A Multi-Objective Genetic Algorithm to Find Active Modules from Multiplex Biological Networks: Supplementary material
Elva-Mar´ıaNovoa-del-Toro1,∗ Efr´enMezura-Montes2 Matthieu Vignes3 Fr´ed´eriqueMagdinier1 Laurent Tichit4 Ana¨ısBaudot1,∗
1Aix Marseille Univ, INSERM, Marseille Medical Genetics (MMG), Marseille, France 2University of Veracruz, Artificial Intelligence Research Center, Mexico 3School of Fundamental Sciences, Massey University, New Zealand 4Aix Marseille Univ, CNRS, Centale Marseille, I2M UMR 7373, Marseille, France ∗To whom correspondence should be addressed
Contents
Figure S1 2
Figure S2 2
Figure S3 3
Figure S4 3
Figure S5 4
Figure S6 4
Figure S7 5
Figure S8 5
Figure S9 6
Figure S10 6
Figure S11 14
Figure S12 18
Figure S13 30
Figure S14 38
Table S1 48
Table S2 48
Table S3 49
1 Figure S1: Sizes of the subnetworks identified by PinnacleZ in the experiment using the network PPI 1 and the simulated data with normal distribution
Figure S2: Sizes of the subnetworks identified by PinnacleZ in the experiment using the network PPI 2 and the sampled data from RNA-Seq TCGA breast cancer dataset
2 Figure S3: Sizes of the subnetworks identified by COSINE in the experiment using the network PPI 1 and the simulated data with normal distribution
Figure S4: Sizes of the subnetworks identified by COSINE in the experiment using the network PPI 2 and the sampled data from RNA-Seq TCGA breast cancer dataset
3 Figure S5: Sizes of the subnetworks identified by jActiveModules in the experiment using the network PPI 1 and the simulated data with normal distribution
Figure S6: Sizes of the subnetworks identified by jActiveModules in the experiment using the network PPI 2 and the sampled data from RNA-Seq TCGA breast cancer dataset
4 Figure S7: Sizes of the subnetworks identified by all the methods in the experiment using the network PPI 1 and the simulated data with normal distribution
Figure S8: Sizes of the subnetworks identified by all the methods in the experiment using the network PPI 2 and the sampled data from RNA-Seq TCGA breast cancer dataset
5 Figure S9: F1 score values of jActiveModules, COSINE, PinnacleZ, and MOGAMUN corresponding to the two experiments from the benchmark
Figure S10
Eighteen active modules obtained by applying MOGAMUN on Yao’s biopsies dataset [1] (see Table S1 for the list of samples). The color of the nodes represents the fold-change, where green and red nodes correspond to under- and over-expressed genes, respectively. Nodes with bold black border correspond to genes significantly differentially expressed (F DR < 0.05 and absolute log2 fold-change > 1). Blue and white nodes correspond to genes with no associated transcriptomics data and no deregulation, respectively.
ORC1
MCM2 ORC6
MCM6 MCM4 FCGR3A ORC5
ORC2 CDC7 MCM5 CDC45 DBF4
FCGR3B CDK2 RPA1
MCM7 MCM3
Figure S10.1. Yao’s dataset, biopsies: Active module 1
6 PRC1
POLA1 JUN CDK2 FCGR3B CDK1
MYC BARD1 CDKN1A
CCNA2 ACTC1
PCDHB4 BRCA1 EP300
ABCC8 BRCA2 TP53
Figure S10.2. Yao’s dataset, biopsies: Active module 2
STAT3 PLCG2
SOS1 SYK CBL SRC
EGFR PIK3R1
ACTC1 PIK3CG LYN AKT1
HSPB2 CXCL14 PRKDC MAPK8
Figure S10.3. Yao’s dataset, biopsies: Active module 3
ABCC8
DUSP1
VAMP2
FCGR3B HSPB2 ACTC1
FCGR3A
HCK SYK
OGDHL STAT3 LYN PLCG2
CBL
KIT PIK3CG
Figure S10.4. Yao’s dataset, biopsies: Active module 4
7 EGFR
PIK3R1 SYK
GRB2 SHC1 PLCG2
SRC PIK3R2 CBL
FYN LYN
STAT3 OGDHL ERBB2 ABL1
Figure S10.5. Yao’s dataset, biopsies: Active module 5
MAPK14 RB1 SOS1 EP300
GRB2 SYK
PLCG1 CDKN1A
AKT1 EGFR CDK1
CBL
STAT3 OGDHL PIK3R1 LYN
Figure S10.6. Yao’s dataset, biopsies: Active module 6
PLCG1
GAB1
CBL VAV1 FYN SHC1
CRK GRB2 PIK3R1 STAT3 EGFR
SRC FCGR3B ERBB2 SYK
Figure S10.7. Yao’s dataset, biopsies: Active module 7
8 STAT3 OGDHL CREBBP UBE2I
EP300 MYC
FCGR3B CKS1B JUN ATF2
FOSL1
DUSP1 JUND HSPB2
MAPK13
ACTC1
Figure S10.8. Yao’s dataset, biopsies: Active module 8
SRC GRB2
EGFR PIK3R1 MET
LYN SYK
ERBB2 SHC1 KIT
CBL FYN LCK
OGDHL STAT3
Figure S10.9. Yao’s dataset, biopsies: Active module 9
9 ACTC1
CDT1
RPA1
DBF4 MCM7 CDC7 MCM6
MCM4 CDK2 MCM2
CDK1 ORC6
ORC5 ORC2 CDKN1A ORC1
Figure S10.10. Yao’s dataset, biopsies: Active module 10
ACTC1
PFN2
ENAH RB1
MAPK1 SP1
CDK2 TP53 SRC
CCND2 CDKN1A MYC
SYK SMAD2
STAT3 CREBBP
EP300
Figure S10.11. Yao’s dataset, biopsies: Active module 11
10 CREBBP HIF1A
HDAC1 SMAD3 MAPK1 EP300
TP53 SP1
RELA MYC ESR1
JUN
RB1
FCGR3B MAPK14
Figure S10.12. Yao’s dataset, biopsies: Active module 12
PIK3R2 FCGR3A
FCGR3B PIK3R1 HCK GRB2
CBL LILRB2 GJA4
SRC KIT SYK STAT3
CREBBP
OGDHL
Figure S10.13. Yao’s dataset, biopsies: Active module 13
11 MYC
DUSP1 EP300
MAPK14
RB1 JUN MAPK8
EGFR HSPB2 ACTC1 CDK1 STAT3
FCGR3B SYK CSF2RB
PIK3CG
OGDHL
Figure S10.14. Yao’s dataset, biopsies: Active module 14
OGDHL
STAT3
RB1 JUN
SMAD3 EP300 HDAC1
CDKN1A CREBBP MYC
RELA ESR1 SP1
BRCA1 TP53
Figure S10.15. Yao’s dataset, biopsies: Active module 15
12 ACTC1
CDH5
CDK1 BRCA1 NDC80
CDK2 RELA CDKN1A
KAT5
KPNA2
UBE2I BARD1
TRRAP TP53 EP300
KAT2A MYC
Figure S10.16. Yao’s dataset, biopsies: Active module 16
OGDHL
HIF1A STAT3 CREBBP
RB1 HDAC3 MAPK14
PIK3R1 EP300 SP1 SMAD3 MYC
CDKN1A KAT2B ESR1
Figure S10.17. Yao’s dataset, biopsies: Active module 17
13 ABCC8
ACTC1 ITGA1 SLC5A4
HSPB2 SOX17 DAAM2 RUVBL1
CLEC14A FCGR3B ADAM33 ALYREF DDX39A
CENPN KPNA2 DUSP1 APLNR
LSM7 GLRX3
Figure S10.18. Yao’s dataset, biopsies: Active module 18
Figure S11
Ten active modules obtained by applying MOGAMUN on Yao’s myoblasts dataset [1] (see Table S1 for the list of samples). The color of the nodes represents the fold-change, where green and red nodes correspond to under- and over-expressed genes, respectively. Nodes with bold black border correspond to genes significantly differentially expressed (F DR < 0.05 and absolute log2 fold-change > 1). Blue and white nodes correspond to genes with no associated transcriptomics data and no deregulation, respectively.
LRRTM4
NRXN2 SHANK3 GFRA1
DLGAP4
MDM2 UBC USP15
JUN ZG16B UBQLN1 SMAD3
SMAD2 OTUB1
EP300
Figure S11.1. Yao’s dataset, myoblasts: Active module 1
14 PDLIM7 GFRA1 CLIP3
UBC FAM149A PTN TRAF2
LRRTM4
UBQLN4 NRXN1 RAI2 BRCA2
ZG16B
APBA2 APP
Figure S11.2. Yao’s dataset, myoblasts: Active module 2
NOTCH2NL HOXA1
KRTAP10-5 LCE1B
KRTAP10-3
KRTAP10-9
KRTAP9-2 KRTAP4-2
SPRY2
KRTAP10-8
KRTAP10-7 CHRD
KCND3
NRXN1 GFRA1
LRRTM4
Figure S11.3. Yao’s dataset, myoblasts: Active module 3
15 MCM3
ORC5
CDC45 ORC6 MCM6
MCM2 MCM7 MCM5
CDC7 CDC6 ORC1
MCM4
DBF4 RAI2 MCM10
ORC2
RHOJ
GFRA1
Figure S11.4. Yao’s dataset, myoblasts: Active module 4
CBL
CTNNB1
RET ERBB2
LRRTM4 NRXN3 AFDN
GFRA1 EGFR PIK3R1
HRAS
DOK1 SRC SHC1
PTK2
Figure S11.5. Yao’s dataset, myoblasts: Active module 5
16 ARAP2
ARHGEF7 APP
RHOG
HOMER2
PIK3R1
SHANK3 LRRTM4 GFRA1 NRXN3
SHC1 EGFR CRK
GRB2 RET
Figure S11.6. Yao’s dataset, myoblasts: Active module 6
NRXN2 LRRTM4
NRXN3 NLGN1 NLGN2
NLGN3 NRXN1 AFDN RIT1 PTN SGTA ZG16B
EPHB2 HRAS
GFRA1
NRTN
Figure S11.7. Yao’s dataset, myoblasts: Active module 7
LRRTM4
NRXN3
NRXN1 MCM6 MCM5
KCND3 GFRA1 MCM2 CDC7 ORC6
ORC1
ORC2 MCM3 MCM7 DBF4
LTV1 ORC5 MCM4
Figure S11.8. Yao’s dataset, myoblasts: Active module 8
17 GFRA1
GAB1 SRC PTN PIANP NRXN2 LRRTM4
CBL SHC1 RET
CRK PIK3R1
FYN GRB2 ERBB2
Figure S11.9. Yao’s dataset, myoblasts: Active module 9
LRRTM4 CDK2 BRCA1 NRXN1 MCM5 CDC7
KCND3 ORC1 MCM7
GFRA1 ORC6
MCM3 CCNA2 MCM2 MCM6
ORC2 DBF4 CDC6
ORC5 MCM4 CDT1
Figure S11.10. Yao’s dataset, myoblasts: Active module 10
Figure S12
Twenty three active modules obtained by applying MOGAMUN on Yao’s myotubes dataset [1] (see Table S1 for the list of samples). The color of the nodes represents the fold-change, where green and red nodes correspond to under- and over-expressed genes, respectively. Nodes with bold black border correspond to genes significantly differentially expressed (F DR < 0.05 and absolute log2 fold-change > 1). Blue and white nodes correspond to genes with no associated transcriptomics data and no deregulation, respectively.
18 TRIM49B TRIM49
TRIM49D2
TRIM49C
PRAMEF12 TRIM49D1 TCEB3B
ZNF705B
MBD3L2 TRIM43 ARGFX
ZIM3 USP29
MBD3L3 PRAMEF11 TPRX1
PRAMEF2
HNRNPCL1
Figure S12.1. Yao’s dataset, myotubes: Active module 1
CCNA1 PRAMEF12 PSMD10 TRIM49D2
MDM2 CCNE1 UBE2D1 TRIM43 RB1 MBD3L2
TRIM49D1 CDKN1A PSMA3
PSMD4
TRAF2 CCNB1
Figure S12.2. Yao’s dataset, myotubes: Active module 2
19 UBE2C CDK2
CCNB1 CDC20
CCNA1 SRC AR
FZR1
UBE2D1
TRIM43
TRIM49B MBD3L2
TRIM49D1 PRAMEF12
TRIM49D2
Figure S12.3. Yao’s dataset, myotubes: Active module 3
CCNA1 FZR1
TP53
CDC6 CDC20 CCNB1
CDK7
CDC25A CCNA2 FOXM1 CDKN1A
CDK2 CDK1
ARID4A UBE2C
CCNE1
Figure S12.4. Yao’s dataset, myotubes: Active module 4
20 SNW1 CDKN1A CCNE1
CDC25A RB1 CCNA1 UBE2D1
CCNB1 EP300 HDAC1
TRIM43 TRIM27 CDK2
HIST1H2BA MBD3L2
Figure S12.5. Yao’s dataset, myotubes: Active module 5
KLHL38 NOTCH2NL MGAT5B CREB5
KRTAP10-8 KRTAP10-3 KRTAP10-7 HOXA1
KRTAP10-9 KRTAP4-2
KRTAP5-9 KRTAP9-2
KRTAP5-6 SPRY2 LCE3E
Figure S12.6. Yao’s dataset, myotubes: Active module 6
21 MDM2 CCNA1 CCNA2
CCNB1
RB1 TP53 CDKN1B
UBE2D1
CDK2 CDKN1A
EP300 SKP2 HDAC1
BRCA1
CCNE1
Figure S12.7. Yao’s dataset, myotubes: Active module 7
KRTAP5-9 KRTAP10-8
KRTAP10-7 KRTAP10-3 MEOX2
KLHL38 NOTCH2NL
KRTAP10-9 PTGER3
TRIM42
TRIM43
PRAMEF12 TRIM49B
MBD3L2 TRIM49D1
Figure S12.8. Yao’s dataset, myotubes: Active module 8
22 PRAMEF11
ZIM3
HNRNPCL1
TPRX1 PRAMEF2 HIST2H2AC
HIST1H2BA
MBD3L3 RELA TRIM43 EP300
UBE2D1 HIST2H2BE CTNNB1
CREBBP LEF1
Figure S12.9. Yao’s dataset, myotubes: Active module 9
MBD3 PRAMEF12 RBBP4
TRIM49B MTA2 TRIM49D2 MBD3L2 HDAC1 GATAD2B
TRIM49D1
TRIM43 RBBP7
HDAC2 GATAD2A TRIM49
CHD4 HIST1H2BA
Figure S12.10. Yao’s dataset, myotubes: Active module 10
23 KRTAP9-4 LCE4A
SPRY2 CREB5
PTGER3 KRTAP10-7
KRTAP5-9 MEOX2
KRTAP4-2
KRTAP10-8 KRTAP10-9
LCE1B KRTAP10-3
NOTCH2NL
KLHL38
Figure S12.11. Yao’s dataset, myotubes: Active module 11
PRAMEF12
TRIM49D2 MBD3L2
TRIM49B
TRIM49D1 TRIM43
UBE2D1
RB1 MCM3
CDC6 CDKN1A MCM2
CCNE1 UBE2C
CCNA1 CDKN1B CCNB1 MCM5
CDK2 SKP2
Figure S12.12. Yao’s dataset, myotubes: Active module 12
24 KRTAP10-8 PTGER3 KRTAP4-12
KRTAP10-3
KRTAP10-7 LCE1B
ADAMTSL4 KRTAP9-2
HOXA1 GLRX3 KRTAP10-9 KRTAP5-9 SPRY2
KRTAP4-2
KRTAP10-5
Figure S12.13. Yao’s dataset, myotubes: Active module 13
USP29 TRIM39
HNRNPCL1 TRIM43 RBCK1
CDKN1A UBE2D1 UBE2D4 UBE2D2 UBE2C
UBC
SKP2
CDK2 TRIM63
CCNA1 CDC20
CCNB1
Figure S12.14. Yao’s dataset, myotubes: Active module 14
25 CLSPN CDC6
CCNE1 CDC25A UBE2C
CCNB1 FZR1 CCNA1 UBE2D1
TRIM43
TRIM49C MBD3L2
TRIM49D1
PRAMEF12 TRIM49D2 TRIM49B
Figure S12.15. Yao’s dataset, myotubes: Active module 15
CDK1 CDC25A
CCNE1 CDKN1A E2F1 CENPA SKP2
CDK2 RPA1 CCNB1 CDC20 EP300
FZR1 UBE2D1 CCNA1 CDC6
UBE2C
Figure S12.16. Yao’s dataset, myotubes: Active module 16
26 KRTAP9-2 SPRY2 ADAMTSL4
LCE3E KRTAP9-4
KRTAP5-9
KRTAP4-12 HOXA1
KRTAP10-5 OTX1
ZNF679 PRAMEF2
HNRNPCL1
TPRX1 ARGFX
USP29
PRAMEF11
MBD3L3
Figure S12.17. Yao’s dataset, myotubes: Active module 17
TRIM43
CCNA1 UBE2D1
BRCA1 CCNE1 CDC27
CDK2
CDC20 CDK1
CDKN1B
CDC6 CDT1 CCNB1 E2F1
CDC25C
Figure S12.18. Yao’s dataset, myotubes: Active module 18
27 CCNK
POLR2A POLR2F POLR2C
POLR2D BRCA1 POLR2B
POLR2G
TCEB3B POLR2H
TRIM43
MBD3L3 PRAMEF12
PRAMEF2 HNRNPCL1
Figure S12.19. Yao’s dataset, myotubes: Active module 19
PRAMEF9
KRTAP10-9 KRTAP9-2
NOTCH2NL KRTAP9-4 KRTAP5-9
LCE3E LCE1B
KRTAP4-12
KRTAP5-6 OTX1 KRTAP4-2
KRTAP10-3
HOXA1 KRTAP10-1
KRTAP10-5
Figure S12.20. Yao’s dataset, myotubes: Active module 20
28 RB1 CDKN1A
CDC6 ORC1 CCNA2 CCNE1
SKP2
CDK2 MCM4 CCNA1
E2F1 FZR1 CDT1 BRCA1
ORC2
Figure S12.21. Yao’s dataset, myotubes: Active module 21
UBE2C
E2F1
FZR1
CDC20 CCNB1 CDK2
CCNA2 CDK1
CDC6 CCNE1 CCNA1 CDT1 CDKN1A
TP53
EP300 CDC25A
Figure S12.22. Yao’s dataset, myotubes: Active module 22
29 KRTAP9-4 KRTAP10-5
KRTAP4-2 KRTAP5-6
KRTAP9-2
LCE1B NOTCH2NL KRTAP5-9 SPRY2
KRTAP10-8 KRTAP10-9 KRTAP4-12
KRTAP10-3 PTGER3
KLHL38
Figure S12.23. Yao’s dataset, myotubes: Active module 23
Figure S13
Twenty three active modules obtained by applying MOGAMUN on Banerji’s 2017 dataset [2] (see Table S2 for the list of samples). The color of the nodes represents the fold-change, where green and red nodes correspond to under- and over-expressed genes, respectively. Nodes with bold black border correspond to genes significantly differentially expressed (F DR < 0.05 and absolute log2 fold-change > 1). Blue and white nodes correspond to genes with no associated transcriptomics data and no deregulation, respectively.
MAPK10 MAPK8
PTGDS EGF EP300 TP53 DSP
SEPP1 ERBB2 SHC1 GRB2 AGT
EGFR ERBB3 CDK1
TGFA
CD4 JAK2
PIK3R1 NEDD4
Figure S13.1. Banerji’s 2017 dataset: Active module 1
30 RELA KAT2B
SMAD2
NCOA1 PPARG TP53
HDAC1 JUN NR4A1 SMAD3
CREBBP EP300 ESR1 RXRA
PPARGC1A NCOA6
Figure S13.2. Banerji’s 2017 dataset: Active module 2
UBE2I NCOA1
SIRT1 AR CREBBP
NR3C1 ESR1 JUN PPARGC1A
FOS TP53
NCOA6 ATF2 RELA
SMAD3
Figure S13.3. Banerji’s 2017 dataset: Active module 3
31 PTGDS A2M NRG1
SOS1 NRAS SRC HSP90AA1 GAB1
FYN MAPK10 HRAS EGF
EGFR SHC1 PIK3R1
Figure S13.4. Banerji’s 2017 dataset: Active module 4
A2M SEPP1 BRCA1
CASQ2 FOS NR4A1 ESR1
SKP2
CDK2 TP53 SMAD3 ACTB
EP300 CREBBP KAT2B
Figure S13.5. Banerji’s 2017 dataset: Active module 5
CATSPER1 HOXA1 ADAMTSL4
PLSCR1 KRTAP5-9 KRTAP4-2 KRTAP10-1
KRTAP10-5 KRTAP10-3 KRTAP10-8 KRTAP5-6 KRTAP10-9
CHRD NOTCH2NL
ZNF417 KRTAP10-7
Figure S13.6. Banerji’s 2017 dataset: Active module 6
32 KRTAP10-1 KRTAP10-5 SPRY2 KRTAP5-6
OTX1 CHRD HOXA1 KRTAP10-3
KRTAP5-9 CATSPER1 CREB5 KRTAP9-2
KRTAP4-2 KRTAP10-8 KRTAP10-9
Figure S13.7. Banerji’s 2017 dataset: Active module 7
SHC1 PIK3R1
PTK2 GRB2
PTEN IRS1 EGF
CBL TP53 EGFR HSP90AA1
UBC LRRK2 TGFA PTGDS
Figure S13.8. Banerji’s 2017 dataset: Active module 8
APP ABCA8
SHC1 A2M PDK4
CASQ2 MAPK10 ACTA2 LRRK2 NEDD4
LUM EGFR PTGDS
CBLB SEPP1 NDN
Figure S13.9. Banerji’s 2017 dataset: Active module 9
33 NR4A1 PROX1
NFKB1 NR3C2
NCOA1 EP300
SMAD3 CDK2 MAPK10
TP53
BRCA1 SP1 SKP2
SMAD2 ESR1
Figure S13.10. Banerji’s 2017 dataset: Active module 10
ERBB3 CRKL CBL
SHC1 GRB2 EGFR MAPK10
A2M ESR1 PTGDS TIMP3
HSP90AA1 CASQ2 SEPP1 CACNA1A
TP53 CDK1
Figure S13.11. Banerji’s 2017 dataset: Active module 11
MCM10 NR3C2
PTGDS ACTA2 ABCA8 CDC6 FMOD
MAPK10 MCM7 CASQ2
LUM PRPH2
NDRG2 SEPP1 NR4A1 A2M
Figure S13.12. Banerji’s 2017 dataset: Active module 12
34 BRCA1 CREBBP MAPK10
MAPK14 FOS UBE2I MYC
SP1
ESR1 TP53 RELA JUN
SMAD3
EP300 MAPK1
Figure S13.13. Banerji’s 2017 dataset: Active module 13
TIMP3
GRB2 A2M CASQ2 EGF
BCR ERBB3 LUM
JAK2 MAPK10 LYN
ABCA8 LRRK2 PIK3R1 SHC1
Figure S13.14. Banerji’s 2017 dataset: Active module 14
A2M TIMP3
NR4A1 MCM2
CDK1 CASQ2
LRRK2 FOS PTGDS
LUM CDC6 MAPK10 NR3C2
SEPP1 PSMC2
Figure S13.15. Banerji’s 2017 dataset: Active module 15
35 SHC1 PLCG1
ERBB3 GRB2 CRKL EGFR
CBL EGF PIK3R1
ABL1 SOS1
SRC PIK3R2
GAB1
CRK
Figure S13.16. Banerji’s 2017 dataset: Active module 16
HDAC1 NR4A1 SMAD3
JUN
CREBBP TP53 PML RXRA
NR3C1 EP300 UBE2I KAT2B
MDM2 PPARG PPARGC1A
Figure S13.17. Banerji’s 2017 dataset: Active module 17
36 TP53 JUN MAPK8
CREBBP FOS EP300 KAT2B SIRT1 ESR1 PPARGC1A NR4A1
NCOA3 NCOA1 AR NCOA2
Figure S13.18. Banerji’s 2017 dataset: Active module 18
HIST3H2A AGT
KAT2B NR4A1 ZFP2
HIST4H4 ZNF43 TRIM28 SIRT1 HIST1H3J TP53 HIST3H2BB ZNF429
EP300
MAPK10 PBX1 HIST1H4L PPARGC1A ZNF681 FOXO1 ZNF726
Figure S13.19. Banerji’s 2017 dataset: Active module 19
PAMR1 CHRD SEPP1 TIMP3
CASQ2
RASSF9 POLE3
FMOD ABCA8 MAPK10
COL14A1 PTGDS A2M
NR3C2 LUM PYGM
Figure S13.20. Banerji’s 2017 dataset: Active module 20
37 SHC1 PLCG1 ERBB3
FYN SRC EGFR HCK
PIK3R1 GRB2 SYK GAB1
ERBB2 EGF CBL
VAV1
Figure S13.21. Banerji’s 2017 dataset: Active module 21
ESR1 PPARGC1A FOS
NR4A1 EGFR TP53 CREBBP MAPK10
NCOA1 EP300 JUN ACTB
NR3C2 KAT2B HIF1A
SMAD5
Figure S13.22. Banerji’s 2017 dataset: Active module 22
ATF2 SMAD3 RELA HDAC1
TP53 MAPK8
BRCA1 JUN
MAPK10 CREBBP EP300 SP1
UBE2I NR3C1
ESR1
Figure S13.23. Banerji’s 2017 dataset: Active module 23
38 Figure S14
Seventeen active modules obtained by applying MOGAMUN on Banerji’s 2019 dataset [3] (see Table S3 for the list of samples). The color of the nodes represents the fold-change, where green and red nodes correspond to under- and over-expressed genes, respectively. Nodes with bold black border correspond to genes significantly differentially expressed (F DR < 0.05 and absolute log2 fold-change > 1). Blue and white nodes correspond to genes with no associated transcriptomics data and no deregulation, respectively.
FZR1 MDM2 UBE2D1
HIST2H2BE TP53 UBC
HIST1H4L CREBBP
SMAD3 PBX1 YY1
EP300 MYC H2AFJ HIST1H3J
KAT2B HIST1H4A NCOA1
RXRA SMARCC1
Figure S14.1. Banerji’s 2019 dataset: Active module 1
RELA MAPK10 ATF2
EP300 SMAD3 NCOA3 ESR1 JUN
CREBBP MYC BRCA1 TP53
MAPK1 SP1 SMAD2
Figure S14.2. Banerji’s 2019 dataset: Active module 2
39 SNCA MAPK1
CSNK2A1
JUN
RAF1 YY1 SMAD3 CDK4
CCND2
MYC TP53 EP300
RB1
CDKN1B SKP2
Figure S14.3. Banerji’s 2019 dataset: Active module 3
ERBB4 NR3C2
TRAF6
MAPK10 CPT1C PPARGC1A SMAD3
FHL2
MAPK12 SMAD2 TP53 NGFR
MAPK14
NCOA1 BCL2
RXRA
Figure S14.4. Banerji’s 2019 dataset: Active module 4
40 KCND3 CYYR1
A2M CILP
MAPK10 DOC2B PTGDS
FMOD
GHR SEPP1 ABCA8 LUM
CIT
FMO3 PAMR1
Figure S14.5. Banerji’s 2019 dataset: Active module 5
CSNK2A1
CREBBP SMAD2 HIST1H4L MAPK8
SMARCE1 STAT3 MYC BCL2
MAPK14 KAT2B JUN
SMAD3 MAPK10 RB1
TP53
Figure S14.6. Banerji’s 2019 dataset: Active module 6
41 NCOA3 TP53 PPARGC1A
MAPK10 MAPK14 EGFR RXRA
SMAD3 MAPK11 YY1 SMAD2
H2AFJ ERBB4 SP1
NEDD4 ABCA8 SMARCC1
Figure S14.7. Banerji’s 2019 dataset: Active module 7
NEDD4 SNCA
SMAD3
CTNNB1 MAPK1
JUN FYN MAPK10
SMAD2
MAPK14 CDK5R1 DPYSL5
ESR1
TRAF6 TP53
Figure S14.8. Banerji’s 2019 dataset: Active module 8
42 SQSTM1 TRAF6 SPSB4
CDK2 TP53 MAPK14 NGFR
HIST1H4L UBB
RB1 UBC SMAD2
KAT2B H2AFV SMARCC1 CHD3 SNCA
HIST1H3J H2AFJ
Figure S14.9. Banerji’s 2019 dataset: Active module 9
MAPK10
SKIL ESR1
TP53 SMAD2 ATF2
RELA
SMAD3 MYC CREBBP
JUN CEBPB KAT2B
EP300 MAPK1
Figure S14.10. Banerji’s 2019 dataset: Active module 10
43 BRCA1
EP300 SMAD2
ESR1
MYC NCOA6 TP53 CREBBP
MAPK14 SMAD3 NCOA1 JUN NCOA3
RELA MAPK1 KAT2B
MAPK10 PPARGC1A
Figure S14.11. Banerji’s 2019 dataset: Active module 11
NCOA1 PPARGC1A PPARG
MAPK10 KAT2B CREBBP
ESR1 EP300
ATF2 JUN MYC TP53
SMAD3 SP1
SMAD2
Figure S14.12. Banerji’s 2019 dataset: Active module 12
44 CIT KPNA2
FMO3 A2M
LUM ABCA8
CDK1
GHR LIMS2 CILP FMOD PAMR1
ATP1A2
KCND3
NR3C2 RASSF9
Figure S14.13. Banerji’s 2019 dataset: Active module 13
SMAD2
NCOA1 TP53
JUN NCOA6
CREBBP MAPK1 KAT2B EP300
MED1 RXRA PPARGC1A MAPK14
AR NCOA3
Figure S14.14. Banerji’s 2019 dataset: Active module 14
45 NFKB1 TP53 SMAD2
PPARGC1A JUN
YY1
PPARG CREBBP MYC SP1
EP300 ESR1
NCOA1
NCOA6 SMAD3
Figure S14.15. Banerji’s 2019 dataset: Active module 15
SMARCC1 NR3C2
AKT1
RB1 MYC NCOA1
MDM2
RXRA TP53
PPARG
BCL2 GSK3B
CREBBP PPARGC1A
MAPK10 MAPK1
Figure S14.16. Banerji’s 2019 dataset: Active module 16
46 FMOD MAPK10
NR3C2
GHR FMO3
PLN CIT
PAMR1 CILP
LUM ABCA8 HSPB6 KCND3
DOC2B RASSF9
Figure S14.17. Banerji’s 2019 dataset: Active module 17
Sample ID Type Origin F1 Patient Biopsy F2 Patient Biopsy F3 Patient Biopsy F4 Patient Biopsy F5 Patient Biopsy F6 Patient Biopsy F7 Patient Biopsy F8 Patient Biopsy F9 Patient Biopsy C1 Control Biopsy C2 Control Biopsy C3 Control Biopsy C4 Control Biopsy C5 Control Biopsy C6 Control Biopsy C7 Control Biopsy C8 Control Biopsy C9 Control Biopsy F4 Patient Myoblast F6 Patient Myoblast C21 Control Myoblast C22 Control Myoblast F4 Patient Myotube F6 Patient Myotube C20 Control Myotube C21 Control Myotube C22 Control Myotube
Table S1: Samples from Yao’s datasets [1]. Downloaded from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56787
47 ID Type Batch 54 12 r1 Patient 1 54 12 r2 Patient 1 54 12 r3 Patient 1 54 6 r1 Control 1 54 6 r2 Control 1 54 6 r3 Control 1 54 2 r1 Patient 2 54 2 r2 Patient 2 54 2 r3 Patient 2 54 A10 r1 Control 2 54 A10 r2 Control 2 54 A10 r3 Control 2 54 A5 r1 Patient 2 54 A5 r2 Patient 2 54 A5 r3 Patient 2 12ABic r3 Patient 3 12ABic r1 Patient 3 12ABic r2 Patient 3 12UBic r3 Control 3 12UBic r2 Control 3 12UBic r1 Control 3 16ABic r1 Patient 3 16ABic r2 Patient 3 16ABic r3 Patient 3 16UBic r3 Control 3 16UBic r2 Control 3 16UBic r1 Control 3
Table S2: Samples from Banerji’s 2017 dataset [2]. Downloaded from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE102812
48 ID Type Batch 54 12 T8 r1 Patient 1 54 12 T8 r2 Patient 1 54 12 T8 r3 Patient 1 54 6 T8 r1 Control 1 54 6 T8 r2 Control 1 54 6 T8 r3 Control 1 54 2 T8 r1 Patient 2 54 2 T8 r2 Patient 2 54 2 T8 r3 Patient 2 54 A10 T8 r1 Control 2 54 A10 T8 r2 Control 2 54 A10 T8 r3 Control 2 54 A5 T8 r1 Patient 2 54 A5 T8 r2 Patient 2 54 A5 T8 r3 Patient 2 12A T8 r1 Patient 3 12A T8 r2 Patient 3 12A T8 r3 Patient 3 12U T8 r1 Control 3 12U T8 r2 Control 3 12U T8 r3 Control 3 16A T8 r1 Patient 3 16A T8 r2 Patient 3 16A T8 r3 Patient 3 16U T8 r1 Control 3 16U T8 r2 Control 3 16U T8 r3 Control 3
Table S3: Samples from Banerji’s 2019 dataset [3]. Downloaded from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE123468
References
[1] Z. Yao, L. Snider, J. Balog, R. J. Lemmers, S. M. Van Der Maarel, R. Tawil, and S. J. Tapscott. Dux4- induced gene expression is the major molecular signature in fshd skeletal muscle. Human molecular genetics 23, 2014.
[2] C. R. Banerji, M. Panamarova, H. Hebaishi, R. B. White, F. Relaix, S. Severini, and P. S. Zammit. Pax7 target genes are globally repressed in facioscapulohumeral muscular dystrophy skeletal muscle. Nature communications 8, 2017. [3] C. R. Banerji, M. Panamarova, J. Pruller, N. Figeac, H. Hebaishi, E. Fidanis, ..., and P. S. Zammit. Dynamic transcriptomic analysis reveals suppression of pgc1 α/err α drives perturbed myogenesis in facioscapulohumeral muscular dystrophy. Human molecular genetics 28, 2019.
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