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Supplementary Information
Network Analysis Reveals Functional Cross-links between Disease
and Inflammation Genes
Yunpeng Zhang, Huihui Fan, Juan Xu, Yun Xiao, Yanjun Xu, Yixue Li, Xia Li
Inventory of Supplementary Information
1. Supplementary Figure 1
2. Supplementary Figure 2
3. Supplementary Figure 3
4. Supplementary Figure 4
5. Supplementary Figure 5
6. Supplementary Figure 6
7. Supplementary Figure 7
8. Supplementary Table 1
Supplementary Figure 1.
Supplementary Figure 1. Degree comparison between inflammation genes, disease genes, and
non-disease genes in the human PPI network. Via mapping inflammation genes and disease genes
onto the human PPI network, we could further compare the degree distribution between them
using Wilcoxon's Rank-Sum Test. Inflammation genes and disease genes were defined in the main
text, while non-disease genes are the remaining nodes in the human PPI after mapping both
inflammation and disease genes. *** indicates that the comparison between inflammation genes
with non-disease genes, and the comparison of disease genes with non-disease genes are
significant (p=6.8293e-07 and p= 2.4687e-75, separately).
Supplementary Figure 2.
Supplementary Figure 2. Mean degree in comparison with random distribution. 1000 random
DINs were generated via mapping inflammation genes and disease genes to the random PPI
network. The real mean degree of the DIN is shown by a red downward arrow, which is
significantly higher when comparing with the random mean degree distribution of 1000 random
DINs.
Supplementary Figure 3.
Supplementary Figure 3. Mean clustering coefficient in comparison with random distribution.
1000 random DINs were generated via mapping inflammation genes and disease genes to the
random PPI network. The real mean clustering coefficient of the DIN is shown by a red downward
arrow, which is significantly higher when comparing with the random mean clustering coefficient
distribution of 1000 random DINs.
Supplementary Figure 4.
Supplementary Figure 4. Mean topological coefficient in comparison with random distribution.
1000 random DINs were generated via mapping inflammation genes and disease genes to the
random PPI network. The real mean topological coefficient of the DIN is shown by a red
downward arrow, which is significantly higher when comparing with the random mean
topological coefficient distribution of 1000 random DINs.
Supplementary Figure 5.
Supplementary Figure 5. Example gene module of disease pair of cardiovascular and metabolic.
The subnetwork was generated by mapping disease genes of cardiovascular and metabolic to the
human PPI network. Genes that belongs to both inflammation and disease genes are marked with
black border.
Supplementary Figure 6.
Supplementary Figure 6. Example gene module of disease pair of cardiovascular and immune.
The subnetwork was generated by mapping disease genes of cardiovascular and immune to the
human PPI network. Genes that belongs to both inflammation and disease genes are marked with
black border.
Supplementary Figure 7.
Supplementary Figure 7. Example gene module of disease pair of aging and cancer. The
subnetwork was generated by mapping disease genes of aging and cancer to the human PPI
network. Genes that belongs to both inflammation and disease genes are marked with black
border.
Supplementary Table 1. rank of the sum of Intimacy for disease pairs
Disease_1
Disease_2
Intimacy_1
Intimacy_2
Sum of Intimacy
immune
infection
0.549388
0.817886
1.367274
cardiovascular
metabolic
0.672727
0.639988
1.312715
normal variation
metabolic
0.878205
0.422304
1.300509
cardiovascular
immune
0.658299
0.63107
1.289369
aging
cancer
0.853896
0.434055
1.287951
cardiovascular
hematological
0.45699
0.816824
1.273814
aging
reproduction
0.745671
0.519902
1.265573
cardiovascular
kidney
0.462431
0.802083
1.264514
aging
immune
0.837662
0.42156
1.259222
cancer
cardiovascular
0.598671
0.632266
1.230937
cancer
metabolic
0.612623
0.615188
1.227811
metabolic
reproduction
0.477757
0.747059
1.224816
cancer
immune
0.606499
0.610367
1.216866
kidney
metabolic
0.77753
0.431577
1.209107
cancer
infection
0.497111
0.707317
1.204428
hematological
reproduction
0.653302
0.52902
1.182322
immune
metabolic
0.590459
0.588909
1.179368
immune
kidney
0.431621
0.747321
1.178942
metabolic
neurological
0.562877
0.614864
1.177741
hematological
immune
0.727201
0.425352
1.152553
hematological
metabolic
0.73978
0.412186
1.151966
aging
infection
0.6829
0.466504
1.149404
cancer
kidney
0.424148
0.720238
1.144386
neurological
reproduction
0.477724
0.665196
1.14292
infection
metabolic
0.666423
0.465958
1.132381
normal variation
cancer
0.732265
0.396245
1.12851
infection
kidney
0.501545
0.625
1.126545
cardiovascular
vision
0.444594
0.678267
1.122861
Psychological
metabolic
0.595051
0.52406
1.119111
immune
vision
0.430612
0.680667
1.111279
infection
neurological
0.625772
0.482171
1.107943
kidney
pharmacogenomic
0.619048
0.486099
1.105147
aging
kidney
0.598485
0.504762
1.103247
metabolic
vision
0.426543
0.670933
1.097476
kidney
neurological
0.665179
0.424599
1.089778
kidney
reproduction
0.589286
0.489314
1.0786
hematological
kidney
0.545597
0.531101
1.076698
hematological
neurological
0.661164
0.415064
1.076228
cancer
vision
0.431542
0.6436
1.075142
aging
hematological
0.555628
0.499528
1.055156
reproduction
vision
0.501765
0.551467
1.053232
neurological
vision
0.429006
0.622
1.051006
aging
vision
0.56645
0.455067
1.021517
kidney
vision
0.533333
0.484267
1.0176
pharmacogenomic
vision
0.472999
0.542933
1.015932
normal variation
reproduction
0.574573
0.435098
1.009671
hematological
vision
0.527044
0.468267
0.995311
normal variation
kidney
0.526709
0.450446
0.977155
Psychological
kidney
0.404695
0.55506
0.959755
infection
vision
0.455772
0.500667
0.956439
normal variation
vision
0.524359
0.428267
0.952626
Psychological
vision
0.40843
0.524133
0.932563
chemdependency
aging
0.41292
0.49026
0.90318
normal variation
developmental
0.471795
0.409592
0.881387
developmental
vision
0.43729
0.4224
0.85969
chemdependency
vision
0.414083
0.406
0.820083
neurological
pharmacogenomic
0.527003
0.664265
1.191268
normal variation
hematological
0.446581
0.425314
0.871895
chemdependency
kidney
0.40155
0.415923
0.817473
normal variation
cardiovascular
0.818376
0.419766
1.238142
normal variation
aging
0.50406
0.507576
1.011636
developmental
kidney
0.414988
0.439732
0.85472
chemdependency
infection
0.42584
0.390325
0.816165
normal variation
immune
0.74359
0.392508
1.136098
metabolic
pharmacogenomic
0.511984
0.714702
1.226686
chemdependency
neurological
0.721835
0.466987
1.188822
developmental
cardiovascular
0.594724
0.419353
1.014077
Psychological
aging
0.411978
0.677489
1.089467
chemdependency
metabolic
0.638889
0.40955
1.048439
Psychological
cancer
0.545658
0.491883
1.037541
developmental
infection
0.443765
0.403577
0.847342
chemdependency
immune
0.537468
0.382844
0.920312
chemdependency
reproduction
0.465633
0.43549
0.901123
Psychological
immune
0.555115
0.497584
1.052699
hematological
infection
0.629717
0.490732
1.120449
normal variation
infection
0.552137
0.415285
0.967422
cardiovascular
infection
0.519904
0.74065
1.260554
Psychological
infection
0.44223
0.523577
0.965807
chemdependency
cancer
0.573643
0.399913
0.973556
Psychological
neurological
0.637255
0.597556
1.234811
aging
pharmacogenomic
0.725108
0.472198
1.197306
cancer
neurological
0.536713
0.571074
1.107787
aging
cardiovascular
0.865801
0.432025
1.297826
immune
pharmacogenomic
0.492355
0.663755
1.15611
infection
pharmacogenomic
0.546098
0.520306
1.066404
cardiovascular
reproduction
0.505165
0.745
1.250165
cancer
pharmacogenomic
0.534315
0.732314
1.266629
immune
reproduction
0.473945
0.70951
1.183455
cancer
hematological
0.425592
0.742138
1.16773
cardiovascular
neurological
0.600034
0.619471
1.219505
normal variation
chemdependency
0.46453
0.40814
0.87267
aging
neurological
0.861472
0.444992
1.306464
immune
neurological
0.556728
0.592989
1.149717
infection
reproduction
0.567154
0.598137
1.165291
Psychological
chemdependency
0.50789
0.76124
1.26913
normal variation
neurological
0.688462
0.401843
1.090305
aging
metabolic
0.883117
0.423075
1.306192
chemdependency
developmental
0.430491
0.426379
0.85687
Psychological
cardiovascular
0.576144
0.52624
1.102384
hematological
pharmacogenomic
0.647013
0.473872
1.120885
chemdependency
cardiovascular
0.58553
0.404098
0.989628
Psychological
pharmacogenomic
0.519401
0.604949
1.12435
chemdependency
pharmacogenomic
0.59199
0.48246
1.07445
Psychological
developmental
0.406843
0.521823
0.928666
Psychological
reproduction
0.44928
0.57598
1.02526
developmental
hematological
0.396882
0.432704
0.829586
developmental
aging
0.427818
0.496104
0.923922
developmental
immune
0.582734
0.402263
0.984997
chemdependency
hematological
0.413566
0.447642
0.861208
Psychological
hematological
0.384667
0.551101
0.935768
cardiovascular
pharmacogenomic
0.541942
0.720524
1.262466
pharmacogenomic
reproduction
0.557424
0.631275
1.188699
cancer
reproduction
0.488359
0.768824
1.257183
developmental
neurological
0.528058
0.395954
0.924012
developmental
metabolic
0.670264
0.426217
1.096481
developmental
reproduction
0.51211
0.482549
0.994659
developmental
pharmacogenomic
0.509832
0.441485
0.951317
Psychological
normal variation
0.401381
0.663462
1.064843
developmental
cancer
0.655276
0.4329
1.088176
normal variation
pharmacogenomic
0.637821
0.4377
1.075521