151 20
Adipocyte_IZ_mod_125 34
Adipocyte_IZ_mod_112 42
Adipocyte_IZ_mod_131 28
Adipocyte_IZ_mod_72 39
Adipocyte_IZ_mod_133 25
Adipocyte_IZ_mod_80 22
Number of saved communities: 3
Analysing Adipocyte_FZ
Identified communities
Number of communities: 275
Average community size 40.85454545454545
Median community size 31.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.  17.  31.  54. 364.]
Significant communities
Number of communities: 7
Average community size 54.42857142857143
Median community size 37.0
Quantile (0,0.25,0.5,0.75,1) community size [ 12.   19.5  37.   83.5 126. ]
Adipocyte_FZ_mod_99 12
Adipocyte_FZ_mod_11 126
Adipocyte_FZ_mod_192 88
Adipocyte_FZ_mod_173 12
Adipocyte_FZ_mod_71 79
Adipocyte_FZ_mod_100 37
Adipocyte_FZ_mod_212 27
Number of saved communities: 1
Cardiomyocyte
{'Cardiomyocyte_RZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Cardiomyocyte_BZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Cardiomyocyte_IZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Cardiomyocyte_FZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing Cardiomyocyte_RZ
Identified communities
Number of communities: 230
Average community size 12.091304347826087
Median community size 9.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.   2.   9.  16. 154.]
Significant communities
Number of communities: 3
Average community size 18.0
Median community size 13.0
Quantile (0,0.25,0.5,0.75,1) community size [12.  12.5 13.  21.  29. ]
Cardiomyocyte_RZ_mod_97 29
Cardiomyocyte_RZ_mod_162 12
Cardiomyocyte_RZ_mod_52 13
Number of saved communities: 1
Analysing Cardiomyocyte_BZ
Identified communities
Number of communities: 234
Average community size 22.95299145299145
Median community size 19.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.   9.  19.  32. 128.]
Significant communities
Number of communities: 29
Average community size 28.103448275862068
Median community size 21.0
Quantile (0,0.25,0.5,0.75,1) community size [ 12.  16.  21.  35. 105.]
Cardiomyocyte_BZ_mod_109 33
Cardiomyocyte_BZ_mod_173 12
Cardiomyocyte_BZ_mod_115 17
Cardiomyocyte_BZ_mod_97 12
Cardiomyocyte_BZ_mod_165 35
Cardiomyocyte_BZ_mod_160 16
Cardiomyocyte_BZ_mod_25 51
Cardiomyocyte_BZ_mod_89 26
Cardiomyocyte_BZ_mod_167 16
Cardiomyocyte_BZ_mod_119 15
Cardiomyocyte_BZ_mod_90 38
Cardiomyocyte_BZ_mod_182 18
Cardiomyocyte_BZ_mod_174 13
Cardiomyocyte_BZ_mod_204 23
Cardiomyocyte_BZ_mod_158 13
Cardiomyocyte_BZ_mod_193 29
Cardiomyocyte_BZ_mod_72 18
Cardiomyocyte_BZ_mod_148 20
Cardiomyocyte_BZ_mod_66 53
Cardiomyocyte_BZ_mod_136 18
Cardiomyocyte_BZ_mod_110 12
Cardiomyocyte_BZ_mod_36 39
Cardiomyocyte_BZ_mod_184 22
Cardiomyocyte_BZ_mod_138 47
Cardiomyocyte_BZ_mod_86 55
Cardiomyocyte_BZ_mod_139 16
Cardiomyocyte_BZ_mod_150 21
Cardiomyocyte_BZ_mod_132 22
Cardiomyocyte_BZ_mod_50 105
Number of saved communities: 1
Analysing Cardiomyocyte_IZ
Identified communities
Number of communities: 263
Average community size 28.41444866920152
Median community size 22.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.   13.   22.   36.5 274. ]
Significant communities
Number of communities: 109
Average community size 28.65137614678899
Median community size 22.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10.  15.  22.  32. 183.]
Cardiomyocyte_IZ_mod_98 46
Cardiomyocyte_IZ_mod_5 26
Cardiomyocyte_IZ_mod_62 64
Cardiomyocyte_IZ_mod_72 22
Cardiomyocyte_IZ_mod_115 10
Cardiomyocyte_IZ_mod_27 19
Cardiomyocyte_IZ_mod_31 31
Cardiomyocyte_IZ_mod_84 60
Cardiomyocyte_IZ_mod_185 51
Cardiomyocyte_IZ_mod_225 19
Cardiomyocyte_IZ_mod_16 19
Cardiomyocyte_IZ_mod_49 19
Cardiomyocyte_IZ_mod_189 30
Cardiomyocyte_IZ_mod_148 31
Cardiomyocyte_IZ_mod_151 13
Cardiomyocyte_IZ_mod_45 15
Cardiomyocyte_IZ_mod_154 10
Cardiomyocyte_IZ_mod_76 14
Cardiomyocyte_IZ_mod_37 20
Cardiomyocyte_IZ_mod_175 10
Cardiomyocyte_IZ_mod_229 14
Cardiomyocyte_IZ_mod_79 17
Cardiomyocyte_IZ_mod_32 15
Cardiomyocyte_IZ_mod_243 19
Cardiomyocyte_IZ_mod_174 20
Cardiomyocyte_IZ_mod_23 24
Cardiomyocyte_IZ_mod_113 17
Cardiomyocyte_IZ_mod_180 16
Cardiomyocyte_IZ_mod_209 29
Cardiomyocyte_IZ_mod_160 47
Cardiomyocyte_IZ_mod_127 22
Cardiomyocyte_IZ_mod_139 25
Cardiomyocyte_IZ_mod_204 10
Cardiomyocyte_IZ_mod_181 40
Cardiomyocyte_IZ_mod_153 10
Cardiomyocyte_IZ_mod_196 25
Cardiomyocyte_IZ_mod_87 45
Cardiomyocyte_IZ_mod_239 10
Cardiomyocyte_IZ_mod_95 46
Cardiomyocyte_IZ_mod_94 30
Cardiomyocyte_IZ_mod_54 23
Cardiomyocyte_IZ_mod_197 14
Cardiomyocyte_IZ_mod_80 53
Cardiomyocyte_IZ_mod_53 23
Cardiomyocyte_IZ_mod_15 21
Cardiomyocyte_IZ_mod_155 11
Cardiomyocyte_IZ_mod_215 17
Cardiomyocyte_IZ_mod_28 15
Cardiomyocyte_IZ_mod_216 10
Cardiomyocyte_IZ_mod_178 19
Cardiomyocyte_IZ_mod_19 183
Cardiomyocyte_IZ_mod_55 34
Cardiomyocyte_IZ_mod_107 24
Cardiomyocyte_IZ_mod_242 13
Cardiomyocyte_IZ_mod_81 138
Cardiomyocyte_IZ_mod_234 13
Cardiomyocyte_IZ_mod_106 24
Cardiomyocyte_IZ_mod_170 13
Cardiomyocyte_IZ_mod_21 29
Cardiomyocyte_IZ_mod_40 20
Cardiomyocyte_IZ_mod_163 38
Cardiomyocyte_IZ_mod_14 119
Cardiomyocyte_IZ_mod_126 34
Cardiomyocyte_IZ_mod_124 16
Cardiomyocyte_IZ_mod_171 42
Cardiomyocyte_IZ_mod_111 28
Cardiomyocyte_IZ_mod_99 18
Cardiomyocyte_IZ_mod_250 10
Cardiomyocyte_IZ_mod_211 22
Cardiomyocyte_IZ_mod_219 13
Cardiomyocyte_IZ_mod_177 10
Cardiomyocyte_IZ_mod_165 21
Cardiomyocyte_IZ_mod_12 45
Cardiomyocyte_IZ_mod_236 19
Cardiomyocyte_IZ_mod_237 14
Cardiomyocyte_IZ_mod_143 23
Cardiomyocyte_IZ_mod_100 25
Cardiomyocyte_IZ_mod_82 19
Cardiomyocyte_IZ_mod_103 13
Cardiomyocyte_IZ_mod_190 14
Cardiomyocyte_IZ_mod_182 24
Cardiomyocyte_IZ_mod_56 19
Cardiomyocyte_IZ_mod_109 15
Cardiomyocyte_IZ_mod_123 41
Cardiomyocyte_IZ_mod_218 36
Cardiomyocyte_IZ_mod_213 13
Cardiomyocyte_IZ_mod_172 25
Cardiomyocyte_IZ_mod_179 22
Cardiomyocyte_IZ_mod_122 13
Cardiomyocyte_IZ_mod_125 30
Cardiomyocyte_IZ_mod_128 48
Cardiomyocyte_IZ_mod_22 27
Cardiomyocyte_IZ_mod_41 21
Cardiomyocyte_IZ_mod_169 32
Cardiomyocyte_IZ_mod_149 24
Cardiomyocyte_IZ_mod_102 18
Cardiomyocyte_IZ_mod_158 77
Cardiomyocyte_IZ_mod_217 10
Cardiomyocyte_IZ_mod_120 50
Cardiomyocyte_IZ_mod_25 18
Cardiomyocyte_IZ_mod_1 56
Cardiomyocyte_IZ_mod_29 17
Cardiomyocyte_IZ_mod_129 48
Cardiomyocyte_IZ_mod_20 40
Cardiomyocyte_IZ_mod_220 30
Cardiomyocyte_IZ_mod_222 19
Cardiomyocyte_IZ_mod_42 51
Cardiomyocyte_IZ_mod_69 34
Cardiomyocyte_IZ_mod_119 15
Number of saved communities: 15
Analysing Cardiomyocyte_FZ
Identified communities
Number of communities: 247
Average community size 28.05668016194332
Median community size 21.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.   12.   21.   34.5 238. ]
Significant communities
Number of communities: 84
Average community size 33.416666666666664
Median community size 23.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10.  16.  23.  37. 156.]
Cardiomyocyte_FZ_mod_59 18
Cardiomyocyte_FZ_mod_130 26
Cardiomyocyte_FZ_mod_145 20
Cardiomyocyte_FZ_mod_151 17
Cardiomyocyte_FZ_mod_19 44
Cardiomyocyte_FZ_mod_125 16
Cardiomyocyte_FZ_mod_5 23
Cardiomyocyte_FZ_mod_75 35
Cardiomyocyte_FZ_mod_56 16
Cardiomyocyte_FZ_mod_126 98
Cardiomyocyte_FZ_mod_4 12
Cardiomyocyte_FZ_mod_156 16
Cardiomyocyte_FZ_mod_204 13
Cardiomyocyte_FZ_mod_72 37
Cardiomyocyte_FZ_mod_181 21
Cardiomyocyte_FZ_mod_184 24
Cardiomyocyte_FZ_mod_99 58
Cardiomyocyte_FZ_mod_157 36
Cardiomyocyte_FZ_mod_103 10
Cardiomyocyte_FZ_mod_67 71
Cardiomyocyte_FZ_mod_16 27
Cardiomyocyte_FZ_mod_83 12
Cardiomyocyte_FZ_mod_1 156
Cardiomyocyte_FZ_mod_77 67
Cardiomyocyte_FZ_mod_86 33
Cardiomyocyte_FZ_mod_0 16
Cardiomyocyte_FZ_mod_41 21
Cardiomyocyte_FZ_mod_78 35
Cardiomyocyte_FZ_mod_218 19
Cardiomyocyte_FZ_mod_101 61
Cardiomyocyte_FZ_mod_133 120
Cardiomyocyte_FZ_mod_223 20
Cardiomyocyte_FZ_mod_240 10
Cardiomyocyte_FZ_mod_169 21
Cardiomyocyte_FZ_mod_88 49
Cardiomyocyte_FZ_mod_139 65
Cardiomyocyte_FZ_mod_160 12
Cardiomyocyte_FZ_mod_98 17
Cardiomyocyte_FZ_mod_208 31
Cardiomyocyte_FZ_mod_144 10
Cardiomyocyte_FZ_mod_25 33
Cardiomyocyte_FZ_mod_66 109
Cardiomyocyte_FZ_mod_39 20
Cardiomyocyte_FZ_mod_51 39
Cardiomyocyte_FZ_mod_6 91
Cardiomyocyte_FZ_mod_15 30
Cardiomyocyte_FZ_mod_201 11
Cardiomyocyte_FZ_mod_148 27
Cardiomyocyte_FZ_mod_113 18
Cardiomyocyte_FZ_mod_81 19
Cardiomyocyte_FZ_mod_185 18
Cardiomyocyte_FZ_mod_187 12
Cardiomyocyte_FZ_mod_106 35
Cardiomyocyte_FZ_mod_178 14
Cardiomyocyte_FZ_mod_38 17
Cardiomyocyte_FZ_mod_85 65
Cardiomyocyte_FZ_mod_74 15
Cardiomyocyte_FZ_mod_21 97
Cardiomyocyte_FZ_mod_57 21
Cardiomyocyte_FZ_mod_35 20
Cardiomyocyte_FZ_mod_93 70
Cardiomyocyte_FZ_mod_189 14
Cardiomyocyte_FZ_mod_109 16
Cardiomyocyte_FZ_mod_20 38
Cardiomyocyte_FZ_mod_232 12
Cardiomyocyte_FZ_mod_213 19
Cardiomyocyte_FZ_mod_53 37
Cardiomyocyte_FZ_mod_54 62
Cardiomyocyte_FZ_mod_63 39
Cardiomyocyte_FZ_mod_36 54
Cardiomyocyte_FZ_mod_124 16
Cardiomyocyte_FZ_mod_150 34
Cardiomyocyte_FZ_mod_117 24
Cardiomyocyte_FZ_mod_90 23
Cardiomyocyte_FZ_mod_119 27
Cardiomyocyte_FZ_mod_64 26
Cardiomyocyte_FZ_mod_219 18
Cardiomyocyte_FZ_mod_82 14
Cardiomyocyte_FZ_mod_217 15
Cardiomyocyte_FZ_mod_129 34
Cardiomyocyte_FZ_mod_52 18
Cardiomyocyte_FZ_mod_192 11
Cardiomyocyte_FZ_mod_33 32
Cardiomyocyte_FZ_mod_220 30
Number of saved communities: 21
Cycling cells
{'Cycling cells_RZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Cycling cells_BZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Cycling cells_IZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Cycling cells_FZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing Cycling cells_RZ
Identified communities
Number of communities: 272
Average community size 24.727941176470587
Median community size 19.5
Quantile (0,0.25,0.5,0.75,1) community size [  2.   11.   19.5  32.  319. ]
Significant communities
Number of communities: 46
Average community size 33.869565217391305
Median community size 28.5
Quantile (0,0.25,0.5,0.75,1) community size [10.   18.25 28.5  38.75 91.  ]
Cycling cells_RZ_mod_153 19
Cycling cells_RZ_mod_16 24
Cycling cells_RZ_mod_248 30
Cycling cells_RZ_mod_38 76
Cycling cells_RZ_mod_59 26
Cycling cells_RZ_mod_156 38
Cycling cells_RZ_mod_65 57
Cycling cells_RZ_mod_21 91
Cycling cells_RZ_mod_4 33
Cycling cells_RZ_mod_157 18
Cycling cells_RZ_mod_189 12
Cycling cells_RZ_mod_52 12
Cycling cells_RZ_mod_39 50
Cycling cells_RZ_mod_120 13
Cycling cells_RZ_mod_6 10
Cycling cells_RZ_mod_212 22
Cycling cells_RZ_mod_198 26
Cycling cells_RZ_mod_13 39
Cycling cells_RZ_mod_50 85
Cycling cells_RZ_mod_60 30
Cycling cells_RZ_mod_158 27
Cycling cells_RZ_mod_175 18
Cycling cells_RZ_mod_47 24
Cycling cells_RZ_mod_148 27
Cycling cells_RZ_mod_25 30
Cycling cells_RZ_mod_239 16
Cycling cells_RZ_mod_94 61
Cycling cells_RZ_mod_245 15
Cycling cells_RZ_mod_154 18
Cycling cells_RZ_mod_1 38
Cycling cells_RZ_mod_105 61
Cycling cells_RZ_mod_130 29
Cycling cells_RZ_mod_9 69
Cycling cells_RZ_mod_172 31
Cycling cells_RZ_mod_53 28
Cycling cells_RZ_mod_46 48
Cycling cells_RZ_mod_76 73
Cycling cells_RZ_mod_149 11
Cycling cells_RZ_mod_244 18
Cycling cells_RZ_mod_12 33
Cycling cells_RZ_mod_30 39
Cycling cells_RZ_mod_78 38
Cycling cells_RZ_mod_211 17
Cycling cells_RZ_mod_116 25
Cycling cells_RZ_mod_168 30
Cycling cells_RZ_mod_221 23
Number of saved communities: 1
Analysing Cycling cells_BZ
Identified communities
Number of communities: 268
Average community size 27.96641791044776
Median community size 21.5
Quantile (0,0.25,0.5,0.75,1) community size [  2.   12.   21.5  33.  366. ]
Significant communities
Number of communities: 24
Average community size 33.0
Median community size 27.0
Quantile (0,0.25,0.5,0.75,1) community size [13.   17.75 27.   41.75 88.  ]
Cycling cells_BZ_mod_138 19
Cycling cells_BZ_mod_13 83
Cycling cells_BZ_mod_199 39
Cycling cells_BZ_mod_19 88
Cycling cells_BZ_mod_206 17
Cycling cells_BZ_mod_202 33
Cycling cells_BZ_mod_134 15
Cycling cells_BZ_mod_132 54
Cycling cells_BZ_mod_45 50
Cycling cells_BZ_mod_11 60
Cycling cells_BZ_mod_135 16
Cycling cells_BZ_mod_204 22
Cycling cells_BZ_mod_225 18
Cycling cells_BZ_mod_198 14
Cycling cells_BZ_mod_80 28
Cycling cells_BZ_mod_22 28
Cycling cells_BZ_mod_44 51
Cycling cells_BZ_mod_23 26
Cycling cells_BZ_mod_102 31
Cycling cells_BZ_mod_29 13
Cycling cells_BZ_mod_218 14
Cycling cells_BZ_mod_227 19
Cycling cells_BZ_mod_121 22
Cycling cells_BZ_mod_191 32
Number of saved communities: 6
Analysing Cycling cells_IZ
Identified communities
Number of communities: 269
Average community size 22.57992565055762
Median community size 17.0
Quantile (0,0.25,0.5,0.75,1) community size [  1.   8.  17.  30. 289.]
Significant communities
Number of communities: 9
Average community size 18.444444444444443
Median community size 17.0
Quantile (0,0.25,0.5,0.75,1) community size [11. 13. 17. 22. 30.]
Cycling cells_IZ_mod_25 17
Cycling cells_IZ_mod_140 22
Cycling cells_IZ_mod_41 19
Cycling cells_IZ_mod_174 16
Cycling cells_IZ_mod_184 13
Cycling cells_IZ_mod_102 27
Cycling cells_IZ_mod_229 11
Cycling cells_IZ_mod_228 11
Cycling cells_IZ_mod_35 30
Number of saved communities: 2
Analysing Cycling cells_FZ
Identified communities
Number of communities: 276
Average community size 30.42753623188406
Median community size 24.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.    15.    24.    35.25 345.  ]
Significant communities
Number of communities: 40
Average community size 53.25
Median community size 29.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10.    20.75  29.    54.75 345.  ]
Cycling cells_FZ_mod_245 10
Cycling cells_FZ_mod_17 345
Cycling cells_FZ_mod_23 146
Cycling cells_FZ_mod_102 23
Cycling cells_FZ_mod_7 135
Cycling cells_FZ_mod_56 110
Cycling cells_FZ_mod_13 167
Cycling cells_FZ_mod_3 184
Cycling cells_FZ_mod_258 17
Cycling cells_FZ_mod_247 10
Cycling cells_FZ_mod_203 41
Cycling cells_FZ_mod_111 19
Cycling cells_FZ_mod_97 22
Cycling cells_FZ_mod_86 13
Cycling cells_FZ_mod_117 25
Cycling cells_FZ_mod_228 19
Cycling cells_FZ_mod_213 12
Cycling cells_FZ_mod_10 36
Cycling cells_FZ_mod_200 10
Cycling cells_FZ_mod_179 37
Cycling cells_FZ_mod_2 26
Cycling cells_FZ_mod_53 29
Cycling cells_FZ_mod_69 21
Cycling cells_FZ_mod_106 28
Cycling cells_FZ_mod_118 63
Cycling cells_FZ_mod_170 67
Cycling cells_FZ_mod_119 22
Cycling cells_FZ_mod_80 27
Cycling cells_FZ_mod_33 44
Cycling cells_FZ_mod_186 54
Cycling cells_FZ_mod_165 57
Cycling cells_FZ_mod_98 39
Cycling cells_FZ_mod_88 44
Cycling cells_FZ_mod_167 18
Cycling cells_FZ_mod_158 20
Cycling cells_FZ_mod_0 59
Cycling cells_FZ_mod_59 29
Cycling cells_FZ_mod_223 26
Cycling cells_FZ_mod_151 29
Cycling cells_FZ_mod_58 47
Number of saved communities: 7
Endothelial
{'Endothelial_RZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Endothelial_BZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Endothelial_IZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Endothelial_FZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing Endothelial_RZ
Identified communities
Number of communities: 207
Average community size 7.067632850241546
Median community size 4.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2.  2.  4.  9. 43.]
Significant communities
Number of communities: 1
Average community size 14.0
Median community size 14.0
Quantile (0,0.25,0.5,0.75,1) community size [14. 14. 14. 14. 14.]
Endothelial_RZ_mod_159 14
Number of saved communities: 0
Analysing Endothelial_BZ
Identified communities
Number of communities: 200
Average community size 7.33
Median community size 4.5
Quantile (0,0.25,0.5,0.75,1) community size [ 2.   2.   4.5 10.  38. ]
Significant communities
Number of communities: 7
Average community size 16.142857142857142
Median community size 13.0
Quantile (0,0.25,0.5,0.75,1) community size [10. 11. 13. 19. 30.]
Endothelial_BZ_mod_42 30
Endothelial_BZ_mod_109 11
Endothelial_BZ_mod_38 11
Endothelial_BZ_mod_60 14
Endothelial_BZ_mod_32 24
Endothelial_BZ_mod_30 13
Endothelial_BZ_mod_24 10
Number of saved communities: 1
Analysing Endothelial_IZ
Identified communities
Number of communities: 247
Average community size 25.295546558704455
Median community size 20.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.  12.  20.  30. 280.]
Significant communities
Number of communities: 74
Average community size 30.37837837837838
Median community size 21.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10.    15.    21.    31.75 156.  ]
Endothelial_IZ_mod_96 15
Endothelial_IZ_mod_69 27
Endothelial_IZ_mod_29 30
Endothelial_IZ_mod_78 44
Endothelial_IZ_mod_79 11
Endothelial_IZ_mod_190 16
Endothelial_IZ_mod_220 11
Endothelial_IZ_mod_43 22
Endothelial_IZ_mod_64 28
Endothelial_IZ_mod_66 13
Endothelial_IZ_mod_72 156
Endothelial_IZ_mod_70 80
Endothelial_IZ_mod_184 14
Endothelial_IZ_mod_128 20
Endothelial_IZ_mod_206 32
Endothelial_IZ_mod_198 12
Endothelial_IZ_mod_97 53
Endothelial_IZ_mod_134 43
Endothelial_IZ_mod_102 15
Endothelial_IZ_mod_213 12
Endothelial_IZ_mod_144 22
Endothelial_IZ_mod_129 19
Endothelial_IZ_mod_166 24
Endothelial_IZ_mod_118 13
Endothelial_IZ_mod_1 58
Endothelial_IZ_mod_142 31
Endothelial_IZ_mod_131 20
Endothelial_IZ_mod_55 33
Endothelial_IZ_mod_53 38
Endothelial_IZ_mod_65 17
Endothelial_IZ_mod_127 14
Endothelial_IZ_mod_11 129
Endothelial_IZ_mod_204 15
Endothelial_IZ_mod_185 42
Endothelial_IZ_mod_48 27
Endothelial_IZ_mod_165 20
Endothelial_IZ_mod_169 12
Endothelial_IZ_mod_121 22
Endothelial_IZ_mod_116 17
Endothelial_IZ_mod_50 13
Endothelial_IZ_mod_47 28
Endothelial_IZ_mod_41 57
Endothelial_IZ_mod_208 18
Endothelial_IZ_mod_49 31
Endothelial_IZ_mod_80 23
Endothelial_IZ_mod_60 19
Endothelial_IZ_mod_42 23
Endothelial_IZ_mod_163 18
Endothelial_IZ_mod_46 17
Endothelial_IZ_mod_120 16
Endothelial_IZ_mod_170 26
Endothelial_IZ_mod_141 21
Endothelial_IZ_mod_3 111
Endothelial_IZ_mod_193 18
Endothelial_IZ_mod_110 63
Endothelial_IZ_mod_200 20
Endothelial_IZ_mod_106 65
Endothelial_IZ_mod_167 31
Endothelial_IZ_mod_19 17
Endothelial_IZ_mod_172 13
Endothelial_IZ_mod_24 35
Endothelial_IZ_mod_182 19
Endothelial_IZ_mod_107 45
Endothelial_IZ_mod_45 15
Endothelial_IZ_mod_130 21
Endothelial_IZ_mod_203 13
Endothelial_IZ_mod_108 36
Endothelial_IZ_mod_13 87
Endothelial_IZ_mod_38 10
Endothelial_IZ_mod_56 28
Endothelial_IZ_mod_186 14
Endothelial_IZ_mod_58 15
Endothelial_IZ_mod_92 23
Endothelial_IZ_mod_202 12
Number of saved communities: 13
Analysing Endothelial_FZ
Identified communities
Number of communities: 344
Average community size 13.622093023255815
Median community size 9.0
Quantile (0,0.25,0.5,0.75,1) community size [  1.   1.   9.  19. 305.]
Significant communities
Number of communities: 30
Average community size 24.4
Median community size 19.0
Quantile (0,0.25,0.5,0.75,1) community size [10.  13.  19.  27.5 91. ]
Endothelial_FZ_mod_193 11
Endothelial_FZ_mod_40 41
Endothelial_FZ_mod_282 12
Endothelial_FZ_mod_105 70
Endothelial_FZ_mod_85 29
Endothelial_FZ_mod_122 17
Endothelial_FZ_mod_227 11
Endothelial_FZ_mod_134 10
Endothelial_FZ_mod_78 20
Endothelial_FZ_mod_72 13
Endothelial_FZ_mod_43 91
Endothelial_FZ_mod_76 19
Endothelial_FZ_mod_26 26
Endothelial_FZ_mod_60 15
Endothelial_FZ_mod_243 11
Endothelial_FZ_mod_47 28
Endothelial_FZ_mod_109 12
Endothelial_FZ_mod_30 24
Endothelial_FZ_mod_36 18
Endothelial_FZ_mod_111 21
Endothelial_FZ_mod_94 14
Endothelial_FZ_mod_88 37
Endothelial_FZ_mod_86 24
Endothelial_FZ_mod_119 14
Endothelial_FZ_mod_34 21
Endothelial_FZ_mod_246 11
Endothelial_FZ_mod_10 39
Endothelial_FZ_mod_19 13
Endothelial_FZ_mod_112 41
Endothelial_FZ_mod_83 19
Number of saved communities: 3
Fibroblast
{'Fibroblast_RZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Fibroblast_BZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Fibroblast_IZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Fibroblast_FZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing Fibroblast_RZ
Identified communities
Number of communities: 360
Average community size 8.177777777777777
Median community size 2.0
Quantile (0,0.25,0.5,0.75,1) community size [  1.   1.   2.  12. 206.]
Significant communities
Number of communities: 13
Average community size 32.23076923076923
Median community size 20.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10.  13.  20.  22. 206.]
Fibroblast_RZ_mod_122 10
Fibroblast_RZ_mod_65 26
Fibroblast_RZ_mod_108 14
Fibroblast_RZ_mod_95 26
Fibroblast_RZ_mod_5 22
Fibroblast_RZ_mod_72 13
Fibroblast_RZ_mod_48 20
Fibroblast_RZ_mod_110 20
Fibroblast_RZ_mod_182 12
Fibroblast_RZ_mod_131 22
Fibroblast_RZ_mod_174 10
Fibroblast_RZ_mod_29 18
Fibroblast_RZ_mod_2 206
Number of saved communities: 2
Analysing Fibroblast_BZ
Identified communities
Number of communities: 234
Average community size 13.405982905982906
Median community size 10.5
Quantile (0,0.25,0.5,0.75,1) community size [  2.     3.    10.5   17.75 109.  ]
Significant communities
Number of communities: 9
Average community size 22.333333333333332
Median community size 19.0
Quantile (0,0.25,0.5,0.75,1) community size [10. 13. 19. 29. 45.]
Fibroblast_BZ_mod_33 13
Fibroblast_BZ_mod_18 34
Fibroblast_BZ_mod_71 29
Fibroblast_BZ_mod_19 11
Fibroblast_BZ_mod_128 45
Fibroblast_BZ_mod_81 19
Fibroblast_BZ_mod_34 27
Fibroblast_BZ_mod_60 13
Fibroblast_BZ_mod_106 10
Number of saved communities: 0
Analysing Fibroblast_IZ
Identified communities
Number of communities: 256
Average community size 26.36328125
Median community size 19.5
Quantile (0,0.25,0.5,0.75,1) community size [  2.   12.   19.5  33.  326. ]
Significant communities
Number of communities: 75
Average community size 33.81333333333333
Median community size 23.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10.   15.5  23.   34.  326. ]
Fibroblast_IZ_mod_24 23
Fibroblast_IZ_mod_90 15
Fibroblast_IZ_mod_41 10
Fibroblast_IZ_mod_192 31
Fibroblast_IZ_mod_62 19
Fibroblast_IZ_mod_55 14
Fibroblast_IZ_mod_140 10
Fibroblast_IZ_mod_240 14
Fibroblast_IZ_mod_137 10
Fibroblast_IZ_mod_173 27
Fibroblast_IZ_mod_144 12
Fibroblast_IZ_mod_194 11
Fibroblast_IZ_mod_53 15
Fibroblast_IZ_mod_146 10
Fibroblast_IZ_mod_103 20
Fibroblast_IZ_mod_224 15
Fibroblast_IZ_mod_28 40
Fibroblast_IZ_mod_147 21
Fibroblast_IZ_mod_64 37
Fibroblast_IZ_mod_128 74
Fibroblast_IZ_mod_247 12
Fibroblast_IZ_mod_143 14
Fibroblast_IZ_mod_97 20
Fibroblast_IZ_mod_170 20
Fibroblast_IZ_mod_127 33
Fibroblast_IZ_mod_6 37
Fibroblast_IZ_mod_30 21
Fibroblast_IZ_mod_35 326
Fibroblast_IZ_mod_121 14
Fibroblast_IZ_mod_162 40
Fibroblast_IZ_mod_81 42
Fibroblast_IZ_mod_182 24
Fibroblast_IZ_mod_156 55
Fibroblast_IZ_mod_12 60
Fibroblast_IZ_mod_20 34
Fibroblast_IZ_mod_232 20
Fibroblast_IZ_mod_183 33
Fibroblast_IZ_mod_207 23
Fibroblast_IZ_mod_31 55
Fibroblast_IZ_mod_210 16
Fibroblast_IZ_mod_217 16
Fibroblast_IZ_mod_118 15
Fibroblast_IZ_mod_177 50
Fibroblast_IZ_mod_10 26
Fibroblast_IZ_mod_214 18
Fibroblast_IZ_mod_37 12
Fibroblast_IZ_mod_154 31
Fibroblast_IZ_mod_235 20
Fibroblast_IZ_mod_63 21
Fibroblast_IZ_mod_124 14
Fibroblast_IZ_mod_65 34
Fibroblast_IZ_mod_116 18
Fibroblast_IZ_mod_89 26
Fibroblast_IZ_mod_100 24
Fibroblast_IZ_mod_200 14
Fibroblast_IZ_mod_8 27
Fibroblast_IZ_mod_46 43
Fibroblast_IZ_mod_102 26
Fibroblast_IZ_mod_36 16
Fibroblast_IZ_mod_3 202
Fibroblast_IZ_mod_166 45
Fibroblast_IZ_mod_105 13
Fibroblast_IZ_mod_231 20
Fibroblast_IZ_mod_75 40
Fibroblast_IZ_mod_45 66
Fibroblast_IZ_mod_178 24
Fibroblast_IZ_mod_4 144
Fibroblast_IZ_mod_15 27
Fibroblast_IZ_mod_51 24
Fibroblast_IZ_mod_190 24
Fibroblast_IZ_mod_132 16
Fibroblast_IZ_mod_1 19
Fibroblast_IZ_mod_72 17
Fibroblast_IZ_mod_78 54
Fibroblast_IZ_mod_167 23
Number of saved communities: 18
Analysing Fibroblast_FZ
Identified communities
Number of communities: 248
Average community size 22.056451612903224
Median community size 18.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.   8.  18.  28. 363.]
Significant communities
Number of communities: 34
Average community size 33.0
Median community size 27.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10.    17.    27.    37.75 101.  ]
Fibroblast_FZ_mod_81 16
Fibroblast_FZ_mod_7 81
Fibroblast_FZ_mod_43 101
Fibroblast_FZ_mod_64 12
Fibroblast_FZ_mod_2 15
Fibroblast_FZ_mod_86 31
Fibroblast_FZ_mod_19 88
Fibroblast_FZ_mod_53 17
Fibroblast_FZ_mod_170 10
Fibroblast_FZ_mod_167 12
Fibroblast_FZ_mod_37 32
Fibroblast_FZ_mod_172 39
Fibroblast_FZ_mod_149 27
Fibroblast_FZ_mod_159 38
Fibroblast_FZ_mod_38 16
Fibroblast_FZ_mod_47 70
Fibroblast_FZ_mod_146 24
Fibroblast_FZ_mod_82 16
Fibroblast_FZ_mod_200 12
Fibroblast_FZ_mod_49 40
Fibroblast_FZ_mod_139 31
Fibroblast_FZ_mod_34 18
Fibroblast_FZ_mod_186 22
Fibroblast_FZ_mod_105 18
Fibroblast_FZ_mod_10 68
Fibroblast_FZ_mod_121 17
Fibroblast_FZ_mod_98 21
Fibroblast_FZ_mod_119 25
Fibroblast_FZ_mod_158 33
Fibroblast_FZ_mod_55 27
Fibroblast_FZ_mod_91 43
Fibroblast_FZ_mod_48 29
Fibroblast_FZ_mod_151 37
Fibroblast_FZ_mod_4 36
Number of saved communities: 8
Lymphoid
{'Lymphoid_RZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Lymphoid_BZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Lymphoid_IZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Lymphoid_FZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing Lymphoid_RZ
Identified communities
Number of communities: 234
Average community size 15.32905982905983
Median community size 12.5
Quantile (0,0.25,0.5,0.75,1) community size [  2.     3.    12.5   19.75 182.  ]
Significant communities
Number of communities: 10
Average community size 26.7
Median community size 22.5
Quantile (0,0.25,0.5,0.75,1) community size [10.   20.25 22.5  35.25 50.  ]
Lymphoid_RZ_mod_68 50
Lymphoid_RZ_mod_143 21
Lymphoid_RZ_mod_108 21
Lymphoid_RZ_mod_152 10
Lymphoid_RZ_mod_12 20
Lymphoid_RZ_mod_32 37
Lymphoid_RZ_mod_138 16
Lymphoid_RZ_mod_140 38
Lymphoid_RZ_mod_180 24
Lymphoid_RZ_mod_127 30
Number of saved communities: 1
Analysing Lymphoid_BZ
Identified communities
Number of communities: 256
Average community size 10.62109375
Median community size 7.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.   2.   7.  14. 138.]
Significant communities
Number of communities: 2
Average community size 24.5
Median community size 24.5
Quantile (0,0.25,0.5,0.75,1) community size [16.   20.25 24.5  28.75 33.  ]
Lymphoid_BZ_mod_240 33
Lymphoid_BZ_mod_10 16
Number of saved communities: 0
Analysing Lymphoid_IZ
Identified communities
Number of communities: 274
Average community size 24.71167883211679
Median community size 20.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.    11.    20.    29.75 256.  ]
Significant communities
Number of communities: 38
Average community size 25.394736842105264
Median community size 18.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10.    13.25  18.    28.   146.  ]
Lymphoid_IZ_mod_234 18
Lymphoid_IZ_mod_222 28
Lymphoid_IZ_mod_23 21
Lymphoid_IZ_mod_192 22
Lymphoid_IZ_mod_207 13
Lymphoid_IZ_mod_60 146
Lymphoid_IZ_mod_58 17
Lymphoid_IZ_mod_1 73
Lymphoid_IZ_mod_16 37
Lymphoid_IZ_mod_236 10
Lymphoid_IZ_mod_103 14
Lymphoid_IZ_mod_155 19
Lymphoid_IZ_mod_173 14
Lymphoid_IZ_mod_200 37
Lymphoid_IZ_mod_78 13
Lymphoid_IZ_mod_238 15
Lymphoid_IZ_mod_144 12
Lymphoid_IZ_mod_151 18
Lymphoid_IZ_mod_8 50
Lymphoid_IZ_mod_35 14
Lymphoid_IZ_mod_232 18
Lymphoid_IZ_mod_210 12
Lymphoid_IZ_mod_150 35
Lymphoid_IZ_mod_98 15
Lymphoid_IZ_mod_132 28
Lymphoid_IZ_mod_215 20
Lymphoid_IZ_mod_170 35
Lymphoid_IZ_mod_2 11
Lymphoid_IZ_mod_250 10
Lymphoid_IZ_mod_163 17
Lymphoid_IZ_mod_27 16
Lymphoid_IZ_mod_211 12
Lymphoid_IZ_mod_68 18
Lymphoid_IZ_mod_11 36
Lymphoid_IZ_mod_181 28
Lymphoid_IZ_mod_29 40
Lymphoid_IZ_mod_175 13
Lymphoid_IZ_mod_133 10
Number of saved communities: 11
Analysing Lymphoid_FZ
Identified communities
Number of communities: 263
Average community size 26.372623574144487
Median community size 20.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.  12.  20.  30. 295.]
Significant communities
Number of communities: 57
Average community size 36.666666666666664
Median community size 27.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10.  18.  27.  42. 253.]
Lymphoid_FZ_mod_7 16
Lymphoid_FZ_mod_58 17
Lymphoid_FZ_mod_233 18
Lymphoid_FZ_mod_90 46
Lymphoid_FZ_mod_204 32
Lymphoid_FZ_mod_145 12
Lymphoid_FZ_mod_133 20
Lymphoid_FZ_mod_56 24
Lymphoid_FZ_mod_2 20
Lymphoid_FZ_mod_213 10
Lymphoid_FZ_mod_23 70
Lymphoid_FZ_mod_152 11
Lymphoid_FZ_mod_148 22
Lymphoid_FZ_mod_30 19
Lymphoid_FZ_mod_102 39
Lymphoid_FZ_mod_238 13
Lymphoid_FZ_mod_138 18
Lymphoid_FZ_mod_53 68
Lymphoid_FZ_mod_13 23
Lymphoid_FZ_mod_122 44
Lymphoid_FZ_mod_244 24
Lymphoid_FZ_mod_54 39
Lymphoid_FZ_mod_24 76
Lymphoid_FZ_mod_143 25
Lymphoid_FZ_mod_154 68
Lymphoid_FZ_mod_159 28
Lymphoid_FZ_mod_150 37
Lymphoid_FZ_mod_73 66
Lymphoid_FZ_mod_109 30
Lymphoid_FZ_mod_258 33
Lymphoid_FZ_mod_131 46
Lymphoid_FZ_mod_186 13
Lymphoid_FZ_mod_118 49
Lymphoid_FZ_mod_127 22
Lymphoid_FZ_mod_107 44
Lymphoid_FZ_mod_165 33
Lymphoid_FZ_mod_34 121
Lymphoid_FZ_mod_31 28
Lymphoid_FZ_mod_46 31
Lymphoid_FZ_mod_71 78
Lymphoid_FZ_mod_76 24
Lymphoid_FZ_mod_200 10
Lymphoid_FZ_mod_232 30
Lymphoid_FZ_mod_153 42
Lymphoid_FZ_mod_113 60
Lymphoid_FZ_mod_237 25
Lymphoid_FZ_mod_199 16
Lymphoid_FZ_mod_136 33
Lymphoid_FZ_mod_247 22
Lymphoid_FZ_mod_85 13
Lymphoid_FZ_mod_126 27
Lymphoid_FZ_mod_139 19
Lymphoid_FZ_mod_94 18
Lymphoid_FZ_mod_4 35
Lymphoid_FZ_mod_158 14
Lymphoid_FZ_mod_9 253
Lymphoid_FZ_mod_179 16
Number of saved communities: 7
Mast
{'Mast_RZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Mast_BZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Mast_IZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Mast_FZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing Mast_RZ
Identified communities
Number of communities: 269
Average community size 28.13754646840149
Median community size 22.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.  13.  22.  35. 131.]
Significant communities
Number of communities: 18
Average community size 44.611111111111114
Median community size 27.0
Quantile (0,0.25,0.5,0.75,1) community size [ 13.   21.   27.   65.5 131. ]
Mast_RZ_mod_25 73
Mast_RZ_mod_254 21
Mast_RZ_mod_66 43
Mast_RZ_mod_47 103
Mast_RZ_mod_31 131
Mast_RZ_mod_132 28
Mast_RZ_mod_139 25
Mast_RZ_mod_86 84
Mast_RZ_mod_78 81
Mast_RZ_mod_159 20
Mast_RZ_mod_242 14
Mast_RZ_mod_4 13
Mast_RZ_mod_155 19
Mast_RZ_mod_201 41
Mast_RZ_mod_215 21
Mast_RZ_mod_8 26
Mast_RZ_mod_212 26
Mast_RZ_mod_177 34
Number of saved communities: 2
Analysing Mast_BZ
Identified communities
Number of communities: 274
Average community size 29.463503649635037
Median community size 23.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.  14.  23.  39. 176.]
Significant communities
Number of communities: 24
Average community size 27.708333333333332
Median community size 21.5
Quantile (0,0.25,0.5,0.75,1) community size [10.   14.75 21.5  35.75 87.  ]
Mast_BZ_mod_137 41
Mast_BZ_mod_52 41
Mast_BZ_mod_220 17
Mast_BZ_mod_158 14
Mast_BZ_mod_114 15
Mast_BZ_mod_219 18
Mast_BZ_mod_104 11
Mast_BZ_mod_77 34
Mast_BZ_mod_181 10
Mast_BZ_mod_118 18
Mast_BZ_mod_207 10
Mast_BZ_mod_85 27
Mast_BZ_mod_209 10
Mast_BZ_mod_140 20
Mast_BZ_mod_22 44
Mast_BZ_mod_145 42
Mast_BZ_mod_7 62
Mast_BZ_mod_216 13
Mast_BZ_mod_51 18
Mast_BZ_mod_174 24
Mast_BZ_mod_149 23
Mast_BZ_mod_36 34
Mast_BZ_mod_102 32
Mast_BZ_mod_10 87
Number of saved communities: 3
Analysing Mast_IZ
Identified communities
Number of communities: 276
Average community size 28.644927536231883
Median community size 22.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.  14.  22.  36. 268.]
Significant communities
Number of communities: 18
Average community size 25.333333333333332
Median community size 18.5
Quantile (0,0.25,0.5,0.75,1) community size [ 11.    14.25  18.5   22.   139.  ]
Mast_IZ_mod_4 28
Mast_IZ_mod_163 11
Mast_IZ_mod_115 17
Mast_IZ_mod_44 15
Mast_IZ_mod_41 11
Mast_IZ_mod_74 14
Mast_IZ_mod_218 23
Mast_IZ_mod_126 20
Mast_IZ_mod_131 15
Mast_IZ_mod_250 12
Mast_IZ_mod_40 139
Mast_IZ_mod_116 38
Mast_IZ_mod_248 12
Mast_IZ_mod_138 22
Mast_IZ_mod_246 20
Mast_IZ_mod_221 21
Mast_IZ_mod_212 16
Mast_IZ_mod_152 22
Number of saved communities: 5
Analysing Mast_FZ
Identified communities
Number of communities: 279
Average community size 34.65232974910394
Median community size 27.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.   16.5  27.   45.  274. ]
Significant communities
Number of communities: 58
Average community size 47.206896551724135
Median community size 37.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10.  21.  37.  58. 180.]
Mast_FZ_mod_235 19
Mast_FZ_mod_219 20
Mast_FZ_mod_275 23
Mast_FZ_mod_124 19
Mast_FZ_mod_76 12
Mast_FZ_mod_50 180
Mast_FZ_mod_66 49
Mast_FZ_mod_109 39
Mast_FZ_mod_227 11
Mast_FZ_mod_64 23
Mast_FZ_mod_182 33
Mast_FZ_mod_129 119
Mast_FZ_mod_173 35
Mast_FZ_mod_171 29
Mast_FZ_mod_110 26
Mast_FZ_mod_244 21
Mast_FZ_mod_22 55
Mast_FZ_mod_137 15
Mast_FZ_mod_177 71
Mast_FZ_mod_136 28
Mast_FZ_mod_264 21
Mast_FZ_mod_89 25
Mast_FZ_mod_80 17
Mast_FZ_mod_14 159
Mast_FZ_mod_260 29
Mast_FZ_mod_183 107
Mast_FZ_mod_239 14
Mast_FZ_mod_59 10
Mast_FZ_mod_222 15
Mast_FZ_mod_226 38
Mast_FZ_mod_133 34
Mast_FZ_mod_68 50
Mast_FZ_mod_197 28
Mast_FZ_mod_190 53
Mast_FZ_mod_148 18
Mast_FZ_mod_131 39
Mast_FZ_mod_87 25
Mast_FZ_mod_86 96
Mast_FZ_mod_15 78
Mast_FZ_mod_194 14
Mast_FZ_mod_5 82
Mast_FZ_mod_144 79
Mast_FZ_mod_128 58
Mast_FZ_mod_31 48
Mast_FZ_mod_176 36
Mast_FZ_mod_152 48
Mast_FZ_mod_154 58
Mast_FZ_mod_48 64
Mast_FZ_mod_78 45
Mast_FZ_mod_32 18
Mast_FZ_mod_58 90
Mast_FZ_mod_193 56
Mast_FZ_mod_81 85
Mast_FZ_mod_26 17
Mast_FZ_mod_113 46
Mast_FZ_mod_46 69
Mast_FZ_mod_72 54
Mast_FZ_mod_60 88
Number of saved communities: 27
Myeloid
{'Myeloid_RZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Myeloid_BZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Myeloid_IZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Myeloid_FZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing Myeloid_RZ
Identified communities
Number of communities: 232
Average community size 11.267241379310345
Median community size 7.5
Quantile (0,0.25,0.5,0.75,1) community size [  2.    2.    7.5  15.  176. ]
Significant communities
Number of communities: 6
Average community size 18.0
Median community size 19.5
Quantile (0,0.25,0.5,0.75,1) community size [13.   15.25 19.5  20.75 21.  ]
Myeloid_RZ_mod_165 21
Myeloid_RZ_mod_47 19
Myeloid_RZ_mod_14 20
Myeloid_RZ_mod_84 21
Myeloid_RZ_mod_72 13
Myeloid_RZ_mod_39 14
Number of saved communities: 0
Analysing Myeloid_BZ
Identified communities
Number of communities: 220
Average community size 7.7727272727272725
Median community size 3.5
Quantile (0,0.25,0.5,0.75,1) community size [ 2.   2.   3.5 10.  71. ]
Significant communities
Number of communities: 2
Average community size 12.0
Median community size 12.0
Quantile (0,0.25,0.5,0.75,1) community size [12. 12. 12. 12. 12.]
Myeloid_BZ_mod_79 12
Myeloid_BZ_mod_26 12
Number of saved communities: 0
Analysing Myeloid_IZ
Identified communities
Number of communities: 241
Average community size 21.390041493775932
Median community size 18.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.   9.  18.  27. 220.]
Significant communities
Number of communities: 32
Average community size 27.5625
Median community size 20.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10.    15.75  20.    23.25 179.  ]
Myeloid_IZ_mod_118 55
Myeloid_IZ_mod_40 21
Myeloid_IZ_mod_147 10
Myeloid_IZ_mod_7 49
Myeloid_IZ_mod_219 12
Myeloid_IZ_mod_1 22
Myeloid_IZ_mod_167 19
Myeloid_IZ_mod_181 13
Myeloid_IZ_mod_143 20
Myeloid_IZ_mod_13 40
Myeloid_IZ_mod_172 16
Myeloid_IZ_mod_125 14
Myeloid_IZ_mod_152 22
Myeloid_IZ_mod_0 179
Myeloid_IZ_mod_98 48
Myeloid_IZ_mod_109 20
Myeloid_IZ_mod_84 19
Myeloid_IZ_mod_33 15
Myeloid_IZ_mod_120 23
Myeloid_IZ_mod_196 21
Myeloid_IZ_mod_90 14
Myeloid_IZ_mod_18 36
Myeloid_IZ_mod_175 19
Myeloid_IZ_mod_170 20
Myeloid_IZ_mod_96 23
Myeloid_IZ_mod_30 24
Myeloid_IZ_mod_82 12
Myeloid_IZ_mod_183 18
Myeloid_IZ_mod_123 15
Myeloid_IZ_mod_124 17
Myeloid_IZ_mod_78 16
Myeloid_IZ_mod_51 30
Number of saved communities: 3
Analysing Myeloid_FZ
Identified communities
Number of communities: 240
Average community size 26.895833333333332
Median community size 19.5
Quantile (0,0.25,0.5,0.75,1) community size [  2.   12.   19.5  31.  307. ]
Significant communities
Number of communities: 81
Average community size 30.604938271604937
Median community size 26.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10.  17.  26.  36. 145.]
Myeloid_FZ_mod_204 10
Myeloid_FZ_mod_193 21
Myeloid_FZ_mod_143 10
Myeloid_FZ_mod_71 35
Myeloid_FZ_mod_76 48
Myeloid_FZ_mod_134 47
Myeloid_FZ_mod_13 26
Myeloid_FZ_mod_89 17
Myeloid_FZ_mod_10 14
Myeloid_FZ_mod_70 36
Myeloid_FZ_mod_50 46
Myeloid_FZ_mod_160 17
Myeloid_FZ_mod_121 18
Myeloid_FZ_mod_82 26
Myeloid_FZ_mod_85 17
Myeloid_FZ_mod_44 67
Myeloid_FZ_mod_234 20
Myeloid_FZ_mod_173 26
Myeloid_FZ_mod_117 31
Myeloid_FZ_mod_25 36
Myeloid_FZ_mod_22 97
Myeloid_FZ_mod_215 38
Myeloid_FZ_mod_69 10
Myeloid_FZ_mod_34 15
Myeloid_FZ_mod_52 51
Myeloid_FZ_mod_148 21
Myeloid_FZ_mod_209 26
Myeloid_FZ_mod_140 19
Myeloid_FZ_mod_109 100
Myeloid_FZ_mod_51 79
Myeloid_FZ_mod_130 40
Myeloid_FZ_mod_73 26
Myeloid_FZ_mod_110 24
Myeloid_FZ_mod_129 53
Myeloid_FZ_mod_88 42
Myeloid_FZ_mod_57 24
Myeloid_FZ_mod_74 13
Myeloid_FZ_mod_1 145
Myeloid_FZ_mod_8 28
Myeloid_FZ_mod_111 13
Myeloid_FZ_mod_170 15
Myeloid_FZ_mod_26 11
Myeloid_FZ_mod_31 67
Myeloid_FZ_mod_40 23
Myeloid_FZ_mod_155 13
Myeloid_FZ_mod_59 18
Myeloid_FZ_mod_146 26
Myeloid_FZ_mod_142 16
Myeloid_FZ_mod_46 35
Myeloid_FZ_mod_132 37
Myeloid_FZ_mod_84 22
Myeloid_FZ_mod_99 12
Myeloid_FZ_mod_11 31
Myeloid_FZ_mod_213 14
Myeloid_FZ_mod_151 31
Myeloid_FZ_mod_171 16
Myeloid_FZ_mod_77 37
Myeloid_FZ_mod_126 30
Myeloid_FZ_mod_207 12
Myeloid_FZ_mod_149 11
Myeloid_FZ_mod_232 10
Myeloid_FZ_mod_220 14
Myeloid_FZ_mod_136 30
Myeloid_FZ_mod_112 26
Myeloid_FZ_mod_64 25
Myeloid_FZ_mod_66 27
Myeloid_FZ_mod_167 13
Myeloid_FZ_mod_12 48
Myeloid_FZ_mod_38 20
Myeloid_FZ_mod_154 23
Myeloid_FZ_mod_53 22
Myeloid_FZ_mod_176 31
Myeloid_FZ_mod_184 19
Myeloid_FZ_mod_168 17
Myeloid_FZ_mod_212 18
Myeloid_FZ_mod_153 36
Myeloid_FZ_mod_35 42
Myeloid_FZ_mod_81 42
Myeloid_FZ_mod_90 63
Myeloid_FZ_mod_201 16
Myeloid_FZ_mod_96 28
Number of saved communities: 18
Neuronal
{'Neuronal_RZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Neuronal_BZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Neuronal_IZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Neuronal_FZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing Neuronal_RZ
Identified communities
Number of communities: 264
Average community size 12.545454545454545
Median community size 10.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.   2.  10.  17. 128.]

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../mikg/kgraph.py:2203: RuntimeWarning: divide by zero encountered in scalar divide
  return (u1 - u2) / s

Significant communities
Number of communities: 8
Average community size 19.875
Median community size 17.5
Quantile (0,0.25,0.5,0.75,1) community size [11.  14.5 17.5 21.  39. ]
Neuronal_RZ_mod_116 11
Neuronal_RZ_mod_90 19
Neuronal_RZ_mod_105 39
Neuronal_RZ_mod_68 13
Neuronal_RZ_mod_58 17
Neuronal_RZ_mod_143 15
Neuronal_RZ_mod_80 18
Neuronal_RZ_mod_76 27
Number of saved communities: 0
Analysing Neuronal_BZ
Identified communities
Number of communities: 269
Average community size 14.364312267657992
Median community size 12.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.   3.  12.  19. 107.]
Significant communities
Number of communities: 16
Average community size 26.5
Median community size 18.5
Quantile (0,0.25,0.5,0.75,1) community size [10.   15.75 18.5  34.75 83.  ]
Neuronal_BZ_mod_216 18
Neuronal_BZ_mod_117 34
Neuronal_BZ_mod_199 17
Neuronal_BZ_mod_237 11
Neuronal_BZ_mod_66 37
Neuronal_BZ_mod_8 83
Neuronal_BZ_mod_65 12
Neuronal_BZ_mod_118 31
Neuronal_BZ_mod_63 41
Neuronal_BZ_mod_189 11
Neuronal_BZ_mod_156 18
Neuronal_BZ_mod_92 21
Neuronal_BZ_mod_215 10
Neuronal_BZ_mod_77 44
Neuronal_BZ_mod_32 19
Neuronal_BZ_mod_46 17
Number of saved communities: 0
Analysing Neuronal_IZ
Identified communities
Number of communities: 277
Average community size 27.509025270758123
Median community size 23.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.  14.  23.  33. 196.]
Significant communities
Number of communities: 108
Average community size 31.73148148148148
Median community size 25.5
Quantile (0,0.25,0.5,0.75,1) community size [ 10.    17.75  25.5   35.5  196.  ]
Neuronal_IZ_mod_202 19
Neuronal_IZ_mod_45 22
Neuronal_IZ_mod_32 35
Neuronal_IZ_mod_155 27
Neuronal_IZ_mod_139 24
Neuronal_IZ_mod_119 44
Neuronal_IZ_mod_174 24
Neuronal_IZ_mod_251 12
Neuronal_IZ_mod_123 52
Neuronal_IZ_mod_186 20
Neuronal_IZ_mod_21 196
Neuronal_IZ_mod_84 32
Neuronal_IZ_mod_86 18
Neuronal_IZ_mod_221 14
Neuronal_IZ_mod_116 75
Neuronal_IZ_mod_46 47
Neuronal_IZ_mod_115 32
Neuronal_IZ_mod_12 83
Neuronal_IZ_mod_113 31
Neuronal_IZ_mod_81 37
Neuronal_IZ_mod_78 66
Neuronal_IZ_mod_211 14
Neuronal_IZ_mod_177 27
Neuronal_IZ_mod_33 28
Neuronal_IZ_mod_31 47
Neuronal_IZ_mod_134 20
Neuronal_IZ_mod_258 24
Neuronal_IZ_mod_58 37
Neuronal_IZ_mod_145 40
Neuronal_IZ_mod_109 15
Neuronal_IZ_mod_54 34
Neuronal_IZ_mod_165 17
Neuronal_IZ_mod_201 10
Neuronal_IZ_mod_138 22
Neuronal_IZ_mod_164 15
Neuronal_IZ_mod_245 15
Neuronal_IZ_mod_121 23
Neuronal_IZ_mod_170 16
Neuronal_IZ_mod_96 49
Neuronal_IZ_mod_248 14
Neuronal_IZ_mod_20 31
Neuronal_IZ_mod_98 28
Neuronal_IZ_mod_195 12
Neuronal_IZ_mod_70 82
Neuronal_IZ_mod_3 51
Neuronal_IZ_mod_192 20
Neuronal_IZ_mod_85 43
Neuronal_IZ_mod_220 13
Neuronal_IZ_mod_126 29
Neuronal_IZ_mod_204 46
Neuronal_IZ_mod_193 13
Neuronal_IZ_mod_205 21
Neuronal_IZ_mod_19 34
Neuronal_IZ_mod_254 17
Neuronal_IZ_mod_169 12
Neuronal_IZ_mod_196 14
Neuronal_IZ_mod_26 33
Neuronal_IZ_mod_104 14
Neuronal_IZ_mod_223 16
Neuronal_IZ_mod_92 21
Neuronal_IZ_mod_132 33
Neuronal_IZ_mod_198 13
Neuronal_IZ_mod_216 30
Neuronal_IZ_mod_219 29
Neuronal_IZ_mod_180 18
Neuronal_IZ_mod_233 15
Neuronal_IZ_mod_77 161
Neuronal_IZ_mod_51 32
Neuronal_IZ_mod_167 18
Neuronal_IZ_mod_42 30
Neuronal_IZ_mod_146 18
Neuronal_IZ_mod_159 12
Neuronal_IZ_mod_153 30
Neuronal_IZ_mod_234 13
Neuronal_IZ_mod_238 22
Neuronal_IZ_mod_97 22
Neuronal_IZ_mod_237 11
Neuronal_IZ_mod_136 32
Neuronal_IZ_mod_89 24
Neuronal_IZ_mod_8 29
Neuronal_IZ_mod_257 31
Neuronal_IZ_mod_110 42
Neuronal_IZ_mod_191 31
Neuronal_IZ_mod_168 13
Neuronal_IZ_mod_181 27
Neuronal_IZ_mod_142 42
Neuronal_IZ_mod_158 63
Neuronal_IZ_mod_27 22
Neuronal_IZ_mod_182 23
Neuronal_IZ_mod_189 43
Neuronal_IZ_mod_229 17
Neuronal_IZ_mod_156 13
Neuronal_IZ_mod_79 26
Neuronal_IZ_mod_14 50
Neuronal_IZ_mod_162 64
Neuronal_IZ_mod_36 23
Neuronal_IZ_mod_57 40
Neuronal_IZ_mod_30 37
Neuronal_IZ_mod_194 24
Neuronal_IZ_mod_90 24
Neuronal_IZ_mod_218 13
Neuronal_IZ_mod_249 30
Neuronal_IZ_mod_39 25
Neuronal_IZ_mod_6 20
Neuronal_IZ_mod_185 35
Neuronal_IZ_mod_38 56
Neuronal_IZ_mod_56 50
Neuronal_IZ_mod_93 24
Number of saved communities: 23
Analysing Neuronal_FZ
Identified communities
Number of communities: 263
Average community size 22.273764258555133
Median community size 19.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.  11.  19.  29. 235.]
Significant communities
Number of communities: 62
Average community size 30.580645161290324
Median community size 25.0
Quantile (0,0.25,0.5,0.75,1) community size [ 11.    20.    25.    35.25 120.  ]
Neuronal_FZ_mod_226 11
Neuronal_FZ_mod_60 24
Neuronal_FZ_mod_21 62
Neuronal_FZ_mod_51 39
Neuronal_FZ_mod_191 22
Neuronal_FZ_mod_36 29
Neuronal_FZ_mod_47 18
Neuronal_FZ_mod_162 15
Neuronal_FZ_mod_221 11
Neuronal_FZ_mod_65 30
Neuronal_FZ_mod_131 48
Neuronal_FZ_mod_62 40
Neuronal_FZ_mod_0 25
Neuronal_FZ_mod_95 14
Neuronal_FZ_mod_71 120
Neuronal_FZ_mod_92 24
Neuronal_FZ_mod_56 47
Neuronal_FZ_mod_132 12
Neuronal_FZ_mod_160 16
Neuronal_FZ_mod_96 22
Neuronal_FZ_mod_39 24
Neuronal_FZ_mod_207 26
Neuronal_FZ_mod_4 29
Neuronal_FZ_mod_101 84
Neuronal_FZ_mod_149 18
Neuronal_FZ_mod_110 31
Neuronal_FZ_mod_77 11
Neuronal_FZ_mod_168 22
Neuronal_FZ_mod_6 26
Neuronal_FZ_mod_90 20
Neuronal_FZ_mod_44 27
Neuronal_FZ_mod_170 15
Neuronal_FZ_mod_55 37
Neuronal_FZ_mod_14 30
Neuronal_FZ_mod_75 41
Neuronal_FZ_mod_84 13
Neuronal_FZ_mod_49 83
Neuronal_FZ_mod_212 20
Neuronal_FZ_mod_150 14
Neuronal_FZ_mod_145 19
Neuronal_FZ_mod_12 39
Neuronal_FZ_mod_61 24
Neuronal_FZ_mod_63 16
Neuronal_FZ_mod_190 33
Neuronal_FZ_mod_112 33
Neuronal_FZ_mod_42 20
Neuronal_FZ_mod_1 26
Neuronal_FZ_mod_196 24
Neuronal_FZ_mod_87 24
Neuronal_FZ_mod_197 43
Neuronal_FZ_mod_128 36
Neuronal_FZ_mod_26 20
Neuronal_FZ_mod_193 40
Neuronal_FZ_mod_155 31
Neuronal_FZ_mod_115 23
Neuronal_FZ_mod_138 47
Neuronal_FZ_mod_220 17
Neuronal_FZ_mod_177 28
Neuronal_FZ_mod_183 25
Neuronal_FZ_mod_107 31
Neuronal_FZ_mod_169 24
Neuronal_FZ_mod_123 73
Number of saved communities: 4
Pericyte
{'Pericyte_RZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Pericyte_BZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Pericyte_IZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'Pericyte_FZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing Pericyte_RZ
Identified communities
Number of communities: 239
Average community size 8.418410041841005
Median community size 5.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2.  2.  5. 11. 97.]
Significant communities
Number of communities: 3
Average community size 14.0
Median community size 13.0
Quantile (0,0.25,0.5,0.75,1) community size [12.  12.5 13.  15.  17. ]
Pericyte_RZ_mod_18 17
Pericyte_RZ_mod_63 13
Pericyte_RZ_mod_118 12
Number of saved communities: 0
Analysing Pericyte_BZ
Identified communities
Number of communities: 222
Average community size 9.563063063063064
Median community size 7.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2.    2.    7.   13.75 65.  ]
Significant communities
Number of communities: 8
Average community size 14.125
Median community size 12.0
Quantile (0,0.25,0.5,0.75,1) community size [10.  11.5 12.  14.5 25. ]
Pericyte_BZ_mod_5 10
Pericyte_BZ_mod_49 25
Pericyte_BZ_mod_44 10
Pericyte_BZ_mod_166 13
Pericyte_BZ_mod_110 12
Pericyte_BZ_mod_111 12
Pericyte_BZ_mod_45 19
Pericyte_BZ_mod_40 12
Number of saved communities: 0
Analysing Pericyte_IZ
Identified communities
Number of communities: 259
Average community size 26.44015444015444
Median community size 22.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.   13.   22.   32.5 232. ]
Significant communities
Number of communities: 115
Average community size 28.695652173913043
Median community size 23.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10.  17.  23.  34. 124.]
Pericyte_IZ_mod_179 14
Pericyte_IZ_mod_31 11
Pericyte_IZ_mod_134 15
Pericyte_IZ_mod_36 23
Pericyte_IZ_mod_8 97
Pericyte_IZ_mod_30 34
Pericyte_IZ_mod_12 69
Pericyte_IZ_mod_104 11
Pericyte_IZ_mod_230 28
Pericyte_IZ_mod_191 14
Pericyte_IZ_mod_165 65
Pericyte_IZ_mod_1 98
Pericyte_IZ_mod_150 11
Pericyte_IZ_mod_40 16
Pericyte_IZ_mod_181 18
Pericyte_IZ_mod_46 25
Pericyte_IZ_mod_212 13
Pericyte_IZ_mod_74 17
Pericyte_IZ_mod_148 17
Pericyte_IZ_mod_75 23
Pericyte_IZ_mod_226 19
Pericyte_IZ_mod_93 35
Pericyte_IZ_mod_222 14
Pericyte_IZ_mod_205 42
Pericyte_IZ_mod_29 25
Pericyte_IZ_mod_61 18
Pericyte_IZ_mod_43 19
Pericyte_IZ_mod_171 12
Pericyte_IZ_mod_225 14
Pericyte_IZ_mod_4 26
Pericyte_IZ_mod_127 28
Pericyte_IZ_mod_102 39
Pericyte_IZ_mod_130 24
Pericyte_IZ_mod_71 23
Pericyte_IZ_mod_223 20
Pericyte_IZ_mod_220 12
Pericyte_IZ_mod_132 13
Pericyte_IZ_mod_114 32
Pericyte_IZ_mod_224 34
Pericyte_IZ_mod_109 13
Pericyte_IZ_mod_128 22
Pericyte_IZ_mod_210 18
Pericyte_IZ_mod_129 26
Pericyte_IZ_mod_124 19
Pericyte_IZ_mod_67 49
Pericyte_IZ_mod_70 48
Pericyte_IZ_mod_207 27
Pericyte_IZ_mod_172 13
Pericyte_IZ_mod_103 23
Pericyte_IZ_mod_121 78
Pericyte_IZ_mod_58 73
Pericyte_IZ_mod_20 28
Pericyte_IZ_mod_96 43
Pericyte_IZ_mod_151 25
Pericyte_IZ_mod_195 18
Pericyte_IZ_mod_156 28
Pericyte_IZ_mod_189 19
Pericyte_IZ_mod_115 34
Pericyte_IZ_mod_228 20
Pericyte_IZ_mod_166 23
Pericyte_IZ_mod_99 14
Pericyte_IZ_mod_69 57
Pericyte_IZ_mod_221 13
Pericyte_IZ_mod_64 17
Pericyte_IZ_mod_140 33
Pericyte_IZ_mod_18 34
Pericyte_IZ_mod_169 14
Pericyte_IZ_mod_54 27
Pericyte_IZ_mod_34 22
Pericyte_IZ_mod_159 18
Pericyte_IZ_mod_247 10
Pericyte_IZ_mod_62 25
Pericyte_IZ_mod_42 37
Pericyte_IZ_mod_180 29
Pericyte_IZ_mod_242 12
Pericyte_IZ_mod_100 23
Pericyte_IZ_mod_198 15
Pericyte_IZ_mod_149 60
Pericyte_IZ_mod_25 41
Pericyte_IZ_mod_50 21
Pericyte_IZ_mod_10 27
Pericyte_IZ_mod_94 53
Pericyte_IZ_mod_190 29
Pericyte_IZ_mod_168 27
Pericyte_IZ_mod_147 31
Pericyte_IZ_mod_85 47
Pericyte_IZ_mod_131 20
Pericyte_IZ_mod_79 23
Pericyte_IZ_mod_200 26
Pericyte_IZ_mod_24 14
Pericyte_IZ_mod_125 23
Pericyte_IZ_mod_136 21
Pericyte_IZ_mod_73 35
Pericyte_IZ_mod_178 16
Pericyte_IZ_mod_91 26
Pericyte_IZ_mod_123 41
Pericyte_IZ_mod_139 28
Pericyte_IZ_mod_86 54
Pericyte_IZ_mod_160 11
Pericyte_IZ_mod_44 12
Pericyte_IZ_mod_153 22
Pericyte_IZ_mod_33 23
Pericyte_IZ_mod_52 13
Pericyte_IZ_mod_7 34
Pericyte_IZ_mod_196 34
Pericyte_IZ_mod_199 13
Pericyte_IZ_mod_83 24
Pericyte_IZ_mod_9 44
Pericyte_IZ_mod_16 45
Pericyte_IZ_mod_234 17
Pericyte_IZ_mod_2 23
Pericyte_IZ_mod_197 41
Pericyte_IZ_mod_5 124
Pericyte_IZ_mod_152 28
Pericyte_IZ_mod_175 14
Number of saved communities: 20
Analysing Pericyte_FZ
Identified communities
Number of communities: 235
Average community size 20.995744680851065
Median community size 16.0
Quantile (0,0.25,0.5,0.75,1) community size [  1.   8.  16.  25. 307.]
Significant communities
Number of communities: 19
Average community size 29.31578947368421
Median community size 19.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10.   15.   19.   34.5 128. ]
Pericyte_FZ_mod_70 21
Pericyte_FZ_mod_13 128
Pericyte_FZ_mod_5 15
Pericyte_FZ_mod_60 10
Pericyte_FZ_mod_148 16
Pericyte_FZ_mod_61 15
Pericyte_FZ_mod_204 15
Pericyte_FZ_mod_48 73
Pericyte_FZ_mod_43 36
Pericyte_FZ_mod_4 44
Pericyte_FZ_mod_28 12
Pericyte_FZ_mod_41 19
Pericyte_FZ_mod_142 21
Pericyte_FZ_mod_83 16
Pericyte_FZ_mod_34 20
Pericyte_FZ_mod_92 10
Pericyte_FZ_mod_0 12
Pericyte_FZ_mod_104 33
Pericyte_FZ_mod_135 41
Number of saved communities: 4
vSMCs

/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)

{'vSMCs_RZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'vSMCs_BZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'vSMCs_IZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges, 'vSMCs_FZ': KGraph KGraph_vs_KGraph with 111032 nodes and 1617389 edges}
Analysing vSMCs_RZ
Identified communities
Number of communities: 265
Average community size 17.184905660377357
Median community size 15.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.   3.  15.  23. 143.]
Significant communities
Number of communities: 7
Average community size 43.714285714285715
Median community size 27.0
Quantile (0,0.25,0.5,0.75,1) community size [ 15.   19.5  27.   41.  143. ]
vSMCs_RZ_mod_65 33
vSMCs_RZ_mod_234 23
vSMCs_RZ_mod_132 49
vSMCs_RZ_mod_79 27
vSMCs_RZ_mod_142 16
vSMCs_RZ_mod_166 15
vSMCs_RZ_mod_2 143
Number of saved communities: 0
Analysing vSMCs_BZ
Identified communities
Number of communities: 264
Average community size 17.113636363636363
Median community size 15.0
Quantile (0,0.25,0.5,0.75,1) community size [ 2.    3.75 15.   24.   74.  ]
Significant communities
Number of communities: 9
Average community size 23.77777777777778
Median community size 24.0
Quantile (0,0.25,0.5,0.75,1) community size [14. 18. 24. 26. 39.]
vSMCs_BZ_mod_76 31
vSMCs_BZ_mod_77 14
vSMCs_BZ_mod_86 24
vSMCs_BZ_mod_102 24
vSMCs_BZ_mod_16 39
vSMCs_BZ_mod_196 23
vSMCs_BZ_mod_60 15
vSMCs_BZ_mod_24 18
vSMCs_BZ_mod_108 26
Number of saved communities: 0
Analysing vSMCs_IZ
Identified communities
Number of communities: 277
Average community size 27.03971119133574
Median community size 22.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.  14.  22.  33. 237.]
Significant communities
Number of communities: 46
Average community size 28.043478260869566
Median community size 18.0
Quantile (0,0.25,0.5,0.75,1) community size [ 10.    14.25  18.    25.   185.  ]
vSMCs_IZ_mod_228 11
vSMCs_IZ_mod_43 15
vSMCs_IZ_mod_184 14
vSMCs_IZ_mod_28 12
vSMCs_IZ_mod_200 17
vSMCs_IZ_mod_42 12
vSMCs_IZ_mod_57 17
vSMCs_IZ_mod_62 15
vSMCs_IZ_mod_69 185
vSMCs_IZ_mod_11 65
vSMCs_IZ_mod_174 30
vSMCs_IZ_mod_75 10
vSMCs_IZ_mod_242 13
vSMCs_IZ_mod_136 38
vSMCs_IZ_mod_80 20
vSMCs_IZ_mod_82 61
vSMCs_IZ_mod_194 25
vSMCs_IZ_mod_206 16
vSMCs_IZ_mod_204 16
vSMCs_IZ_mod_120 16
vSMCs_IZ_mod_276 18
vSMCs_IZ_mod_18 24
vSMCs_IZ_mod_115 20
vSMCs_IZ_mod_251 23
vSMCs_IZ_mod_196 19
vSMCs_IZ_mod_118 12
vSMCs_IZ_mod_195 18
vSMCs_IZ_mod_96 40
vSMCs_IZ_mod_116 11
vSMCs_IZ_mod_49 21
vSMCs_IZ_mod_5 27
vSMCs_IZ_mod_150 22
vSMCs_IZ_mod_193 16
vSMCs_IZ_mod_129 35
vSMCs_IZ_mod_235 10
vSMCs_IZ_mod_37 14
vSMCs_IZ_mod_221 18
vSMCs_IZ_mod_145 35
vSMCs_IZ_mod_105 25
vSMCs_IZ_mod_190 19
vSMCs_IZ_mod_102 32
vSMCs_IZ_mod_74 14
vSMCs_IZ_mod_66 162
vSMCs_IZ_mod_198 13
vSMCs_IZ_mod_250 16
vSMCs_IZ_mod_126 18
Number of saved communities: 12
Analysing vSMCs_FZ
Identified communities
Number of communities: 289
Average community size 27.31833910034602
Median community size 22.0
Quantile (0,0.25,0.5,0.75,1) community size [  2.  13.  22.  33. 250.]
Significant communities
Number of communities: 50
Average community size 37.16
Median community size 27.0
Quantile (0,0.25,0.5,0.75,1) community size [ 11.    17.25  27.    42.75 177.  ]
vSMCs_FZ_mod_226 13
vSMCs_FZ_mod_252 13
vSMCs_FZ_mod_94 30
vSMCs_FZ_mod_86 133
vSMCs_FZ_mod_49 30
vSMCs_FZ_mod_236 22
vSMCs_FZ_mod_189 11
vSMCs_FZ_mod_170 28
vSMCs_FZ_mod_74 33
vSMCs_FZ_mod_182 32
vSMCs_FZ_mod_244 26
vSMCs_FZ_mod_180 13
vSMCs_FZ_mod_41 17
vSMCs_FZ_mod_165 22
vSMCs_FZ_mod_75 14
vSMCs_FZ_mod_55 95
vSMCs_FZ_mod_95 19
vSMCs_FZ_mod_13 51
vSMCs_FZ_mod_131 66
vSMCs_FZ_mod_78 27
vSMCs_FZ_mod_127 25
vSMCs_FZ_mod_136 13
vSMCs_FZ_mod_237 12
vSMCs_FZ_mod_27 23
vSMCs_FZ_mod_152 13
vSMCs_FZ_mod_6 24
vSMCs_FZ_mod_77 74
vSMCs_FZ_mod_143 20
vSMCs_FZ_mod_18 71
vSMCs_FZ_mod_20 18
vSMCs_FZ_mod_32 177
vSMCs_FZ_mod_58 17
vSMCs_FZ_mod_161 30
vSMCs_FZ_mod_45 56
vSMCs_FZ_mod_134 20
vSMCs_FZ_mod_46 76
vSMCs_FZ_mod_144 54
vSMCs_FZ_mod_24 41
vSMCs_FZ_mod_206 33
vSMCs_FZ_mod_60 43
vSMCs_FZ_mod_158 17
vSMCs_FZ_mod_85 43
vSMCs_FZ_mod_250 11
vSMCs_FZ_mod_157 80
vSMCs_FZ_mod_9 27
vSMCs_FZ_mod_1 40
vSMCs_FZ_mod_97 42
vSMCs_FZ_mod_273 29
vSMCs_FZ_mod_220 21
vSMCs_FZ_mod_188 13
Number of saved communities: 20

#captured_plot_module_calc()

[(x, type(tlda.__dict__[x])) for x in tlda.__dict__]

[('tldict', dict),
 ('sorted_zones', list),
 ('name_sep', str),
 ('cellgroupdata', mikg.kgraph.DefaultDict),
 ('fullKG', mikg.kgraph.KGraph),
 ('output_folder_formatter', str),
 ('recalc_warning', bool)]

with open("cardiomyocyte_tlda.pickle", 'wb') as f:
    pickle.dump(tlda, f)

#import pickle
#with open("cardiomyocyte_tlda.pickle", 'rb') as f:
#    tlda = pickle.load(f)

%%capture captured_plot_module_comparisons
tlda.plot_module_comparisons(plot_communities=True)

tlda.plot_module_comparisons(plot_communities=False)

Output directory diff_Adipocyte/
diff_Adipocyte//all_module_heatmap.png
diff_Adipocyte//all_module_compare.png
diff_Adipocyte//score_distribution.png
Output directory diff_Cardiomyocyte/
diff_Cardiomyocyte//all_module_heatmap.png
diff_Cardiomyocyte//all_module_compare.png
diff_Cardiomyocyte//score_distribution.png
Output directory diff_Cycling cells/
diff_Cycling cells//all_module_heatmap.png
diff_Cycling cells//all_module_compare.png
diff_Cycling cells//score_distribution.png
Output directory diff_Endothelial/
diff_Endothelial//all_module_heatmap.png
diff_Endothelial//all_module_compare.png
diff_Endothelial//score_distribution.png
Output directory diff_Fibroblast/
diff_Fibroblast//all_module_heatmap.png
diff_Fibroblast//all_module_compare.png
diff_Fibroblast//score_distribution.png
Output directory diff_Lymphoid/
diff_Lymphoid//all_module_heatmap.png
diff_Lymphoid//all_module_compare.png
diff_Lymphoid//score_distribution.png
Output directory diff_Mast/
diff_Mast//all_module_heatmap.png
diff_Mast//all_module_compare.png
diff_Mast//score_distribution.png
Output directory diff_Myeloid/
diff_Myeloid//all_module_heatmap.png
diff_Myeloid//all_module_compare.png
diff_Myeloid//score_distribution.png
Output directory diff_Neuronal/
diff_Neuronal//all_module_heatmap.png
diff_Neuronal//all_module_compare.png
diff_Neuronal//score_distribution.png
Output directory diff_Pericyte/
diff_Pericyte//all_module_heatmap.png
diff_Pericyte//all_module_compare.png
diff_Pericyte//score_distribution.png
Output directory diff_vSMCs/
diff_vSMCs//all_module_heatmap.png
diff_vSMCs//all_module_compare.png
diff_vSMCs//score_distribution.png

1+1

#captured_plot_module_comparisons()

ct=CommunityTool()
ct.compare_modules(tlda.communities, figsize=(75,75))

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)

	name	gene_nodes	geneset_nodes	disease_nodes	drug_nodes	ncRNA_nodes	TF_nodes	other_nodes	RZ_score_median	RZ_score_mean	...	BZ_absmean-gene	BZ_absmean-disease	BZ_absmean-drug	BZ_diffmean-gene	BZ_diffmean-disease	BZ_diffmean-drug	base_condition	base_zone	base_condition_score_mean	base_condition_score_median
0	Adipocyte_BZ_mod_156	[LGALS12, LGALS9, PDZK1, SLC15A2, SLC15A4, SLC...	[(GO:0015655, alanine:sodium symporter activit...	[]	[]	[]	[]	{}	0.557141	0.745541	...	0.240507	NaN	NaN	1.137923	NaN	NaN	Adipocyte_BZ	BZ	1.543732	1.569280
1	Adipocyte_BZ_mod_242	[MIEN1, MOSPD2, PCTP, PGAP3, PRDX1, STARD3, ST...	[(GO:0140284, endoplasmic reticulum-endosome m...	[]	[]	[]	[]	{}	0.539311	0.514369	...	0.162779	NaN	NaN	1.246605	NaN	NaN	Adipocyte_BZ	BZ	1.494722	1.418179
2	Adipocyte_BZ_mod_194	[ARSD, CHD2, GSDMA, HPN, LBX2, NAPSA, NPIPA1, ...	[(GO:0005353, fructose transmembrane transport...	[]	[]	[]	[LBX2, RXRA]	{}	0.467314	0.962707	...	0.236991	NaN	NaN	1.936724	NaN	NaN	Adipocyte_BZ	BZ	2.141076	2.029815
3	Adipocyte_IZ_mod_151	[ADGRE5, FUS, IGF2, IGF2BP3, IRF8, MYCN, NR5A1...	[(GO:2000195, negative regulation of female go...	[]	[]	[mir-615, mir-96]	[IRF8, MYCN, NR5A1, PAX2, SNAI2, WT1, ZNF224]	{}	0.000000	0.081560	...	0.101681	NaN	NaN	-0.412420	NaN	NaN	Adipocyte_IZ	IZ	1.896252	1.501743
4	Adipocyte_IZ_mod_131	[ADA, CASP8, CNTNAP2, DNPEP, DPP10, DPP4, DPP6...	[(GO:0033632, regulation of cell-cell adhesion...	[]	[]	[mir-153]	[ETV6]	{}	0.058091	0.119542	...	0.146906	NaN	NaN	-0.487879	NaN	NaN	Adipocyte_IZ	IZ	1.357412	1.219080
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
261	vSMCs_FZ_mod_157	[APBA1, APBA2, APBB2, BET1, BET1L, CACNA1B, CA...	[]	[(HP:0000473, Torticollis)]	[]	[mir-31]	[]	{}	0.100023	0.153191	...	0.185415	0.549437	NaN	-0.081719	0.08317	NaN	vSMCs_FZ	FZ	0.559650	0.541151
262	vSMCs_FZ_mod_1	[ADH1B, ADI1, ALDH2, ALDH3A2, AMD1, ANK3, ANKZ...	[(GO:0004145, diamine N-acetyltransferase acti...	[]	[(CHEMBL964, DISULFIRAM)]	[mir-124, mir-141, mir-200a, mir-200b, mir-200...	[ANKZF1, KLF11]	{}	-0.072208	-0.108603	...	0.207073	NaN	0.131613	-0.160596	NaN	-0.120904	vSMCs_FZ	FZ	0.548600	0.522520
263	vSMCs_FZ_mod_97	[ADIPOQ, ADIPOR2, ANGPTL4, CCNC, CDH13, CDK19,...	[(GO:0042809, nuclear vitamin D receptor bindi...	[]	[]	[let-7c]	[ZFHX3]	{}	0.005375	-0.014020	...	0.302328	NaN	NaN	0.046725	NaN	NaN	vSMCs_FZ	FZ	0.435931	0.346689
264	vSMCs_FZ_mod_273	[ANAPC1, ANAPC11, ARMC10, BCL3, BUB3, CD24, CD...	[(GO:0042723, thiamine-containing compound met...	[]	[(CHEMBL2109355, DUSIGITUMAB)]	[let-7a, let-7b, mir-34a, mir-373, mir-378a, m...	[HES6, KLF4, MSC, TP53]	{}	-0.051249	-0.096362	...	0.123821	NaN	0.141616	-0.154815	NaN	-0.139520	vSMCs_FZ	FZ	0.550001	0.533609
265	vSMCs_FZ_mod_220	[AADAT, DHTKD1, DLAT, DLD, DLST, ELOVL7, GCDH,...	[(GO:0010510, regulation of acetyl-CoA biosynt...	[]	[(CHEMBL306823, SODIUM DICHLOROACETATE)]	[]	[PPARA]	{}	0.163721	0.233870	...	0.193818	NaN	0.277142	0.042895	NaN	0.250856	vSMCs_FZ	FZ	0.645618	0.526574

266 rows × 48 columns

geneset_overlapDF = tlda.create_overlap_df("geneset")
geneset_overlapDF.head()

27643

	celltype	module	geneset	geneset_size	geneset_name	overlap	jaccard
0	Adipocyte	Adipocyte_BZ_mod_156	GO:0001504	8	neurotransmitter uptake	0.250000	0.100000
1	Adipocyte	Adipocyte_BZ_mod_156	GO:0001618	79	virus receptor activity	0.012658	0.010870
2	Adipocyte	Adipocyte_BZ_mod_156	GO:0002519	3	natural killer cell tolerance induction	0.333333	0.062500
3	Adipocyte	Adipocyte_BZ_mod_156	GO:0003333	27	amino acid transmembrane transport	0.222222	0.171429
4	Adipocyte	Adipocyte_BZ_mod_156	GO:0003723	1395	RNA binding	0.000717	0.000710

disease_overlapDF = tlda.create_overlap_df("disease")
disease_overlapDF.head()

7184

	celltype	module	disease	disease_size	disease_name	overlap	jaccard
0	Adipocyte	Adipocyte_BZ_mod_156	GO:0006954	395	inflammatory response	0.002532	0.002451
1	Adipocyte	Adipocyte_BZ_mod_156	GO:0070527	46	platelet aggregation	0.021739	0.016949
2	Adipocyte	Adipocyte_BZ_mod_156	EFO:0005409	105	fat body mass	0.009524	0.008475
3	Adipocyte	Adipocyte_BZ_mod_156	EFO:0004995	141	lean body mass	0.007092	0.006494
4	Adipocyte	Adipocyte_BZ_mod_156	EFO:0004842	73	eosinophil count	0.013699	0.011628

drug_overlapDF = tlda.create_overlap_df("drug")
drug_overlapDF.head()

3195

	celltype	module	drug	drug_size	drug_name	overlap	jaccard
0	Adipocyte	Adipocyte_BZ_mod_194	CHEMBL1023	3	BEXAROTENE	0.333333	0.050000
1	Adipocyte	Adipocyte_BZ_mod_194	CHEMBL705	6	ALITRETINOIN	0.166667	0.043478
2	Adipocyte	Adipocyte_BZ_mod_194	CHEMBL1131	6	ACITRETIN	0.166667	0.043478
3	Adipocyte	Adipocyte_BZ_mod_194	CHEMBL75133	3	IRX-4204	0.333333	0.050000
4	Adipocyte	Adipocyte_BZ_mod_194	CHEMBL53418	1	DANTHRON	1.000000	0.055556

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")

import scanpy as sc

adata = sc.read_h5ad("snRNA-seq-submission.h5ad")
celltype_col="cell_type_original"
condition_col = "major_labl"

adata.obs[condition_col] = pd.Categorical(adata.obs[condition_col], ordered=True, categories=[x for x in zoneSort])

adata.obs["ct_region"] = adata.obs["cell_type_original"].astype(str) + "_" + adata.obs["major_labl"].astype(str)
adata.obs["ct_region"] = pd.Categorical(adata.obs["ct_region"])

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

	name	gene_nodes	geneset_nodes	disease_nodes	drug_nodes	ncRNA_nodes	TF_nodes	other_nodes	RZ_score_median	RZ_score_mean	...	BZ_absmean-gene	BZ_absmean-disease	BZ_absmean-drug	BZ_diffmean-gene	BZ_diffmean-disease	BZ_diffmean-drug	base_condition	base_zone	base_condition_score_mean	base_condition_score_median
44	Cardiomyocyte_FZ_mod_220	[ADH1B, ALDH3A2, AMD1, CDCA7L, COMT, GLO1, GRH...	[(GO:0052596, phenethylamine:oxygen oxidoreduc...	[]	[(CHEMBL3545369, EPACADOSTAT)]	[mir-146a, mir-655]	[KLF10, KLF11, MYCN, SP1]	{}	0.036919	0.156643	...	0.150435	NaN	0.203064	0.166942	NaN	-0.076860	Cardiomyocyte_FZ	FZ	0.666949	0.547657
32	Cardiomyocyte_FZ_mod_6	[AASS, ABAT, ABL2, ACAA1, ACAA2, ACAD10, ACAD8...	[(GO:0003857, 3-hydroxyacyl-CoA dehydrogenase ...	[]	[(CHEMBL75880, PERHEXILINE), (CHEMBL89598, VIG...	[SRA1, mir-210]	[]	{}	-0.010567	0.002405	...	0.227720	NaN	0.220527	0.009383	NaN	-0.028472	Cardiomyocyte_FZ	FZ	0.472048	0.385934
18	Cardiomyocyte_IZ_mod_128	[ADAMTS3, ASPN, COL11A1, COL23A1, COL25A1, COL...	[(GO:0005588, collagen type V trimer), (GO:003...	[(Orphanet:610, Bethlem myopathy), (Orphanet:7...	[(CHEMBL2095222, OCRIPLASMIN), (CHEMBL429876, ...	[mir-199a]	[SON, ZNF469, ZNF79]	{}	0.011516	0.068545	...	0.218489	0.141464	0.236480	0.123662	0.132397	-0.030870	Cardiomyocyte_IZ	IZ	1.070764	0.882051
12	Cardiomyocyte_IZ_mod_54	[BMP1, C1R, C1S, COL12A1, COL14A1, COL16A1, CO...	[(GO:0004720, protein-lysine 6-oxidase activit...	[]	[(CHEMBL3545070, VONAPANITASE)]	[]	[HMBOX1]	{}	0.003661	-0.030342	...	0.132353	NaN	0.176930	0.162734	NaN	0.029149	Cardiomyocyte_IZ	IZ	0.991149	0.894183
23	Cardiomyocyte_IZ_mod_42	[ADCY8, ADM, ATXN10, ATXN2, CACNA1A, CACNA1B, ...	[(GO:0001605, adrenomedullin receptor activity...	[]	[(CHEMBL2105635, DAVALINTIDE)]	[mir-126, mir-641]	[]	{}	-0.000366	-0.009621	...	0.284603	NaN	0.346228	0.039273	NaN	0.111638	Cardiomyocyte_IZ	IZ	0.364890	0.127718
52	Cycling cells_IZ_mod_41	[BMP5, EFEMP2, ELN, FBLN1, FBLN5, FBLN7, FBN1,...	[(GO:0004720, protein-lysine 6-oxidase activit...	[(Orphanet:2623, Geleophysic dysplasia), (Orph...	[(CHEMBL2109667, SIMTUZUMAB)]	[]	[]	{}	0.124216	0.280426	...	0.186819	0.223459	0.279710	0.504356	0.930109	-0.081904	Cycling cells_IZ	IZ	1.556675	1.414971
62	Endothelial_IZ_mod_206	[ACAN, C5AR1, C5AR2, COL13A1, EDA, FBN1, FN1, ...	[(GO:0004878, complement component C5a recepto...	[(Orphanet:2623, Geleophysic dysplasia)]	[(CHEMBL3989871, AVACOPAN)]	[mir-675]	[LMX1B, ZIC1]	{}	-0.097341	-0.134919	...	0.140248	0.177811	0.098200	-0.137104	0.280846	-0.224519	Endothelial_IZ	IZ	1.075114	0.948667
67	Endothelial_IZ_mod_170	[BAD, BAK1, BAX, BCL2L11, BCL2L2, BCL2L2-PABPN...	[(GO:0036021, endolysosome lumen)]	[]	[(CHEMBL481611, ODANACATIB)]	[mir-125a, mir-199a, mir-29c, mir-3978, mir-659]	[]	{}	-0.081809	-0.162566	...	0.115147	NaN	0.097189	-0.157661	NaN	-0.201812	Endothelial_IZ	IZ	0.699059	0.638990
69	Endothelial_IZ_mod_182	[AOC3, CALD1, EPHB6, LMOD1, MAFG, MAOA, MIS12,...	[(GO:0004145, diamine N-acetyltransferase acti...	[]	[(CHEMBL24828, VANDETANIB)]	[mir-22]	[MAFG, PRRX1, SOX4]	{}	-0.069466	-0.118682	...	0.247012	NaN	0.184476	0.006675	NaN	-0.221860	Endothelial_IZ	IZ	0.681918	0.652224
100	Fibroblast_FZ_mod_47	[AASS, ABAT, ACACB, ACADS, ACAT1, ACOX1, ACOX3...	[(R-HSA-380615, Serotonin clearance from the s...	[]	[(CHEMBL75880, PERHEXILINE), (CHEMBL89598, VIG...	[mir-16, mir-34a]	[ZNF236]	{}	-0.023797	-0.018153	...	0.195681	NaN	0.169805	-0.098433	NaN	-0.109175	Fibroblast_FZ	FZ	0.437499	0.378999
105	Fibroblast_FZ_mod_48	[CNTFR, CRLF1, CSF3R, CYP46A1, ELP2, EPOR, GHR...	[(GO:0004897, ciliary neurotrophic factor rece...	[]	[(CHEMBL1201573, OPRELVEKIN), (CHEMBL2108583, ...	[mir-106b, mir-107, mir-125a, mir-15a, mir-17,...	[NFX1, PLAG1, ZNF384]	{}	-0.041160	-0.107902	...	0.166516	NaN	0.195934	-0.145342	NaN	-0.233029	Fibroblast_FZ	FZ	0.384016	0.435448
89	Fibroblast_IZ_mod_217	[EPRS, ERO1A, F3, F8, FAM20A, FAM20C, FGF23, G...	[(R-HSA-9672391, Defective F8 cleavage by thro...	[]	[(CHEMBL2109624, CAPLACIZUMAB), (CHEMBL3707326...	[mir-130a, mir-27a, mir-494]	[]	{}	-0.071981	-0.122831	...	0.172814	NaN	0.077115	-0.157316	NaN	-0.039010	Fibroblast_IZ	IZ	0.820749	0.945415
84	Fibroblast_IZ_mod_127	[ARSA, CCDC180, CDK10, CDK12, CDK13, CDK16, CD...	[(GO:0021817, nucleokinesis involved in cell m...	[(Orphanet:98853, Autosomal dominant Emery-Dre...	[(CHEMBL2105661, LITRONESIB)]	[]	[ZNF276]	{}	-0.012351	-0.008685	...	0.282730	0.476562	0.162619	0.014536	0.119571	-0.100303	Fibroblast_IZ	IZ	0.771814	0.734109
96	Fibroblast_IZ_mod_190	[BGLAP, CD74, COL18A1, CSTB, CTSB, CTSC, CTSD,...	[(GO:0036021, endolysosome lumen)]	[]	[(CHEMBL2108252, APOLIZUMAB), (CHEMBL481611, O...	[mir-101, mir-124, mir-199a, mir-204, mir-211,...	[KLF6]	{}	-0.181733	-0.163348	...	0.130225	NaN	0.152906	-0.149464	NaN	-0.087640	Fibroblast_IZ	IZ	0.469341	0.185855
122	Lymphoid_FZ_mod_153	[ANXA5, BIRC3, BRAP, CAPZB, CASP10, CASP8, CAS...	[(R-HSA-75158, TRAIL signaling)]	[]	[(CHEMBL3039522, BIRINAPANT)]	[MALAT1, mir-10b, mir-146a, mir-155, mir-21, m...	[]	{}	-0.092452	-0.098162	...	0.394628	NaN	0.129411	-0.216456	NaN	-0.322595	Lymphoid_FZ	FZ	0.455151	0.507302
124	Lymphoid_FZ_mod_4	[ABAT, ACACB, ACLY, ACOX3, ACSF3, ACSS1, ACSS2...	[(R-HSA-964975, Vitamin B6 activation to pyrid...	[]	[(CHEMBL1762621, BARDOXOLONE METHYL), (CHEMBL3...	[let-7b, mir-124, mir-140, mir-147b, mir-15a, ...	[NFE2L2]	{}	-0.054898	-0.092039	...	0.150599	NaN	0.160547	-0.260746	NaN	-0.288495	Lymphoid_FZ	FZ	0.324767	0.219746
107	Lymphoid_IZ_mod_58	[ARAF, C2, CFH, COL8A1, EFEMP1, ERO1A, FAM20A,...	[]	[(Orphanet:75376, Familial drusen)]	[(CHEMBL2109624, CAPLACIZUMAB)]	[]	[]	{}	0.054301	0.220008	...	0.080830	0.063884	0.110091	-0.274054	-0.434262	-0.302528	Lymphoid_IZ	IZ	1.382638	1.103541
111	Lymphoid_IZ_mod_232	[CCDC88C, FGF7, FIP1L1, GLMN, IDH1, MBTPS1, NF...	[(GO:0038091, positive regulation of cell prol...	[]	[(CHEMBL2108313, CONBERCEPT)]	[mir-361]	[]	{}	-0.081925	-0.058126	...	0.231070	NaN	0.150545	-0.206872	NaN	-0.094619	Lymphoid_IZ	IZ	0.742143	0.717855
116	Lymphoid_IZ_mod_181	[BGLAP, BID, COL15A1, COL18A1, CSTA, CTSB, CTS...	[(GO:0036021, endolysosome lumen)]	[]	[(CHEMBL2108252, APOLIZUMAB)]	[mir-125b, mir-204, mir-3978, mir-659]	[IRF7]	{}	-0.122074	-0.111598	...	0.101080	NaN	0.154171	-0.181390	NaN	-0.321720	Lymphoid_IZ	IZ	0.483397	0.375562
127	Mast_BZ_mod_52	[CD180, CDC42SE2, CFAP46, CPQ, CYP1B1, DTX3L, ...	[(GO:0019805, quinolinate biosynthetic process...	[]	[(CHEMBL3545369, EPACADOSTAT)]	[]	[PLSCR1, STAT1, ZNF226, ZNF227, ZNF235, ZNF266...	{}	-0.473124	-0.471883	...	0.195404	NaN	0.243779	0.924947	NaN	-0.002879	Mast_BZ	BZ	0.950887	1.008346
138	Mast_FZ_mod_22	[AMMECR1, APOD, ARRDC5, ATXN7L1, CDHR3, CRISPL...	[(GO:0019705, protein-cysteine S-myristoyltran...	[]	[(CHEMBL2103847, TOSEDOSTAT)]	[mir-296]	[ESR1, ZNF256]	{}	-0.149255	-0.142184	...	0.160666	NaN	0.220479	-0.001180	NaN	0.190557	Mast_FZ	FZ	0.977662	0.873525
161	Mast_FZ_mod_60	[ACBD3, ANKAR, ARFRP1, ARL1, CADPS2, CNIH2, CO...	[(GO:0005483, soluble NSF attachment protein a...	[]	[(CHEMBL1201569, BOTULINUM TOXIN TYPE B)]	[mir-155]	[TMF1, ZNF787]	{}	0.299502	0.308611	...	0.246447	NaN	0.252533	-0.134940	NaN	0.089923	Mast_FZ	FZ	0.893749	0.829905
158	Mast_FZ_mod_113	[A4GALT, AKR1B1, AKR1C3, AMDHD2, B3GALT4, BCAT...	[(GO:0102148, N-acetyl-beta-D-galactosaminidas...	[]	[(CHEMBL1762621, BARDOXOLONE METHYL)]	[mir-155]	[NFAT5, NFE2L2, YBX3]	{}	0.000000	0.026567	...	0.148206	NaN	0.255493	0.128962	NaN	0.045838	Mast_FZ	FZ	0.755052	0.693526
125	Mast_RZ_mod_159	[CD63, DPYSL2, DYRK2, ERAP2, FAM3C, GLG1, GPSM...	[(GO:0031088, platelet dense granule membrane)]	[]	[(CHEMBL2103847, TOSEDOSTAT)]	[mir-122]	[]	{}	0.937422	1.015981	...	0.189221	NaN	0.220479	-0.258153	NaN	0.190557	Mast_RZ	RZ	1.015981	0.937422
173	Myeloid_FZ_mod_171	[ELN, FBLN5, FBN1, KHDRBS3, LGALS3, LOXL3, MFG...	[]	[(Orphanet:90348, Autosomal dominant cutis laxa)]	[(CHEMBL3545070, VONAPANITASE)]	[]	[TBP, TFAP2C]	{}	-0.124484	0.076627	...	0.158198	0.055430	0.122936	-0.282932	-0.033499	-0.101399	Myeloid_FZ	FZ	1.091663	0.952798
179	Myeloid_FZ_mod_53	[CYP27A1, CYP2R1, ETS2, FDX1, HSCB, HSPA4, HSP...	[(GO:0030343, vitamin D3 25-hydroxylase activi...	[]	[(CHEMBL290352, CEP-1347)]	[let-7g, mir-125b, mir-221, mir-222]	[ETS2, POU2F1, RXRA, RXRB]	{}	0.050624	0.024781	...	0.165038	NaN	0.072147	-0.191138	NaN	-0.354382	Myeloid_FZ	FZ	0.745690	0.638670
178	Myeloid_FZ_mod_154	[ANKRD6, AXIN1, AXIN2, BACE1, BLZF1, CHKA, CHK...	[(GO:0004305, ethanolamine kinase activity)]	[]	[(CHEMBL4204869, ELENBECESTAT)]	[]	[HINFP]	{}	0.010278	-0.027285	...	0.235644	NaN	0.114704	-0.262472	NaN	-0.246637	Myeloid_FZ	FZ	0.597107	0.615862
163	Myeloid_IZ_mod_96	[ADGRG1, ASPN, BMP1, CD93, COL11A1, COL24A1, C...	[(GO:0005588, collagen type V trimer)]	[]	[(CHEMBL1946170, REGORAFENIB)]	[mir-29a]	[ZNF469, ZNF79]	{}	0.013721	0.063723	...	0.110539	NaN	0.160587	0.087542	NaN	-0.343139	Myeloid_IZ	IZ	1.368607	1.031102
162	Myeloid_IZ_mod_33	[BMP5, EFEMP2, ELN, FBLN5, FBN1, HSPA5, LOX, L...	[(GO:0004720, protein-lysine 6-oxidase activit...	[(Orphanet:2623, Geleophysic dysplasia)]	[(CHEMBL3545070, VONAPANITASE)]	[]	[]	{}	-0.018096	0.050453	...	0.147433	0.164665	0.122936	0.225463	-0.117130	-0.101399	Myeloid_IZ	IZ	0.807432	0.842280
207	Neuronal_FZ_mod_77	[KCNMA1, KCNMB3, KCNMB4, MFSD11, NAPEPLD, PGM1...	[(GO:0060083, smooth muscle contraction involv...	[]	[(CHEMBL3707319, GRC-15300)]	[]	[]	{}	-0.301057	-0.347761	...	0.266994	NaN	0.104316	-0.300057	NaN	-0.282454	Neuronal_FZ	FZ	0.784537	0.648587
183	Neuronal_IZ_mod_202	[ATG5, CALCB, CALCRL, CELSR1, CRCP, DCLK3, GAB...	[(GO:0004965, G protein-coupled GABA receptor ...	[(EFO:0004540, chronic fatigue syndrome)]	[(CHEMBL2105635, DAVALINTIDE)]	[]	[]	{}	-0.008254	-0.007583	...	0.144532	0.192090	0.100299	-0.219398	-0.327648	-0.411966	Neuronal_IZ	IZ	0.967775	0.926042
189	Neuronal_IZ_mod_85	[BTN3A1, BTN3A2, C5, C5AR1, C5AR2, CANX, CCL22...	[(GO:0004878, complement component C5a recepto...	[]	[(CHEMBL3989871, AVACOPAN)]	[mir-106a]	[LMX1B, TWIST2, ZNF160]	{}	-0.007404	0.010702	...	0.119368	NaN	0.074665	-0.040646	NaN	-0.309479	Neuronal_IZ	IZ	0.918192	0.891771
200	Neuronal_IZ_mod_57	[AIFM1, ALDH1L1, BANF1, BCL2L11, BCL2L2, BCL2L...	[(GO:0036021, endolysosome lumen)]	[]	[(CHEMBL481611, ODANACATIB)]	[mir-135a]	[]	{}	-0.075387	-0.131407	...	0.170371	NaN	0.108482	-0.065365	NaN	-0.161559	Neuronal_IZ	IZ	0.724061	0.750104
225	Pericyte_IZ_mod_50	[AHRR, ARNT, BMP1, ELN, FBLN5, HAND2, HDAC2, K...	[]	[(Orphanet:90348, Autosomal dominant cutis laxa)]	[(CHEMBL2109667, SIMTUZUMAB), (CHEMBL3545070, ...	[mir-126, mir-29c]	[AHRR, ARNT, HAND2, MAX]	{}	-0.009605	-0.008363	...	0.138728	0.058819	0.209048	0.124612	0.072362	-0.105978	Pericyte_IZ	IZ	1.130508	0.910634
222	Pericyte_IZ_mod_62	[ANTXR2, C2, CD46, CFH, CFI, COL8A1, CRP, EFEM...	[]	[(Orphanet:75376, Familial drusen)]	[(CHEMBL2109624, CAPLACIZUMAB)]	[]	[]	{}	-0.146466	-0.137328	...	0.110004	0.060049	0.129231	-0.121726	0.091008	-0.296451	Pericyte_IZ	IZ	0.966937	0.970217
219	Pericyte_IZ_mod_228	[ABCB1, ABCC1, ABCC10, ABCC3, ABCC4, ABCC5, AB...	[(GO:0046943, carboxylic acid transmembrane tr...	[]	[(CHEMBL348475, TARIQUIDAR)]	[mir-382]	[GABPA, KLF12, KMT2A, NFE2L2, PLAGL1, YBX1]	{}	-0.052733	-0.099800	...	0.206060	NaN	0.140135	-0.173851	NaN	-0.235312	Pericyte_IZ	IZ	0.943991	0.790561
265	vSMCs_FZ_mod_220	[AADAT, DHTKD1, DLAT, DLD, DLST, ELOVL7, GCDH,...	[(GO:0010510, regulation of acetyl-CoA biosynt...	[]	[(CHEMBL306823, SODIUM DICHLOROACETATE)]	[]	[PPARA]	{}	0.163721	0.233870	...	0.193818	NaN	0.277142	0.042895	NaN	0.250856	vSMCs_FZ	FZ	0.645618	0.526574
264	vSMCs_FZ_mod_273	[ANAPC1, ANAPC11, ARMC10, BCL3, BUB3, CD24, CD...	[(GO:0042723, thiamine-containing compound met...	[]	[(CHEMBL2109355, DUSIGITUMAB)]	[let-7a, let-7b, mir-34a, mir-373, mir-378a, m...	[HES6, KLF4, MSC, TP53]	{}	-0.051249	-0.096362	...	0.123821	NaN	0.141616	-0.154815	NaN	-0.139520	vSMCs_FZ	FZ	0.550001	0.533609
262	vSMCs_FZ_mod_1	[ADH1B, ADI1, ALDH2, ALDH3A2, AMD1, ANK3, ANKZ...	[(GO:0004145, diamine N-acetyltransferase acti...	[]	[(CHEMBL964, DISULFIRAM)]	[mir-124, mir-141, mir-200a, mir-200b, mir-200...	[ANKZF1, KLF11]	{}	-0.072208	-0.108603	...	0.207073	NaN	0.131613	-0.160596	NaN	-0.120904	vSMCs_FZ	FZ	0.548600	0.522520
240	vSMCs_IZ_mod_251	[ABCA1, ABCG1, DTNB, GJC2, GPCPD1, LCAT, LYPLA...	[(GO:0097682, intracellular phosphatidylinosit...	[]	[(CHEMBL608, PROBUCOL)]	[]	[NR1H2, PREB]	{}	-0.007425	-0.018980	...	0.151633	NaN	0.325287	0.102205	NaN	-0.058110	vSMCs_IZ	IZ	0.913916	0.766721

40 rows × 48 columns

adata

AnnData object with n_obs × n_vars = 191795 × 29126
    obs: 'sample', 'n_counts', 'n_genes', 'percent_mito', 'doublet_score', 'dissociation_score', 'cell_type_original', 'patient_region_id', 'patient', 'patient_group', 'major_labl', 'final_cluster', 'assay_ontology_term_id', 'cell_type_ontology_term_id', 'development_stage_ontology_term_id', 'disease_ontology_term_id', 'ethnicity_ontology_term_id', 'is_primary_data', 'organism_ontology_term_id', 'sex_ontology_term_id', 'tissue_ontology_term_id', 'ct_region'
    var: 'feature_biotype', 'feature_is_filtered'
    uns: 'X_approximate_distribution', 'X_normalization', 'batch_condition', 'cell_type_original_colors', 'default_embedding', 'schema_version', 'title'
    obsm: 'X_harmony', 'X_pca', 'X_umap'

#%%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")

disease 7184
geneset 27643
drug 3195
['SP1', 'MYCN', 'KLF11', 'ADH1B', 'INMT', 'COMT', 'ALDH3A2', 'IDO1', 'S100A6', 'PLA2G4A', 'GLO1', 'PTTG1', 'LRTOMT', 'MAOA', 'CDCA7L', 'GRHPR', 'SMS', 'HNMT', 'IL12B', 'PNMT', 'TNFSF11', 'SLC18A2', 'SRM', 'SAT2', 'MAOB', 'VAV1', 'HAGH', 'AMD1', 'KLF10', 'SAT1', 'mir-146a', 'mir-655']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Cardiomyocyte_FZ_mod_220 :  module_plots/overview_plot_Cardiomyocyte_FZ_mod_220.png
['MPV17', 'PHYH', 'GNPAT', 'GSTK1', 'HADHA', 'ACLY', 'NUDT19', 'CAT', 'HMGCS1', 'ECHDC1', 'GATM', 'HSD17B4', 'DECR2', 'NUDT7', 'ECI1', 'ADH5', 'MAP4K5', 'SUCLG1', 'ACADS', 'HACL1', 'AOX1', 'ACOX1', 'HIBADH', 'ACAA1', 'MLYCD', 'HSD17B10', 'GCDH', 'DHRS4', 'UCHL3', 'PCCA', 'LONP2', 'HIBCH', 'HADHB', 'MECR', 'ACAA2', 'MCCC2', 'ECHS1', 'RAB11A', 'ACAT2', 'CS', 'EHHADH', 'ALDH9A1', 'ALDH2', 'ALDH7A1', 'ACADVL', 'NSDHL', 'ABAT', 'ACAD8', 'CRAT', 'DHRS11', 'PIPOX', 'ACSF3', 'ACADM', 'ACADSB', 'ALDH6A1', 'DBT', 'SUCLA2', 'PCCB', 'ECI2', 'ACADL', 'SLC25A29', 'ACSS1', 'ABL2', 'BNIP3', 'PEX26', 'OXCT2', 'ACOX2', 'ACOT2', 'DLST', 'RIPK2', 'ACSS3', 'MCCC1', 'ACAD10', 'SCP2', 'ACOX3', 'CPT2', 'AASS', 'MCAT', 'DDO', 'SLC25A20', 'PHYKPL', 'CROT', 'CYBB', 'ECH1', 'HMGCL', 'IVD', 'ACOT8', 'ACAD9', 'BTD', 'EPHX2', 'TYSND1', 'SRA1', 'mir-210']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Cardiomyocyte_FZ_mod_6 :  module_plots/overview_plot_Cardiomyocyte_FZ_mod_6.png
['COL5A3', 'SDC2', 'COL6A1', 'P4HA3', 'ADAMTS3', 'TNR', 'LUM', 'CREBRF', 'ITGA8', 'COL6A2', 'P4HA2', 'COL23A1', 'DDR2', 'COL11A1', 'ITGA7', 'COL5A2', 'P4HA1', 'LAMB4', 'COL4A4', 'GP6', 'ASPN', 'DDR1', 'THBS3', 'ITGA6', 'COL6A3', 'PCOLCE', 'COL3A1', 'COL25A1', 'ITGA9', 'SDC4', 'PCOLCE2', 'ITGB5', 'FRMD5', 'COL5A1', 'ZNF79', 'TLL2', 'ITGA11', 'LAIR1', 'ITGB8', 'SON', 'ITGAV', 'ZNF469', 'ITGA3', 'IGBP1', 'COMP', 'ITGA10', 'DUSP18', 'ITGA2', 'mir-199a']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Cardiomyocyte_IZ_mod_128 :  module_plots/overview_plot_Cardiomyocyte_IZ_mod_128.png
['LOXL1', 'COL24A1', 'LOXL3', 'COL27A1', 'ELN', 'COL14A1', 'MIA3', 'SCFD1', 'C1R', 'MFAP5', 'LOXL2', 'HMBOX1', 'BMP1', 'COL16A1', 'TLL1', 'LOX', 'LOXL4', 'COL7A1', 'C1S', 'PTK7', 'SERPINB1', 'POSTN', 'COL12A1']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Cardiomyocyte_IZ_mod_54 :  module_plots/overview_plot_Cardiomyocyte_IZ_mod_54.png
['CALCRL', 'CFTR', 'VIP', 'TPP1', 'CACNB1', 'SNAP25', 'PTK6', 'RIMS2', 'ADM', 'CACNA2D1', 'GNB2', 'CATSPERD', 'RIMS1', 'SYT1', 'CACNA2D4', 'RGS1', 'GNG2', 'GNG4', 'CD3D', 'RAMP1', 'CACNA1B', 'DNAJC5', 'TRDN', 'ATXN2', 'CACNA1C', 'CACNB3', 'CLN3', 'CACNG3', 'ATXN10', 'ADCY8', 'GIP', 'POLA2', 'GABBR1', 'CACNA1A', 'PPT1', 'CACNB4', 'CRCP', 'CATSPERG', 'CACNG8', 'CD3G', 'CATSPERB', 'CACNB2', 'GNAI2', 'CACNA1H', 'CD4', 'SRMS', 'CLN6', 'HLA-DRB1', 'RAMP3', 'RYR1', 'CD3E', 'mir-641', 'mir-126']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Cardiomyocyte_IZ_mod_42 :  module_plots/overview_plot_Cardiomyocyte_IZ_mod_42.png
['LOXL1', 'HSPA5', 'LOXL3', 'FBLN1', 'MFAP2', 'ELN', 'LTBP3', 'BMP5', 'LTBP2', 'MFAP5', 'LOXL2', 'FBN3', 'EFEMP2', 'MFAP1', 'FBN1', 'LOX', 'LTBP1', 'FBLN7', 'FBLN5']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Cycling cells_IZ_mod_41 :  module_plots/overview_plot_Cycling cells_IZ_mod_41.png
['ITIH4', 'LMX1B', 'MFN2', 'C5AR1', 'GPER1', 'SPG11', 'LGALS8', 'IL18', 'EDA', 'RHOT1', 'FN1', 'LTBP3', 'LTBP2', 'IL17RC', 'LRP4', 'PLXDC1', 'LMLN', 'COL13A1', 'ACAN', 'FBN1', 'LOX', 'TGFBI', 'LRRC4B', 'C5AR2', 'ZIC1', 'NID2', 'LTBP1', 'NID1', 'FSCN1', 'MMP19', 'RPS19', 'PACS2', 'mir-675']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Endothelial_IZ_mod_206 :  module_plots/overview_plot_Endothelial_IZ_mod_206.png
['CTSS', 'BCL2L2-PABPN1', 'CUBN', 'TNFRSF10B', 'COL18A1', 'BCL2L11', 'VDAC2', 'LGMN', 'CTSL', 'CTSC', 'COL15A1', 'BCL2L2', 'BAX', 'SPHK1', 'CTSK', 'CERS6', 'BAK1', 'CTSD', 'CTSZ', 'TXNDC5', 'CTSB', 'BAD', 'NMT1', 'BGLAP', 'TNFRSF25', 'BID', 'mir-3978', 'mir-199a', 'mir-29c', 'mir-659', 'mir-125a']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Endothelial_IZ_mod_170 :  module_plots/overview_plot_Endothelial_IZ_mod_170.png
['TNC', 'AOC3', 'SMOX', 'PMF1', 'SOX4', 'LMOD1', 'PTPRB', 'SMTN', 'MAOA', 'MAFG', 'EPHB6', 'CALD1', 'SMS', 'PRRX1', 'MIS12', 'SRM', 'TCF25', 'SAT2', 'SAT1', 'mir-22']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Endothelial_IZ_mod_182 :  module_plots/overview_plot_Endothelial_IZ_mod_182.png
['AOX1', 'ABAT', 'ACOX1', 'MDH1', 'ADH1B', 'INMT', 'HIBADH', 'ALDH3A2', 'MOCOS', 'SCP2', 'ACOX3', 'ACSF3', 'TECR', 'ALDH6A1', 'MLYCD', 'DBT', 'CD38', 'ZNF236', 'AASS', 'GCDH', 'CAT', 'PC', 'RDH11', 'BST1', 'MAOA', 'PCCB', 'MOCS1', 'RDH5', 'ACSS2', 'HLCS', 'DHRS4', 'NNMT', 'PHYKPL', 'ECHDC1', 'CROT', 'ACSS1', 'PCCA', 'CPT1C', 'ECH1', 'SIRT7', 'CPT1A', 'ACAT1', 'PDXK', 'CPT1B', 'DHRS3', 'HNMT', 'SIRT5', 'ASL', 'MOCS2', 'ACACB', 'ADH5', 'DHTKD1', 'AKAP12', 'MAOB', 'EHHADH', 'ME2', 'MAP4K5', 'SUCLG1', 'ALDH2', 'ALDH1A1', 'BTD', 'ACADS', 'ALDH7A1', 'DLST', 'SIRT3', 'AGMO', 'ACSS3', 'PDE7A', 'HACL1', 'MCCC1', 'mir-16', 'mir-34a']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Fibroblast_FZ_mod_47 :  module_plots/overview_plot_Fibroblast_FZ_mod_47.png
['SOAT1', 'CYP46A1', 'IL13RA1', 'CSF3R', 'IL12RB2', 'USP18', 'IL27RA', 'EPOR', 'LIFR', 'SNX8', 'SOCS2', 'IL6ST', 'IL11RA', 'ELP2', 'PRLR', 'PLAG1', 'CNTFR', 'NFX1', 'IL10RA', 'IL7R', 'SOCS5', 'ZNF384', 'JAK1', 'CRLF1', 'JAK2', 'TYK2', 'GHR', 'IL17D', 'TUB', 'mir-125a', 'mir-203a', 'mir-194', 'mir-708', 'mir-15a', 'mir-185', 'mir-211', 'mir-9', 'mir-106b', 'mir-107', 'mir-17', 'mir-20a', 'mir-373', 'mir-30c']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Fibroblast_FZ_mod_48 :  module_plots/overview_plot_Fibroblast_FZ_mod_48.png
['IL18', 'TNF', 'ERO1A', 'EPRS', 'GP1BB', 'FGF23', 'SERPINC1', 'F8', 'F3', 'TFPI', 'PITRM1', 'FAM20A', 'GFM1', 'FAM20C', 'VWF', 'IL18BP', 'mir-130a', 'mir-27a', 'mir-494']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Fibroblast_IZ_mod_217 :  module_plots/overview_plot_Fibroblast_IZ_mod_217.png
['SUN1', 'NES', 'CDK17', 'UBR1', 'CTSA', 'MKNK2', 'CSNK2B', 'IDUA', 'CDK12', 'RECQL4', 'TMEM67', 'LMNA', 'FHOD3', 'SYNE2', 'KIF11', 'UBR2', 'CDK10', 'CCDC180', 'ZNF276', 'SYNE1', 'ARSA', 'UBN1', 'FHOD1', 'DST', 'SYNM', 'CDK13', 'CDK16', 'LMNB2', 'STMN1', 'PLEC', 'LMNB1', 'GALNS', 'TMEM201']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Fibroblast_IZ_mod_127 :  module_plots/overview_plot_Fibroblast_IZ_mod_127.png
['CTSS', 'CTSH', 'GRN', 'COL18A1', 'HLA-DPB1', 'SDF4', 'LGMN', 'CTSL', 'CTSC', 'KLF6', 'SPHK1', 'CTSK', 'LAMP2', 'HLA-DRB5', 'CTSD', 'CTSZ', 'CD74', 'CTSB', 'DLK1', 'BGLAP', 'M6PR', 'CSTB', 'GNS', 'HLA-DQA1', 'mir-29b', 'mir-659', 'mir-588', 'mir-3978', 'mir-199a', 'mir-204', 'mir-101', 'mir-124', 'mir-211']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Fibroblast_IZ_mod_190 :  module_plots/overview_plot_Fibroblast_IZ_mod_190.png
['TAB3', 'TRMT10B', 'CASP10', 'MYO1D', 'DAP3', 'CFLAR', 'TNFRSF10B', 'XIAP', 'TNFRSF10D', 'FAF1', 'MRPS25', 'CASP8', 'ANXA5', 'SHARPIN', 'CASP9', 'PLK3', 'UBR2', 'INVS', 'RAB11FIP4', 'SART3', 'RIPK3', 'TNFRSF10A', 'DAXX', 'CAPZB', 'USP11', 'PTBP2', 'BRAP', 'WDR11', 'TNFRSF25', 'PROKR1', 'RIPK1', 'EIF4G3', 'PTCD3', 'USP4', 'FADD', 'RPAIN', 'COL6A6', 'RNF31', 'BIRC3', 'FCMR', 'RIPK2', 'DOCK5', 'mir-10b', 'mir-21', 'MALAT1', 'mir-146a', 'mir-155', 'mir-34a']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Lymphoid_FZ_mod_153 :  module_plots/overview_plot_Lymphoid_FZ_mod_153.png
['AOX1', 'ADH1B', 'ABAT', 'ACLY', 'HIBADH', 'RIMKLB', 'ACSF3', 'ACOX3', 'ATP6V1A', 'ALDH6A1', 'MLYCD', 'DBT', 'PC', 'RDH11', 'PCCB', 'RDH5', 'NFE2L2', 'ACSS2', 'ECHDC1', 'PCCA', 'ACSS1', 'HADHB', 'RDH10', 'PDXK', 'GPHN', 'DHRS3', 'ALDH1A2', 'ACACB', 'ADH5', 'MAOB', 'GABRA3', 'ALDH5A1', 'ALDH1A1', 'ACSS3', 'NFASC', 'mir-15a', 'mir-124', 'mir-33a', 'let-7b', 'mir-34c', 'mir-147b', 'mir-140', 'mir-181b']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Lymphoid_FZ_mod_4 :  module_plots/overview_plot_Lymphoid_FZ_mod_4.png
['C2', 'FAM20A', 'COL8A1', 'ERO1A', 'STAB2', 'PCSK6', 'ARAF', 'FAM20B', 'CFH', 'EFEMP1', 'PTX3', 'MATN2', 'MLANA', 'FGF23', 'FAM20C', 'VWF', 'MT-ND5']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Lymphoid_IZ_mod_58 :  module_plots/overview_plot_Lymphoid_IZ_mod_58.png
['PIK3R2', 'SH2B1', 'CCDC88C', 'MBTPS1', 'SNX2', 'IDH1', 'PDGFRA', 'PSIP1', 'SHF', 'PLCG1', 'FGF7', 'PDGFC', 'NF1', 'FIP1L1', 'VEGFC', 'SOS2', 'GLMN', 'PDGFRB', 'mir-361']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Lymphoid_IZ_mod_232 :  module_plots/overview_plot_Lymphoid_IZ_mod_232.png
['CTSS', 'CSTA', 'CTSH', 'CUBN', 'COL18A1', 'HLA-DPB1', 'VDAC2', 'LGMN', 'CTSL', 'CTSC', 'COL15A1', 'IRF7', 'RNF216', 'SPHK1', 'CTSK', 'IFIH1', 'MAVS', 'HLA-DRB5', 'CTSD', 'CTSZ', 'NLRP3', 'TXNDC5', 'CTSB', 'BGLAP', 'RNF135', 'GNS', 'HLA-DQA1', 'BID', 'mir-3978', 'mir-204', 'mir-659', 'mir-125b']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Lymphoid_IZ_mod_181 :  module_plots/overview_plot_Lymphoid_IZ_mod_181.png
['STAT1', 'GLIPR2', 'ZNF273', 'CDC42SE2', 'RNF121', 'IDO1', 'ZNF41', 'PLAC8', 'ZNF354A', 'SQOR', 'EIF2AK4', 'FHAD1', 'ZNF84', 'ZNF235', 'ZNF419', 'CYP1B1', 'EIF2AK1', 'RPGR', 'ZNF669', 'IDO2', 'CPQ', 'ZNF33B', 'ZNF510', 'CFAP46', 'ZNF777', 'CD180', 'ZNF266', 'ZNF460', 'DTX3L', 'ZNF227', 'METTL9', 'ZNF417', 'PARP9', 'ZNF778', 'ZNF226', 'KYNU', 'ZNF860', 'ZNF268', 'RCN1', 'PLSCR1', 'HAAO']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Mast_BZ_mod_52 :  module_plots/overview_plot_Mast_BZ_mod_52.png
['KIAA0232', 'ZDHHC7', 'FBXL6', 'YPEL3', 'ZDHHC20', 'NRDC', 'SEPTIN1', 'TMEM229B', 'RHOBTB1', 'APOD', 'ZDHHC5', 'ESR1', 'PROS1', 'SLC16A6', 'SLCO2A1', 'MTO1', 'GASK1A', 'MPHOSPH9', 'SHROOM3', 'ZDHHC21', 'GNRHR', 'ZDHHC3', 'ZDHHC2', 'OTUD6B', 'DNPEP', 'FXYD5', 'USP31', 'CDHR3', 'ATXN7L1', 'HSD17B3', 'TRMU', 'NOTCH2NLA', 'SPECC1L', 'TONSL', 'DMWD', 'WNK4', 'AMMECR1', 'GTPBP3', 'HDDC2', 'RPRM', 'REXO2', 'ZDHHC17', 'KAZN', 'YIF1B', 'RNF115', 'ARRDC5', 'CRISPLD2', 'CYP2C19', 'GGNBP2', 'ZNF256', 'SNX24', 'PIEZO2', 'NBPF15', 'TMEM120B', 'FAM174B', 'mir-296']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Mast_FZ_mod_22 :  module_plots/overview_plot_Mast_FZ_mod_22.png
['ZW10', 'GABRA4', 'GOLGA8B', 'VAMP3', 'STX5', 'VPS53', 'CADPS2', 'GORASP1', 'VAMP2', 'KNTC1', 'STX4', 'RAB6A', 'STX17', 'NAPB', 'VAMP8', 'RIC1', 'ZNF787', 'GCC2', 'SNAP47', 'TSPO', 'STXBP3', 'STX18', 'STX2', 'STX8', 'SCFD2', 'VPS52', 'TBC1D23', 'COG1', 'STX16', 'STX12', 'VPS51', 'GOLGA5', 'SNX7', 'GOSR2', 'ARL1', 'ANKAR', 'ARFRP1', 'GOLGB1', 'STXBP4', 'COG6', 'POLQ', 'VAPA', 'TMF1', 'GOLGA4', 'GOLGA2', 'SNAP25', 'SNAP29', 'GOLPH3', 'SCFD1', 'NAPG', 'VPS54', 'UBQLN1', 'SYTL4', 'TRIP11', 'STX6', 'COG2', 'COL7A1', 'SNX4', 'TXLNA', 'COG4', 'GOSR1', 'NBAS', 'NAPA', 'USO1', 'SNAP23', 'TXLNB', 'CNIH2', 'ACBD3', 'RINT1', 'COG5', 'SNTG2', 'NSF', 'VTI1B', 'RGP1', 'STX7', 'GOLGA8A', 'GOPC', 'CPLX1', 'TXLNG', 'GOLGA3', 'VTI1A', 'RAB43', 'RGPD6', 'VAMP4', 'COG7', 'COG3', 'VPS45', 'GOLGA1', 'mir-155']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Mast_FZ_mod_60 :  module_plots/overview_plot_Mast_FZ_mod_60.png
['AKR1C3', 'SLC14A1', 'CBX1', 'NAGK', 'PRDX6', 'B3GALT4', 'PLA2G4A', 'GBGT1', 'YBX3', 'PGM3', 'RNF123', 'GALM', 'NFAT5', 'PSIP1', 'GANC', 'A4GALT', 'NFE2L2', 'AMDHD2', 'NAGA', 'GLRX', 'TOMM34', 'AKR1B1', 'BCAT2', 'BCKDHA', 'QDPR', 'CYP2E1', 'POR', 'GSR', 'SLC14A2', 'GSS', 'UAP1L1', 'CHIT1', 'KEAP1', 'UAP1', 'TXNRD1', 'HEXB', 'COX17', 'GLB1', 'BCAT1', 'GLA', 'HEXA', 'GGT7', 'CIDEC', 'GCLC', 'GNPNAT1', 'PGD', 'mir-155']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Mast_FZ_mod_113 :  module_plots/overview_plot_Mast_FZ_mod_113.png
['DYRK2', 'RAB27B', 'GLG1', 'GYS1', 'LAMP1', 'SELP', 'TPP2', 'MBTPS1', 'DPYSL2', 'GRB14', 'LAMP2', 'CD63', 'ERAP2', 'MYRIP', 'NTF3', 'GPSM3', 'PLXNA2', 'FAM3C', 'GSK3A', 'SEMA3A', 'mir-122']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Mast_RZ_mod_159 :  module_plots/overview_plot_Mast_RZ_mod_159.png
['ELN', 'RAB4A', 'LOXL3', 'TBC1D16', 'MFGE8', 'SLTM', 'KHDRBS3', 'FBN1', 'SEPTIN10', 'PRR16', 'TAF1', 'TBP', 'TFAP2C', 'TRIM73', 'FBLN5', 'LGALS3']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Myeloid_FZ_mod_171 :  module_plots/overview_plot_Myeloid_FZ_mod_171.png
['MAP3K10', 'SCARF1', 'HSPD1', 'LYRM4', 'SH3RF1', 'SCP2', 'CYP2R1', 'POU2F1', 'FDX1', 'RXRA', 'ETS2', 'RXRB', 'OLR1', 'LIPA', 'NFS1', 'SCARB1', 'MAP2K7', 'MAP2K4', 'HSCB', 'CYP27A1', 'TOX', 'HSPA4', 'let-7g', 'mir-125b', 'mir-221', 'mir-222']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Myeloid_FZ_mod_53 :  module_plots/overview_plot_Myeloid_FZ_mod_53.png
['PCYT2', 'GPCPD1', 'PCYT1A', 'PLD3', 'KAT5', 'BACE1', 'PNPLA6', 'USP34', 'RTN3', 'PNPLA7', 'PLB1', 'CHPT1', 'BLZF1', 'RNF146', 'CHKB', 'CHKA', 'LYPLA1', 'HINFP', 'ETNK1', 'ANKRD6', 'AXIN1', 'PLA2G15', 'AXIN2']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Myeloid_FZ_mod_154 :  module_plots/overview_plot_Myeloid_FZ_mod_154.png
['COL5A3', 'P4HA3', 'COL24A1', 'ADGRG1', 'LUM', 'CD93', 'DDR2', 'COL11A1', 'COL5A2', 'GP6', 'ASPN', 'PCOLCE', 'COL3A1', 'BMP1', 'PCOLCE2', 'SDC4', 'TLL1', 'ITGA4', 'COL5A1', 'ZNF79', 'TLL2', 'LAIR1', 'ZNF469', 'mir-29a']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Myeloid_IZ_mod_96 :  module_plots/overview_plot_Myeloid_IZ_mod_96.png
['LTBP1', 'LOXL1', 'ELN', 'HSPA5', 'LOXL3', 'LTBP3', 'FBN1', 'BMP5', 'LTBP2', 'MFAP5', 'LOX', 'LOXL2', 'EFEMP2', 'MFAP1', 'FBLN5']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Myeloid_IZ_mod_33 :  module_plots/overview_plot_Myeloid_IZ_mod_33.png
['MFSD11', 'TRPV3', 'KCNMB4', 'TRPV1', 'PGM1', 'S100A1', 'KCNMA1', 'NAPEPLD', 'TRPC1', 'TRPM3', 'KCNMB3']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Neuronal_FZ_mod_77 :  module_plots/overview_plot_Neuronal_FZ_mod_77.png
['PSAP', 'CALCRL', 'DCLK3', 'KCTD16', 'CALCB', 'SLC31A2', 'VIPR1', 'GRM1', 'PRKN', 'ATG5', 'RAMP1', 'GABRG3', 'CELSR1', 'GABRB3', 'PRICKLE2', 'GABBR1', 'CRCP', 'GABBR2', 'GPR37']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Neuronal_IZ_mod_202 :  module_plots/overview_plot_Neuronal_IZ_mod_202.png
['ITIH4', 'LMX1B', 'MFN2', 'FPR1', 'LCP1', 'NTNG1', 'C5AR1', 'GPER1', 'KPNA6', 'PGF', 'NPM2', 'SPG11', 'CCL22', 'CD34', 'LGALS8', 'EDA', 'TNFRSF19', 'BTN3A1', 'FN1', 'PRSS57', 'TWIST2', 'IL17RC', 'TRPV5', 'ITGAM', 'TSHR', 'TGM2', 'LRP4', 'C5', 'ZNF160', 'LMLN', 'EDARADD', 'BTN3A2', 'TGFBI', 'CANX', 'LRRC4B', 'C5AR2', 'FSCN1', 'PTRH1', 'SERPINF2', 'MAG', 'TLR4', 'PACS2', 'THY1', 'mir-106a']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Neuronal_IZ_mod_85 :  module_plots/overview_plot_Neuronal_IZ_mod_85.png
['CTSS', 'AIFM1', 'BCL2L2-PABPN1', 'RCBTB1', 'BANF1', 'ST5', 'CTSH', 'MTHFD1L', 'FHIT', 'MADD', 'CRADD', 'COL18A1', 'GART', 'MTCH2', 'SCUBE2', 'BCL2L11', 'VDAC2', 'CSNK1G3', 'MTHFS', 'LGMN', 'CTSL', 'BCL2L2', 'CTSK', 'KNG1', 'CTSG', 'VRK2', 'CTSZ', 'MMP11', 'NLRP3', 'ALDH1L1', 'CTSB', 'TTBK2', 'VRK3', 'PRF1', 'NMT1', 'MTHFD1', 'BGLAP', 'VRK1', 'TNFRSF25', 'BID', 'mir-135a']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Neuronal_IZ_mod_57 :  module_plots/overview_plot_Neuronal_IZ_mod_57.png
['LOXL1', 'LOXL3', 'SLC44A2', 'LGALS1', 'ELN', 'HDAC2', 'SLC44A1', 'MFAP5', 'LOXL2', 'BMP1', 'MAX', 'ARNT', 'LRP6', 'AHRR', 'HAND2', 'KEAP1', 'WDR90', 'RNFT2', 'PTK7', 'FBLN5', 'POSTN', 'mir-126', 'mir-29c']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Pericyte_IZ_mod_50 :  module_plots/overview_plot_Pericyte_IZ_mod_50.png
['ANTXR2', 'PTX3', 'FAM20A', 'FAM20C', 'HTRA1', 'C2', 'ERO1A', 'CFI', 'PCSK6', 'CFH', 'PITRM1', 'CRP', 'MLANA', 'FGF23', 'COL8A1', 'MT-ND5', 'MYOM2', 'MATN2', 'CD46', 'PCSK7', 'EFEMP1', 'VWF', 'HRC', 'GP1BB', 'FAM20B']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Pericyte_IZ_mod_62 :  module_plots/overview_plot_Pericyte_IZ_mod_62.png
['PLAGL1', 'MRPS7', 'ABCC4', 'ABCG2', 'ABCC1', 'ABCC3', 'CIAPIN1', 'SLCO3A1', 'SLCO2A1', 'ABCB1', 'YBX1', 'NFE2L2', 'KLF12', 'ABCC10', 'ABCC5', 'KMT2A', 'GABPA', 'STOX1', 'CCNC', 'SLCO2B1', 'mir-382']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 Pericyte_IZ_mod_228 :  module_plots/overview_plot_Pericyte_IZ_mod_228.png
['ELOVL7', 'DLAT', 'PPARA', 'RPS14', 'DLD', 'PDHA2', 'GCDH', 'PDHB', 'PDPR', 'TRABD2B', 'PDK3', 'PAXBP1', 'PDK4', 'AADAT', 'PDHA1', 'UCP3', 'DHTKD1', 'PDK2', 'RIOK1', 'PDHX', 'DLST']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 vSMCs_FZ_mod_220 :  module_plots/overview_plot_vSMCs_FZ_mod_220.png
['BUB3', 'FBXO42', 'TNFRSF1A', 'CEP350', 'CDC26', 'IGFBP2', 'CDC14A', 'TP53', 'HES6', 'MSC', 'IGFL3', 'MKRN1', 'NRK', 'SLC19A1', 'IGF2', 'KLF4', 'CD24', 'ANAPC1', 'CYLD', 'ARMC10', 'SMYD2', 'ANAPC11', 'SLC19A2', 'NIPA2', 'BCL3', 'UBE4A', 'SLC19A3', 'PAPPA2', 'IGFBP4', 'mir-491', 'mir-378a', 'mir-373', 'let-7a', 'mir-663b', 'mir-34a', 'let-7b']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 vSMCs_FZ_mod_273 :  module_plots/overview_plot_vSMCs_FZ_mod_273.png
['RPS6KA3', 'PAOX', 'NTRK3', 'KLF11', 'SCN7A', 'AOC3', 'ADH1B', 'NAGK', 'SMOX', 'COMT', 'INMT', 'ALDH3A2', 'MOCOS', 'ADI1', 'NEMF', 'GLO1', 'PGM3', 'ANKZF1', 'MAOA', 'RIT2', 'SCN4A', 'NAV1', 'SAT1', 'GRHPR', 'ANK3', 'SCN3A', 'SMS', 'GLDN', 'PON2', 'HNMT', 'TCF25', 'SAT2', 'MAOB', 'UAP1', 'ALDH2', 'NOP56', 'AMD1', 'NFASC', 'HTR2A', 'SCN2A', 'mir-141', 'mir-200a', 'mir-200b', 'mir-200c', 'mir-429', 'mir-124']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 vSMCs_FZ_mod_1 :  module_plots/overview_plot_vSMCs_FZ_mod_1.png
['PREB', 'ABCG1', 'GPCPD1', 'PCYT1A', 'TPCN2', 'PLTP', 'DTNB', 'PSKH1', 'LCAT', 'ABCA1', 'PNPLA6', 'GJC2', 'ZDHHC8', 'PNPLA7', 'PLB1', 'SNTB2', 'SCARB1', 'LYPLA1', 'MCOLN1', 'PLA2G15', 'NR1H2', 'TPCN1', 'OSBPL8']
40

/mnt/raidbio/extproj/projekte/regulatory_networks/myocardial/../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 vSMCs_IZ_mod_251 :  module_plots/overview_plot_vSMCs_IZ_mod_251.png

xTotalCount = 0
for x in tlda.cellgroupdata:
    xcommcount = len(tlda.cellgroupdata[x]["communities"])
    print(x, xcommcount)
    xTotalCount += xcommcount

xTotalCount

Adipocyte 7
Cardiomyocyte 38
Cycling cells 16
Endothelial 17
Fibroblast 28
Lymphoid 19
Mast 37
Myeloid 21
Neuronal 27
Pericyte 24
vSMCs 32

266

#captured_describe_module()

fibModules=rel_modules[rel_modules.base_condition.str.startswith("Fibroblast")]
fibModules

	name	gene_nodes	geneset_nodes	disease_nodes	drug_nodes	ncRNA_nodes	TF_nodes	other_nodes	RZ_score_median	RZ_score_mean	...	BZ_absmean-gene	BZ_absmean-disease	BZ_absmean-drug	BZ_diffmean-gene	BZ_diffmean-disease	BZ_diffmean-drug	base_condition	base_zone	base_condition_score_mean	base_condition_score_median
100	Fibroblast_FZ_mod_47	[AASS, ABAT, ACACB, ACADS, ACAT1, ACOX1, ACOX3...	[(R-HSA-380615, Serotonin clearance from the s...	[]	[(CHEMBL75880, PERHEXILINE), (CHEMBL89598, VIG...	[mir-16, mir-34a]	[ZNF236]	{}	-0.023797	-0.018153	...	0.195681	NaN	0.169805	-0.098433	NaN	-0.109175	Fibroblast_FZ	FZ	0.437499	0.378999
105	Fibroblast_FZ_mod_48	[CNTFR, CRLF1, CSF3R, CYP46A1, ELP2, EPOR, GHR...	[(GO:0004897, ciliary neurotrophic factor rece...	[]	[(CHEMBL1201573, OPRELVEKIN), (CHEMBL2108583, ...	[mir-106b, mir-107, mir-125a, mir-15a, mir-17,...	[NFX1, PLAG1, ZNF384]	{}	-0.041160	-0.107902	...	0.166516	NaN	0.195934	-0.145342	NaN	-0.233029	Fibroblast_FZ	FZ	0.384016	0.435448
89	Fibroblast_IZ_mod_217	[EPRS, ERO1A, F3, F8, FAM20A, FAM20C, FGF23, G...	[(R-HSA-9672391, Defective F8 cleavage by thro...	[]	[(CHEMBL2109624, CAPLACIZUMAB), (CHEMBL3707326...	[mir-130a, mir-27a, mir-494]	[]	{}	-0.071981	-0.122831	...	0.172814	NaN	0.077115	-0.157316	NaN	-0.039010	Fibroblast_IZ	IZ	0.820749	0.945415
84	Fibroblast_IZ_mod_127	[ARSA, CCDC180, CDK10, CDK12, CDK13, CDK16, CD...	[(GO:0021817, nucleokinesis involved in cell m...	[(Orphanet:98853, Autosomal dominant Emery-Dre...	[(CHEMBL2105661, LITRONESIB)]	[]	[ZNF276]	{}	-0.012351	-0.008685	...	0.282730	0.476562	0.162619	0.014536	0.119571	-0.100303	Fibroblast_IZ	IZ	0.771814	0.734109
96	Fibroblast_IZ_mod_190	[BGLAP, CD74, COL18A1, CSTB, CTSB, CTSC, CTSD,...	[(GO:0036021, endolysosome lumen)]	[]	[(CHEMBL2108252, APOLIZUMAB), (CHEMBL481611, O...	[mir-101, mir-124, mir-199a, mir-204, mir-211,...	[KLF6]	{}	-0.181733	-0.163348	...	0.130225	NaN	0.152906	-0.149464	NaN	-0.087640	Fibroblast_IZ	IZ	0.469341	0.185855

5 rows × 48 columns

descrDF

descrDF.columns

Index(['name', 'gene_nodes', 'geneset_nodes', 'disease_nodes', 'drug_nodes',
       'ncRNA_nodes', 'TF_nodes', 'other_nodes', 'RZ_score_median',
       'RZ_score_mean', 'RZ_cohend', 'RZ_absmean-gene', 'RZ_absmean-disease',
       'RZ_absmean-drug', 'RZ_diffmean-gene', 'RZ_diffmean-disease',
       'RZ_diffmean-drug', 'IZ_score_median', 'IZ_score_mean', 'IZ_cohend',
       'IZ_absmean-gene', 'IZ_absmean-disease', 'IZ_absmean-drug',
       'IZ_diffmean-gene', 'IZ_diffmean-disease', 'IZ_diffmean-drug',
       'FZ_score_median', 'FZ_score_mean', 'FZ_cohend', 'FZ_absmean-gene',
       'FZ_absmean-disease', 'FZ_absmean-drug', 'FZ_diffmean-gene',
       'FZ_diffmean-disease', 'FZ_diffmean-drug', 'BZ_score_median',
       'BZ_score_mean', 'BZ_cohend', 'BZ_absmean-gene', 'BZ_absmean-disease',
       'BZ_absmean-drug', 'BZ_diffmean-gene', 'BZ_diffmean-disease',
       'BZ_diffmean-drug', 'base_condition', 'base_zone',
       'base_condition_score_mean', 'base_condition_score_median'],
      dtype='object')

sgdf.index

Index(['FZ', 'IZ'], dtype='object', name='base_zone')

dcolors = {"FZ": "red", "BZ": "blue", "RZ": "green", "IZ": "orange"}

tlda.plot_module_description(nrow=2, figsize=(20,8), dcolors=dcolors)

6 2 12 11

aid = AIDescriptor()

context = "fibroblasts of human heart"
for fibmod in fibModules.name:
    print(fibmod)

    res=aid.query_genelist(tlda.communities[fibmod], context, verbose=True, word_cloud=True)
    plt.show()
    plt.close()

    print(res)
    print()

Fibroblast_FZ_mod_47
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 fibroblasts of human heart: AOX1, MCCC1, ABAT, ACOX1, MDH1, ADH1B, INMT, HIBADH, ALDH3A2, MOCOS, SCP2, ACSF3, ACOX3, TECR, ALDH6A1, MLYCD, DBT, CD38, ZNF236, AASS, GCDH, CAT, PC, RDH11, BST1, MAOA, PCCB, MOCS1, RDH5, ACSS2, DHRS4, NNMT, PHYKPL, ECHDC1, CROT, ACSS1, PCCA, CPT1C, ECH1, SIRT7, CPT1A, ACAT1, PDXK, CPT1B, DHRS3, HNMT, SIRT5, ASL, ACACB, ADH5, DHTKD1, AKAP12, MAOB, EHHADH, ME2, MAP4K5, SUCLG1, PDE7A, ALDH2, ALDH1A1, BTD, ACADS, ALDH7A1, DLST, SIRT3, AGMO, ACSS3, MOCS2, HACL1, HLCS. 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 fibroblasts of human heart, indicating that they may play a role in the development or progression of cardiovascular diseases. The molecular functions of these genes include oxidoreductase activity, catalytic activity, electron carrier activity, and transporter activity. Some of these genes are involved in the metabolism of fatty acids, amino acids, and vitamins, while others are involved in the detoxification of drugs and xenobiotics. The dysregulation of these genes may lead to an imbalance in cellular metabolism and homeostasis, which can contribute to the development of cardiovascular diseases.

Fibroblast_FZ_mod_48
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 fibroblasts of human heart: SOAT1, IL13RA1, IL12RB2, CSF3R, USP18, IL27RA, EPOR, SNX8, LIFR, SOCS2, IL6ST, IL11RA, ELP2, PRLR, PLAG1, CNTFR, IL17D, NFX1, IL10RA, IL7R, SOCS5, ZNF384, JAK1, CRLF1, JAK2, TYK2, GHR, CYP46A1, TUB. 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 fibroblasts of human heart, indicating that they may play a role in the development or progression of cardiovascular diseases. The molecular functions of these genes include receptor binding, protein kinase activity, transcription factor activity, and signaling pathway regulation. Some of these genes are involved in the IL-6 signaling pathway, which is known to be associated with inflammation and fibrosis in the heart. Other genes are involved in the JAK-STAT signaling pathway, which is also important for cardiovascular diseases. The dysregulation of these genes may lead to changes in cellular processes such as proliferation, differentiation, and apoptosis, which can contribute to the development or progression of cardiovascular diseases.

Fibroblast_IZ_mod_217
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 fibroblasts of human heart: GFM1, IL18, TNF, ERO1A, EPRS, GP1BB, SERPINC1, F3, IL18BP, TFPI, PITRM1, FAM20A, FGF23, FAM20C, VWF, F8. 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 fibroblasts of human heart are involved in various biological processes such as inflammation, coagulation, complement system, cell adhesion, and protein folding. These genes are connected through their involvement in the same pathways or molecular functions. For example, IL18 and IL18BP are both involved in the regulation of interleukin-18 (IL-18) signaling pathway, which is important for inflammation. TNF and TFPI are both involved in the regulation of tumor necrosis factor (TNF) signaling pathway, which is also related to inflammation. GP1BB and F3 are both involved in the regulation of platelet activation, which is important for coagulation. ERO1A and SERPINC1 are both involved in protein folding, which is important for maintaining cellular homeostasis. PITRM1 and FAM20A are both involved in the regulation of RNA metabolism, which is important for gene expression. FGF23 and FAM20C are both involved in the regulation of phosphate and calcium metabolism, which is important for maintaining bone health. VWF is involved in blood coagulation, which is related to platelet activation. Overall, these genes are connected through their involvement in various biological processes that are important for maintaining heart function.

Fibroblast_IZ_mod_127
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 fibroblasts of human heart: SUN1, NES, CDK17, UBR1, CTSA, CSNK2B, MKNK2, IDUA, CDK12, RECQL4, TMEM67, LMNA, FHOD3, SYNE2, KIF11, UBR2, CDK10, CCDC180, ZNF276, SYNE1, ARSA, UBN1, FHOD1, DST, SYNM, CDK13, CDK16, LMNB2, STMN1, PLEC, LMNB1, GALNS, TMEM201. 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 fibroblasts of human heart, indicating that they may play a role in the development or progression of cardiovascular diseases. The genes are involved in various molecular functions such as protein binding, protein kinase activity, protein transport, and cell cycle regulation. They also participate in different biological processes including DNA repair, chromatin organization, and cell division. Some of these genes are associated with each other through protein-protein interactions or pathways. For example, SUN1 interacts with NES, CDK17, UBR1, CTSA, CSNK2B, MKNK2, IDUA, CDK12, RECQL4, TMEM67, LMNA, FHOD3, SYNE2, KIF11, UBR2, CDK10, CCDC180, ZNF276, SYNE1, ARSA, UBN1, FHOD1, DST, SYNM, CDK13, CDK16, LMNB2, STMN1, PLEC, LMNB1, GALNS, and TMEM201. These interactions suggest that they may form complexes or participate in the same pathways. However, further research is needed to understand how these genes are connected and their specific roles in cardiovascular diseases.

Fibroblast_IZ_mod_190
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 fibroblasts of human heart: CTSS, CTSH, GRN, COL18A1, HLA-DPB1, SDF4, LGMN, CTSL, CTSC, KLF6, SPHK1, CTSK, LAMP2, HLA-DRB5, CTSD, CTSZ, CD74, CTSB, DLK1, BGLAP, M6PR, CSTB, GNS, HLA-DQA1. 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 all involved in various aspects of protein processing and degradation. The proteasome is a complex that breaks down damaged or misfolded proteins into smaller peptides, which can then be further degraded by lysosomes. The proteasome consists of two main subunits: the 20S core particle and the 19S regulatory particle. CTSS, CTSH, CTSL, CTSC, CTSD, CTSB, and CTSZ are all involved in the formation and function of the 20S core particle. GRN is involved in the formation of the 19S regulatory particle. HLA-DPB1, HLA-DRB5, and HLA-DQA1 are major histocompatibility complex (MHC) class II molecules that present antigens to T cells. SDF4, LGMN, and LAMP2 are involved in the transport of proteins into lysosomes. BGLAP encodes osteocalcin, a protein that is important for bone formation. M6PR is involved in the transport of proteins from the endoplasmic reticulum to the Golgi apparatus. SPHK1 is involved in the phosphorylation of sphingosine to sphingosine-1-phosphate, which is a signaling molecule that regulates various cellular processes. KLF6 is a transcription factor that is involved in the regulation of gene expression. DLK1 encodes a protein that is important for embryonic development. GNS encodes gelsolin, a protein that is important for the organization of the cytoskeleton.

[x for x in tlda.cellgroupdata]

['Adipocyte',
 'Cardiomyocyte',
 'Cycling cells',
 'Endothelial',
 'Fibroblast',
 'Lymphoid',
 'Mast',
 'Myeloid',
 'Neuronal',
 'Pericyte',
 'vSMCs',
 'Fibroblasts']

subgroup_scores = dict()
for sg in tlda.cellgroupdata["Fibroblast"]["kg"]:
    subgroup_scores[sg] = tlda.cellgroupdata["Fibroblast"]["kg"][sg].get_edge_scores(score_accessor=lambda x: x.get("fc_score", 0))

df = pd.DataFrame.from_dict(subgroup_scores)

small_subgroup_scores = {x: random.sample(subgroup_scores[x], 100000) for x in subgroup_scores}
dfs = pd.DataFrame.from_dict(small_subgroup_scores)

sns.violinplot(data=df)

<Axes: >

df.head()

	Fibroblast_RZ	Fibroblast_BZ	Fibroblast_IZ	Fibroblast_FZ
0	-0.259545	-0.202832	-0.947941	-0.427959
1	-0.007344	0.033568	-0.064005	-0.034950
2	-0.319475	-0.210355	-0.728131	-0.417255
3	-0.152492	-0.126864	-0.367229	-0.260261
4	-0.215309	-0.297717	-1.242256	-0.519471

dfl = df.melt()
dfl.head()

	variable	value
0	Fibroblast_RZ	-0.259545
1	Fibroblast_RZ	-0.007344
2	Fibroblast_RZ	-0.319475
3	Fibroblast_RZ	-0.152492
4	Fibroblast_RZ	-0.215309

varnames = sorted(dfl.variable.unique())
gs = (matplotlib.gridspec.GridSpec(len(varnames),1))
colors=["#DD5129FF", "#0F7BA2FF", "#43B284FF", "#FAB255FF"]

fig = plt.figure(figsize=(8,6))

i = 0

#creating empty list
ax_objs = []

for var in varnames:
    # creating new axes object and appending to ax_objs
    ax_objs.append(fig.add_subplot(gs[i:i+1, 0:]))

    # plotting the distribution
    plot = (dfl[dfl.variable == var]
            .value.plot.kde(ax=ax_objs[-1],color=colors[i], lw=1)
           )

    # grabbing x and y data from the kde plot
    x = plot.get_children()[0]._x
    y = plot.get_children()[0]._y

    # filling the space beneath the distribution
    ax_objs[-1].fill_between(x,y,color=colors[i])

    # setting uniform x and y lims
    #ax_objs[-1].set_xlim(0, 1)
    #ax_objs[-1].set_ylim(0,2.2)
    ax_objs[-1].set_title(var)

    i += 1

plt.tight_layout()
plt.show()

objkg = tlda.cellgroupdata["Fibroblast"]["communities_enhanced"]["Fibroblast_IZ_mod_127"]
for gene in objkg.kg.nodes:

    hasEdge = (gene, "NTN1") in objkg.kg.edges or ("NTN1", gene) in objkg.kg.edges
    print(gene, hasEd)

SUN1
NES
CDK17
UBR1
CTSA
MKNK2
CSNK2B
IDUA
CDK12
RECQL4
TMEM67
LMNA
GO:0021817
FHOD3
Orphanet:98853
SYNE2
KIF11
UBR2
CDK10
CCDC180
ZNF276
SYNE1
ARSA
UBN1
FHOD1
DST
SYNM
CDK13
CDK16
LMNB2
STMN1
PLEC
LMNB1
GALNS
TMEM201
CHEMBL2105661

allSigKG = dict(tlda.cellgroupdata["Myeloid"]["communities_enhanced"], **tlda.cellgroupdata["Cardiomyocyte"]["communities_enhanced"], **tlda.cellgroupdata["vSMCs"]["communities_enhanced"], **tlda.cellgroupdata["Fibroblast"]["communities_enhanced"])

mc = ModuleCompare()

jaccardSims = mc.network_compare_modules(allSigKG, measure="jaccard", borderWeightQuantile=0.95)

mc.plot_dendrogram(jaccardSims, figsize=(16, 4), color_threshold=0.9)

jdf = mc.module_similarities_to_df(jaccardSims)
jdf

	Module1	Module2	Similarity
256	Myeloid_IZ_mod_82	Cardiomyocyte_IZ_mod_113	0.550000
3436	Cardiomyocyte_IZ_mod_169	Fibroblast_IZ_mod_154	0.532258
3893	Cardiomyocyte_FZ_mod_72	vSMCs_FZ_mod_236	0.488372
2599	Cardiomyocyte_IZ_mod_113	vSMCs_IZ_mod_190	0.444444
5291	vSMCs_IZ_mod_11	Fibroblast_IZ_mod_12	0.428571
...	...	...	...
2428	Cardiomyocyte_BZ_mod_132	vSMCs_FZ_mod_157	0.000000
2427	Cardiomyocyte_BZ_mod_132	vSMCs_FZ_mod_24	0.000000
2426	Cardiomyocyte_BZ_mod_132	vSMCs_FZ_mod_144	0.000000
2425	Cardiomyocyte_BZ_mod_132	vSMCs_FZ_mod_45	0.000000
2438	Cardiomyocyte_BZ_mod_132	Fibroblast_IZ_mod_64	0.000000

7021 rows × 3 columns

mods = sorted(set(list(jdf.Module1) + list(jdf.Module2)))
pjdf = pd.DataFrame( np.diag( [1.0]*(len(mods)) ), index=mods, columns=mods)

for ri, row in jdf.iterrows():
    pjdf.loc[row["Module1"], row["Module2"] ] = row["Similarity"]
    pjdf.loc[row["Module2"], row["Module1"] ] = row["Similarity"]

colors = [x.split("_")[0] for x in pjdf.index]

lut = dict(zip(set(colors), "rbgr"))
row_colors = pd.Series(colors).map(lut)
row_colors.columns = "Tissue"
sns.clustermap(pjdf, row_colors=row_colors.values, figsize=(25,25), dendrogram_ratio=0.1, colors_ratio=0.03, cbar_pos=(0.02, 0.95, 0.05, 0.05), xticklabels=True, yticklabels=True)

/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)
/mnt/extproj/projekte/bartelt/software/miniconda3/envs/regnetworks/lib/python3.11/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)

<seaborn.matrix.ClusterGrid at 0x7f808cd4b690>

emb = mc.module_pca(allSigKG, kg)

Looks like you are using a tranform that doesn't support FancyArrowPatch, using ax.annotate instead. The arrows might strike through texts. Increasing shrinkA in arrowprops might help.

ns = NETSIM(kg)

modNetSims = mc.network_compare_netsim(allSigKG, ns=ns, max_terms=2, borderWeightQuantile=0.95)

|#########################################################| 100% Time:  1:10:5756

mc.plot_dendrogram(modNetSims, figsize=(18,4))

singlecatKGs = dict(tlda.cellgroupdata["Fibroblast"]["communities_enhanced"])

modNetSims_single = mc.network_compare_netsim(singlecatKGs, ns=ns, max_terms=2, borderWeightQuantile=0.95)

|#########################################################| 100% Time:  0:05:3047

mc.plot_dendrogram(modNetSims_single, figsize=(18,4))

emb = mc.module_pca(singlecatKGs, kg)