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Commit 1f735baa authored by Matteo Barcella's avatar Matteo Barcella
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scRNAseq realted scripts

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scRNAseq/Figure_5H_data.gmt 0 → 100644
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KEGG_NOTCH_SIGNALING_PATHWAY http://www.gsea-msigdb.org/gsea/msigdb/cards/KEGG_NOTCH_SIGNALING_PATHWAY HES5 DTX3 NOTCH4 DTX3L NOTCH3 NOTCH2 EP300 HES1 NOTCH1 NUMB PSEN2 PSEN1 PTCRA SNW1 APH1A KAT2A ADAM17 RFNG RBPJ DTX1 CREBBP DTX2 MAML1 CTBP2 NCOR2 CTBP1 DVL3 JAG2 DVL2 NUMBL MAML2 KAT2B DLL4 PSENEN DLL3 DVL1 CIR1 DLL1 LFNG JAG1 MAML3 HDAC1 HDAC2 NCSTN DTX4 MFNG RBPJL
KEGG_ECM_RECEPTOR_INTERACTION http://www.gsea-msigdb.org/gsea/msigdb/cards/KEGG_ECM_RECEPTOR_INTERACTION GP1BA COL6A2 COL6A3 GP1BB COL5A2 COL6A1 LAMA1 VWF HSPG2 TNN FN1 ITGA9 GP9 COMP IBSP CD36 CHAD GP5 VTN THBS4 ITGA4 ITGA3 ITGA2B ITGA7 ITGA5 COL5A1 COL4A6 ITGA11 SV2C COL2A1 COL3A1 COL4A1 AGRN COL4A2 COL4A4 ITGB3 ITGB4 RELN ITGB5 ITGB6 ITGB7 LAMC2 ITGAV ITGB1 LAMB2 SPP1 LAMB3 LAMC1 COL1A1 LAMA4 LAMA5 LAMB1 COL1A2 ITGA10 GP6 ITGA8 LAMB4 TNR CD47 SV2A CD44 DAG1 TNXB LAMA3 LAMA2 SDC3 ITGB8 ITGA6 ITGA2 ITGA1 SV2B TNC COL11A1 LAMC3 COL11A2 HMMR SDC2 SDC4 COL5A3 THBS3 COL6A6 THBS2 SDC1 THBS1
KEGG_CELL_ADHESION_MOLECULES_CAMS http://www.gsea-msigdb.org/gsea/msigdb/cards/KEGG_CELL_ADHESION_MOLECULES_CAMS CDH5 JAM3 CDH3 NLGN3 CDH4 CD80 NLGN1 CD86 CD28 CD274 PDCD1LG2 ITGA9 ITGAL NRCAM ITGAM CD34 CD276 ICOSLG CADM3 ITGA4 ICOS SIGLEC1 CADM1 HLA-G CLDN20 PECAM1 CD22 ITGB7 SELL VCAM1 ITGAV SELP SPN ITGB1 SELPLG ITGB2 CDH2 JAM2 CTLA4 HLA-DRB4 CLDN18 CD4 HLA-DRB5 CNTN1 NLGN2 HLA-DRB3 NRXN3 ALCAM SELE CD8A CD8B CD6 CLDN17 L1CAM ITGB8 MAG VCAN HLA-F NFASC HLA-E NRXN1 HLA-DPA1 HLA-DPB1 HLA-DQA1 HLA-DQA2 HLA-DQB1 NRXN2 CD2 CLDN16 CLDN23 MADCAM1 SDC2 SDC4 SDC1 OCLN PVR HLA-DRB1 NECTIN2 CDH1 HLA-DRA NECTIN1 HLA-DOA HLA-DOB CLDN10 ICAM2 ICAM3 CLDN8 CLDN2 CLDN6 CLDN5 CLDN1 ICAM1 NEO1 HLA-C HLA-B ESAM HLA-DMB HLA-DMA HLA-A F11R PDCD1 CLDN19 PTPRF CLDN15 CD226 CD99 CLDN22 CNTNAP2 MPZ MPZL1 PTPRC NECTIN3 ITGA8 NCAM2 NCAM1 CD58 NEGR1 CLDN11 SDC3 CLDN7 CLDN4 PTPRM CLDN3 ITGA6 CNTN2 CD40LG CNTNAP1 CD40 GLG1 CDH15 CLDN14 NLGN4X CLDN9
BIOCARTA_P53_PATHWAY http://www.gsea-msigdb.org/gsea/msigdb/cards/BIOCARTA_P53_PATHWAY CDK2 GADD45A CDK4 PCNA BCL2 CCNE1 ATM CDKN1A RB1 TP53 MDM2 CCND1 APAF1 BAX TIMP3 E2F1
WNT_SIGNALING http://www.gsea-msigdb.org/gsea/msigdb/cards/WNT_SIGNALING TLE5 APC AXIN1 BCL9 BTRC FZD5 CCND1 CCND2 CCND3 CSNK1A1 CSNK1D CSNK1G1 CSNK2A1 CTBP1 CTBP2 CTNNB1 CTNNBIP1 CXXC4 DAAM1 DIXDC1 DKK1 DVL1 DVL2 EP300 FBXW11 FBXW2 FGF4 FOSL1 FOXN1 FRAT1 FRZB FSHB FZD1 FZD2 FZD3 FZD4 FZD6 FZD7 FZD8 GSK3A GSK3B JUN KREMEN1 LEF1 LRP5 LRP6 MYC NKD1 NLK PITX2 PORCN PPP2CA PPP2R1A PYGO1 RHOU SENP2 SFRP1 SFRP4 FBXW4 SLC9A3R1 SOX17 TBXT TCF7 TCF7L1 TLE1 TLE2 WIF1 CCN4 WNT1 WNT10A WNT11 WNT16 WNT2 WNT2B WNT3 WNT3A WNT4 WNT5A WNT5B WNT6 WNT7A WNT7B WNT8A WNT9A B2M HPRT1 RPL13A GAPDH ACTB
PID_NOTCH_PATHWAY http://www.gsea-msigdb.org/gsea/msigdb/cards/PID_NOTCH_PATHWAY NCOR1 NOTCH1 DTX1 FURIN SSPO DLL1 DNM1 PTCRA RAB11A NEURL1 CTBP1 FBXW7 NUMB CBL MFAP2 MYCBP DLL4 MAML1 SPEN MYC SKP2 CNTN6 NOTCH2 PSEN1 MAML2 YY1 GATA3 ENO1 ITCH IL4 APH1B LNX1 NCOR2 RBPJ CDKN1A JAG2 ADAM12 DLK1 EPS15 DNER SKP1 NOTCH3 RBBP8 NOTCH4 JAG1 APH1A DLL3 PSENEN MFAP5 NCSTN CCND1 EP300 HDAC1 MARK2 MIB1 ADAM10 CUL1 CNTN1 KDM1A
PID_WNT_SIGNALING_PATHWAY http://www.gsea-msigdb.org/gsea/msigdb/cards/PID_WNT_SIGNALING_PATHWAY DKK1 WNT3A FZD9 WNT2 WNT7A IGFBP4 WNT1 FZD8 FZD1 KREMEN2 LRP6 KREMEN1 FZD2 LRP5 FZD6 RSPO1 WNT7B FZD4 CTHRC1 WNT5A RYK FZD5 ATP6AP2 WNT3 ROR2 WIF1 FZD10 FZD7
PID_WNT_CANONICAL_PATHWAY http://www.gsea-msigdb.org/gsea/msigdb/cards/PID_WNT_CANONICAL_PATHWAY LRP6 GSK3A AXIN1 CUL3 WNT3A DVL3 GSK3B FZD5 PPP2R5A CSNK1G1 DVL1 PI4K2A CTNNB1 DVL2 APC NKD2 PIP5K1B RANBP3 KLHL12 CAV1
REACTOME_SIGNALING_BY_WNT http://www.gsea-msigdb.org/gsea/msigdb/cards/REACTOME_SIGNALING_BY_WNT WNT16 HECW1 DVL2 CREBBP AP2B1 PSMB1 PSMC4 CCDC88C RUNX3 CUL3 PSMA4 AP2S1 CUL1 WNT8A TLE2 PPP2R5A RHOA PSME4 PPP2R5B VPS35 GNB5 LRP6 CSNK2A2 CAMK2A WNT8B PPP2R5C GNB1 TNRC6C TCF7 GSK3B XPO1 WNT11 NLK PSMC5 GNAO1 TNRC6A PSME1 AGO1 PSMD5 ITPR3 PSMD8 CBY1 TNRC6B RBX1 EP300 PSMC6 PSMA3 DAAM1 PSMC1 PSMB5 CHD8 PSMA6 PSME2 PSMA7 CSNK2A1 RSPO4 PSMD10 XIAP PORCN PARD6A PSMD7 AXIN1 FZD3 SFRP1 DKK4 PPP2CB TLE5 AKT2 PPP2R1A H2AFV CAV1 WNT2 PSMA2 TLE4 PIP5K1B DVL1 PPP3CB TNKS2 DKK1 PSMD3 RNF43 WNT3 PFN1 PSMD11 SMURF2 SOX6 PSMD9 WNT5B VPS29 GNB3 SNX3 PPP2R5D SKP1 PPP2CA CSNK1A1 WNT5A GNB4 PSMD14 USP34 WNT6 BCL9 HDAC1 WLS KLHL12 RBBP5 TMED5 RNF146 CLTA VPS26A ITPR2 AGO2 HIST1H2BJ SOX4 WNT1 SOX9 PSMF1 PSMB2 AGO3 PRKCG GNG13 SMARCA4 GNG11 SEM1 GNGT1 RAC2 PSMA1 ASH2L NFATC1 PSME3 KREMEN2 H3F3B PDE6A LGR6 PDE6B CSNK1G2 MEN1 GNAT2 WNT2B CDC73 SOX3 AGO4 APC MAP3K7 WNT10A RAC1 PSMB7 MYC PPP2R1B PLCB2 USP8 PRKG2 LEF1 PPP3CA PRICKLE1 LGR5 TLE3 CLTC ARRB2 AKT1 PSMB6 PSMA5 WNT9A SOX13 RPS27A SFRP2 HIST1H2BA RSPO3 RSPO2 TCF7L2 PLCB3 UBC ITPR1 TCF7L1 PPP2R5E PRKCA WNT3A PSMA8 WNT7A DKK2 MOV10 FZD7 WIF1 FZD1 HIST1H2BD HIST1H4H WNT9B PSMD4 PSMB4 CTBP1 PSMC2 DVL3 AP2M1 GNG3 LRP5 WNT4 VANGL2 H3F3A FZD5 PYGO2 PSMD6 RYK H2AFZ TERT SOX17 YWHAZ FZD6 DACT1 FRAT1 PSMC3 BTRC LEO1 PRKCB GNGT2 AC093503.1 KMT2D SOST CTNNB1 HIST3H3 GNG4 AXIN2 CXXC4 ROR2 RSPO1 RAC3 WNT10B UBB PYGO1 SOX7 GNB2 GNG12 KAT5 TNKS PSMD1 GNG5 FZD4 CTBP2 PSMD2 CLTB RUVBL1 GNG7 FZD8 CTNNBIP1 FZD2 HIST1H2AC HIST1H2BC SCRIB FRAT2 SOX2 PLCB1 AP2A2 ZNRF3 HIST2H3D KREMEN1 HIST2H2AC AMER1 HIST2H2BE SRY HIST1H2BL ROR1 AC139530.1 PRKG1 PSMD13 BCL9L GNG2 WNT7B H2AFX TRRAP TCF4 TLE1 HIST1H2AD HIST3H2BB AP2A1 HIST1H4C HIST1H3J PSMD12 HIST1H4J HIST1H3D HIST4H4 HIST1H2BK CALM1 SMURF1 HIST2H3C HIST2H2AA3 HIST2H3A PSMB8 CSNK2B LGR4 PSMB10 CSNK1E PPP3R1 UBA52 PSMB11 HIST1H2BN H2BFS PSMB9 GNG10 H2AFJ HIST2H4B HIST2H4A HIST2H2AA4 HIST1H4K HIST1H2BM HIST1H2BG HIST1H3G H2AFB1 HIST1H2BE HIST1H4F HIST1H2BO HIST1H3E HIST1H4L HIST1H3I HIST1H2BH HIST1H3A PSMB3 HIST1H4I HIST1H2AJ HIST1H2BB HIST1H4E HIST1H2AE HIST1H4D HIST1H2BF HIST1H3F HIST1H2AB HIST1H2BI HIST1H4A HIST1H4B HIST1H3H
REACTOME_REGULATION_OF_MITOTIC_CELL_CYCLE http://www.gsea-msigdb.org/gsea/msigdb/cards/REACTOME_REGULATION_OF_MITOTIC_CELL_CYCLE CDC27 PSMB1 PSMC4 PSMA4 ANAPC4 CUL1 PSME4 UBE2D1 CDC14A PSMC5 AURKA ANAPC5 PSME1 CDC23 PSMD5 PSMD8 PSMC6 PSMA3 PSMC1 PSMB5 PSMA6 PSME2 PSMA7 PSMD10 PSMD7 FZR1 PSMA2 PSMD3 PSMD11 ANAPC15 PSMD9 FBXO5 SKP1 PSMD14 CDC20 NEK2 CDK2 PSMF1 PSMB2 SEM1 PSMA1 CDC16 PSME3 CCNA1 CCNB1 PSMB7 ANAPC11 PSMB6 PSMA5 RPS27A CCNA2 SKP2 UBC ANAPC1 BUB3 PSMA8 BUB1B PSMD4 PSMB4 PSMC2 PSMD6 MAD2L1 ANAPC10 PTTG1 PSMC3 BTRC ANAPC16 PLK1 UBE2E1 CDK1 UBB PSMD1 UBE2C PSMD2 ANAPC2 CDC26 AURKB PSMD13 ANAPC7 PSMD12 PSMB8 PSMB10 UBA52 PSMB11 PSMB9 PSMB3
REACTOME_INTERFERON_SIGNALING http://www.gsea-msigdb.org/gsea/msigdb/cards/REACTOME_INTERFERON_SIGNALING CD44 IFNGR1 NUP160 PIAS1 TPR EIF2AK2 CAMK2B NDC1 SP100 IFI35 IP6K2 NUP133 NEDD4 CAMK2A EIF4G3 NUP37 SEH1L AC004551.1 ICAM1 NUP50 AAAS NUP188 JAK2 RAE1 SAMHD1 MID1 KPNA3 MAPK3 NUP93 TRIM35 TYK2 TRIM14 DDX58 KPNB1 NUP88 TRIM2 TRIM3 EIF4G2 NUP98 OAS3 OAS2 IFNG NUP107 PTPN6 TRIM38 NUP155 KPNA1 EIF4G1 STAT1 SUMO1 TRIM62 GBP3 GBP1 IRF6 IFIT3 IFIT2 IFNA6 IFNA8 NUP43 EGR1 TRIM25 TRIM6 PLCG1 NUP153 MT2A IRF1 NUP85 IRF3 IFI6 NUP214 PIN1 IRF5 BST2 TRIM21 NUP210 TRIM5 TRIM22 XAF1 TRIM45 RSAD2 OASL RNASEL EIF4E2 FLNB NUPL2 IFNA21 IRF4 TRIM29 PPM1B HERC5 NUP54 NUP58 PML IRF8 EIF4A3 IFITM3 IFNAR1 RPS27A CAMK2D IFNA5 IFNA16 CAMK2G NCAM1 TRIM48 FCGR1A UBC EIF4E RANBP2 GBP5 NUP205 UBE2L6 EIF4A2 SEC13 MX1 IFNAR2 IFNGR2 ADAR USP41 EIF4A1 JAK1 GBP2 GBP4 VCAM1 TRIM17 NUP35 EIF4E3 TRIM46 PRKCD ABCE1 IFI27 ARIH1 B2M TRIM68 IRF2 PTAFR UBE2E1 UBB STAT2 TRIM8 IFNB1 ISG20 PDE12 PTPN2 UBE2N PTPN11 HLA-DQB1 CIITA UBA7 KPNA2 GBP6 MX2 SOCS3 USP18 IFITM2 SOCS1 KPNA7 IRF7 IFIT1 IFITM1 KPNA4 IFNA10 ISG15 IFNA2 HLA-DRB3 HLA-DRB1 POM121 PTPN1 HLA-DQA1 KPNA5 FLNA IFNA1 FCGR1B HLA-DRB5 PSMB8 HLA-DRA HLA-C HLA-E TRIM10 TRIM31 HLA-G HLA-F HLA-H HLA-B HLA-A NUP62 GBP7 IRF9 IFNA7 IFI30 UBA52 HLA-DPB1 HLA-DRB4 IFNA14 HLA-DPA1 HLA-DQB2 IFNA13 TRIM26 IFNA17 IFNA4 HLA-DQA2 TRIM34 POM121C
KIM_RESPONSE_TO_TSA_AND_DECITABINE_UP http://www.gsea-msigdb.org/gsea/msigdb/cards/KIM_RESPONSE_TO_TSA_AND_DECITABINE_UP LAMB3 SFN ICAM2 PLIN2 HIST1H2BC S100P TNXA SSX1 HIST1H2BD CLU HIST1H3D CDA HSD17B1 FAM242E FAM169A MAGEA9 TSPYL5 MUC13 S100A2 CLIC3 CST6 TGM2 COLEC11 HSPA2 SSX4 HNF4G TES MAGEB1 ATF3 SLC25A31 IZUMO4 NEFL PDE2A MAP7 SAP25 BIK FAM50B MYO5C LXN NR1I2 RPP25 TFDP3 HSD17B6 JPT1 SSX3 CDKN1A RHOF KRT19 KRT86 PPL FKBP1B CCN5 TSGA10 HCLS1 IL24 CABYR TM7SF2 S100A3 TPD52L1 SLC6A8 FBXO2 HIST2H2AA3 STAG3 CXADR SSX2 RNASET2 SAT1 CDO1 AOPEP SERPINI1 FAAH SERPINF1 ISYNA1 FADS3 TAC1 DNALI1 KCNV2 PAGE1 ISG20 MAGEB2 GCHFR CYP3A5 CLDN7 TACSTD2 TRIM58 SMPD3 NEFM MUC1 PLAAT3 VAMP8 DNAJB9 KRT23 HTATIP2 NDUFA2 IRF7 IL1RAP COL3A1 TTC39A PECAM1 CSPG5 IFI30 H2AFB3 FUCA1 CYP24A1 XAGE1B IL1R2 SST LAMC2 LGMN IL18 RND2 KRT17 MAGEA4 CD70 HYAL1 ADIRF NXT2 GDF15 DBNDD2 AQP3 TSPAN1 CDCP1 ARL14 FGD6 SLC27A2 MAP1LC3B KRT7 DAZL TOB1 NINJ2 IFI27 CNN1 F2RL1 PARD6A IGKC NMB
RHEIN_ALL_GLUCOCORTICOID_THERAPY_UP http://www.gsea-msigdb.org/gsea/msigdb/cards/RHEIN_ALL_GLUCOCORTICOID_THERAPY_UP LTF DDX39B GABPB1-IT1 MIR22HG KLF9 LY96 GPR18 CD55 NR4A2 ZNF165 MAX TCF25 LY86 ZNF571 TXNIP CH25H DDX17 P2RX5 AC022966.1 ABCA1 ALAS2 IL1R2 CYTIP DEFA1 IQSEC1 PELI1 DUSP5 MS4A1 ITPR3 ASAH1 DEFA4 P2RY10 GABARAPL1 SWAP70 CREM CEBPB RGS2 SEL1L3 CA1 IL13RA1 VNN2 ZNF331 ANKRD11 CHPT1 GPR65 DUSP4 GLUL AREG S100A8 CRYBG1 IDS GLIPR1 EZR GTPBP1 AHNAK BIRC3 DIDO1 RGS13 MNDA CSTA NOTCH2 C15orf39 FOSL2 LYZ PTPRC KLHL2 ADAM28 G0S2 MAP3K8 MFN1 GRK5 SLC7A5 GPR183 CXCL8 ITGAM IFNGR1
KAN_RESPONSE_TO_ARSENIC_TRIOXIDE http://www.gsea-msigdb.org/gsea/msigdb/cards/KAN_RESPONSE_TO_ARSENIC_TRIOXIDE MT1X EIF4G1 VCAN DKK1 IGF2BP3 SOX2 AKR1C1 ARHGAP6 HSPA6 DPYSL3 SPANXA1 MGP IL11 IL7R PIMREG ENO2 SCD MT1E SPC25 DDIT3 PTN FABP7 KIF20A JAG1 RGS4 PSAT1 MAP1LC3B SCG2 TOP2A CXCL8 IGFBP5 GABARAPL1 GSTM3 LMNB1 NDRG1 CLEC2B NUPR1 SERPINE1 SLC7A11 MYBL1 MT-ND5 ARL4C GRN BOP1 TRIB3 MT1H STC2 PTHLH AK4 FAM162A MT1F ADM MT2A GDF15 HEY1 TRIM16 BNIP3L CBS DLGAP5 ATF3 SLC30A1 JUN PIR SLC2A3 DIRAS3 MCM7 C6orf15 TSC22D3 VAT1 AKR1C3 THBS1 GPM6B NAP1L1 MYCNOS SEL1L3 CRYAB AKR1B10 CAV2 CCPG1 DCBLD2 GFPT2 DDIT4 PDGFRA GLRX FBXO5 CTH EFNB2 HSPA1A WIPI1 ID2 SLC12A1 ASNS ANKRD1 SACS PHGDH LDLR TMEM158 HSPA1B CXCL14 STC1 LGALS8 FABP4 PODXL RIT1 INSIG1 PLK2 KLF12 ARHGAP29 PPP1R15A GPR65 ZNF512B RRM2 SPANXB1 PTPRZ1 FEZF2 SOCS2 HMOX1 KCTD12 NES NOX1 NDP GPNMB MT1G
FRIDMAN_SENESCENCE_UP http://www.gsea-msigdb.org/gsea/msigdb/cards/FRIDMAN_SENESCENCE_UP HSPA2 CDKN2A SERPINE1 CDKN2B CYP1B1 CCND1 RRAS RHOB FILIP1L NRG1 RAB31 CCN2 VIM IGFBP4 MMP1 AC034102.1 S100A11 GUK1 MAP2K3 MAP1LC3B CXCL14 IRF5 CITED2 HTATIP2 CDKN2D IGFBP3 NME2 ISG15 NDN IGFBP2 RBL2 TSPYL5 CLTB IRF7 IGFBP7 F3 IGFBP6 TNFAIP3 TP53 IGFBP5 HBS1L ALDH1A3 RAC1 STAT1 IFNG IGSF3 THBS1 IFI16 ING1 CDKN1C OPTN RGL2 CREG1 SOD1 CRYAB COL1A2 HPS5 RABGGTA SMURF2 PEA15 AOPEP RAB13 MDM2 TFAP2A TGFB1I1 SPARC TNFAIP2 SERPINB2 TES CD44 IGFBP1 FN1 CDKN1A EIF2S2 ESM1 SMPD1 GSN
NGUYEN_NOTCH1_TARGETS_DN http://www.gsea-msigdb.org/gsea/msigdb/cards/NGUYEN_NOTCH1_TARGETS_DN HERPUD1 CLIC1 AANAT TGM3 TNFSF4 GNB1 CCN1 BASP1 F3 EXT1 BRD2 PSMB9 TARS USP22 EEF1A1 COL11A2 KLF10 MGAT2 ELL2 SERPINH1 PTPN13 IL1R1 TRIP12 SLC12A4 ETS2 MTHFD2 REV3L DAXX TIPARP HUWE1 STC2 RPE MYO1E COL7A1 BET1 PCDH7 PSME4 GTF2I FNDC3A RGS2 LAMA5 ADSL KLF4 TP63 SRPK2 ITGA2 PTGS2 ERCC5 IGF1R APEX1 TP53 TFAP2A ASS1 ZNF282 SLC20A1 PPIF CYP2E1 NET1 MARCKS KLF5 GADD45B LIMK2 PDE6A RING1 MYC HTRA2 EPHA4 TRIO XBP1 PBX3 RBMS1P1 SLC12A2 HPCAL1 CCNI DYNC1H1 GARS UBR4 SMYD5 CADM1 PNN SERP1 PPP2R1B NOP2
CHIBA_RESPONSE_TO_TSA_UP http://www.gsea-msigdb.org/gsea/msigdb/cards/CHIBA_RESPONSE_TO_TSA_UP TIMP2 NGFR FOS KRT19 CDKN2C KRT14 EFNB3 ARHGDIB LUM CD36 BSG FOSB APRT FBN2 IGFBP2 BMP6 BAD ANK1 COL1A2 ITGA3 COL11A2 PLA2G6 SOD3 VTN CGB3 ITGA7 TRIP6 KRT7 MMP19 VEGFC BMP4 SIVA1 CD8B PDGFRB QSOX1 MMP13 TGFB1 GLRX TIMP3 CCN2 JUNB FASTK CASP9 CAV2 HLA-A RHOG JUP CD9 HBEGF TP53I11 MMP11
INGA_TP53_TARGETS http://www.gsea-msigdb.org/gsea/msigdb/cards/INGA_TP53_TARGETS MDM2 SFN BAX TNFRSF10B FOS TP53AIP1 PCNA THBS1 CCNG1 FASLG PMAIP1 BBC3 IGFBP3 SESN1 IER3 CDKN1A GADD45A
TSAI_RESPONSE_TO_RADIATION_THERAPY http://www.gsea-msigdb.org/gsea/msigdb/cards/TSAI_RESPONSE_TO_RADIATION_THERAPY IL6 THBS1 CD69 COL6A2 TGFBR3 CCN2 CFB IGFBP5 TGFBR2 HBB CCN1 BST2 COL6A3 COL6A1 TGFB2 VEGFC IFITM1 SERPINE1 OAS3 COL4A1 LGALS3BP IFI6 TNFRSF11B IFIT2 LIPC SMAD3 PLAT TSC22D1 EGR1 VEGFA ISG15 A2M
VILIMAS_NOTCH1_TARGETS_UP http://www.gsea-msigdb.org/gsea/msigdb/cards/VILIMAS_NOTCH1_TARGETS_UP P2RY10 CD69 CD3D RAG2 RRAS2 ID2 CD28 GATA3 TRAT1 IL2RA EGR2 GZMB TNFRSF18 LCK BCL2A1 CCR7 CD83 CCL5 TRAF1 IL12B CX3CL1 RELB EGR1 CARD11 NFKB2 ICAM1 IRF7 JUNB IL10RA CD86 NFKBIA BIRC2 BIRC3 CD80 ZAP70 HEY1 DDR1 CD7 CD3G TOX GZMA DTX1 LAT RAG1 PTCRA IL7R CD2 NFATC1 NOTCH3 CD74 NRARP THY1 CTLA4
CHIARETTI_T_ALL_REFRACTORY_TO_THERAPY http://www.gsea-msigdb.org/gsea/msigdb/cards/CHIARETTI_T_ALL_REFRACTORY_TO_THERAPY HOXA9 PCDH9 TFDP2 ADA ICOS PSPH GFI1 MME MXI1 DNTT TMPO TCF7 HIST1H2AC PSPHP1 H1F0 NOTCH3 EPHB6 DHCR24 FYB1 MX1 C2orf27A NID2 CXCL8 BCL6 SELL CR2 PRDX2 ARL4C FHL1 FGFR1
GO_NEGATIVE_REGULATION_OF_NOTCH_SIGNALING_PATHWAY http://www.gsea-msigdb.org/gsea/msigdb/cards/GO_NEGATIVE_REGULATION_OF_NOTCH_SIGNALING_PATHWAY MIR1224 WWP2 DLX1 DLX2 EGF EGFR AKT1 BEND6 HEY1 HEY2 GATA2 PEAR1 LFNG ARRB1 MAGEA1 MMP14 NRARP NFKBIA NOTCH3 EGFL7 ZBTB7A YTHDF2 DLL4 FBXW7 SLC35C1 HIF1AN METTL3 TCIM BCL6 FAM129B BMP7 DLK2 CHAC1 GDPD5 RITA1 DLK1 CBFA2T2 NEURL1 ARRDC1
GO_NOTCH_SIGNALING_PATHWAY http://www.gsea-msigdb.org/gsea/msigdb/cards/GO_NOTCH_SIGNALING_PATHWAY MIR1224 CDH6 TSPAN5 AC242843.1 AC239811.1 ADAM10 CDK6 CDKN1B CEBPA POSTN DLL3 WWP2 PTP4A3 PDCD10 DTX2 CNTN1 RIPPLY2 CREBBP GATA5 MIB2 MESP2 DTX3L PRAG1 GSX2 DLX1 DLX2 JAG1 DTX1 AGXT S1PR3 EGF EGFR DTX3 ELF3 EP300 AKT1 ERH ETV2 FCER2 BEND6 NKAPL FGF10 AAK1 EPN2 SNW1 FOXC1 SPEN ZNF423 PLXND1 DTX4 WWC1 NCSTN HEY1 HEY2 POFUT1 NEPRO SUSD5 IFT172 GAS2 GATA2 HEYL CNTN6 GMDS GOT1 DLL1 EPN1 ANXA4 FOXA1 ONECUT1 HOXD3 HES1 APP RBPJ IL2RA IL6ST JAG2 KCNA5 PEAR1 ENHO KIT KRT19 HES5 NOTCH2NLA HES3 HELT LFNG LLGL2 LLGL1 MIR126 MIR212 ARRB1 MAGEA1 CD46 MDK MFNG ASCL1 MMP14 NRARP MYC ATOH1 NFKBIA NOTCH1 NOTCH2 NOTCH3 NOTCH4 CCN3 YBX1 PBX1 SLC35C2 APH1A EGFL7 ZBTB7A ANGPT4 YTHDF2 PGAM2 PLN NLE1 NEURL1B DLL4 HES2 TMEM100 FBXW7 SYNJ2BP SLC35C1 HES6 MAML3 HIF1AN WDR12 PRKCI PSENEN MESP1 METTL3 PSEN1 PSEN2 TCIM POGLUT1 ZMIZ1 MIB1 HES4 NEUROD4 RFNG BCL6 ROBO1 ROBO2 RPS19 GALNT11 SEL1L PERP NOD2 RBM15 FAM129B BMP2 BMP7 SNAI2 DLK2 SNAI1 SOX9 STAT1 STAT3 ADAM17 TBX2 HNF1B TGFB1 TGFB2 TGFBR2 TIMP4 WNT1 CHAC1 BHLHE41 NKAP FAT4 IFT74 GDPD5 TSPAN14 APH1B ITCH TRAF7 NR0B2 MAML2 HES7 SORBS2 RITA1 BHLHE40 TP63 DLK1 KAT2B CBFA2T2 NEURL1 ITGB1BP1 ARRDC1 DNER DLGAP5 MAML1 NR1H4
GO_REGULATION_OF_NOTCH_SIGNALING_PATHWAY http://www.gsea-msigdb.org/gsea/msigdb/cards/GO_REGULATION_OF_NOTCH_SIGNALING_PATHWAY MIR1224 TSPAN5 AC242843.1 AC239811.1 POSTN WWP2 PDCD10 CREBBP GATA5 PRAG1 GSX2 DLX1 DLX2 JAG1 DTX1 EGF EGFR ELF3 EP300 AKT1 ERH BEND6 FGF10 AAK1 EPN2 SNW1 HEY1 HEY2 POFUT1 NEPRO GAS2 GATA2 CNTN6 DLL1 HES1 RBPJ IL6ST JAG2 PEAR1 ENHO KIT HES5 NOTCH2NLA LFNG LLGL2 LLGL1 MIR126 MIR212 ARRB1 MAGEA1 CD46 MFNG ASCL1 MMP14 NRARP NFKBIA NOTCH1 NOTCH3 NOTCH4 CCN3 SLC35C2 EGFL7 ZBTB7A YTHDF2 DLL4 FBXW7 SYNJ2BP SLC35C1 MAML3 HIF1AN PRKCI MESP1 METTL3 TCIM POGLUT1 ZMIZ1 RFNG BCL6 ROBO1 ROBO2 GALNT11 NOD2 FAM129B BMP7 DLK2 STAT3 TGFB2 WNT1 CHAC1 GDPD5 TSPAN14 MAML2 RITA1 TP63 DLK1 KAT2B CBFA2T2 NEURL1 ITGB1BP1 ARRDC1 MAML1
WNT_UP.V1_DN http://www.gsea-msigdb.org/gsea/msigdb/cards/WNT_UP.V1_DN ARHGEF25 ST3GAL2 VWA7 EIF2AK2 ADRB1 PHOSPHO2 CSF2RB MMP3 KRTAP3-1 RAMP3 PXMP2 GSTA4 ABCA1 SH2D2A PCP2 IGFBP6 PLXNA1 GPX3 NFATC2 CYBA KRT6B PRH1 RSPH1 LGALS3BP HSD3B7 DNAJC4 TBXA2R AGT GGNBP1 EFNB2 BPGM TSPAN5 CORT KCNA2 PLA2G1B MAPK7 SLC22A18 GPATCH2 MET PTGS2 BIK TUSC2 ST6GALNAC6 CLEC4D LAMA1 ADAM12 TGFBR1 GFAP SYT8 CLN3 DNASE1L3 SYNGR4 TAL2 KCNA4 LBP SDSL DNAJC11 IRF9 F10 NCOA2 ISG15 NT5DC2 HDGFL1 TFR2 COL9A1 IL12RB1 AARD ADAR NGEF SLC1A6 CCDC130 CD79B FAAH MYOG SINHCAF SEPT6 HES3 FZD7 NEO1 USP18 STC1 ASH2L GBP4 KCNA3 TRAPPC5 CD5 STK11IP ACVR1B KITLG KCND1 LHX1 VIM VEGFA MRAP SERGEF CNTN1 FOXN3 PRL CELF1 ACKR1 MEP1A HOXB5 PIGN DPF3 SPRR2B TLR6 LHB SYT11 THBD DDX25 IRF7 HEY1 HLA-DMB TSHB CD48 VPREB1 MAP3K2 CFHR1 CMBL CEACAM1 PIM2 NGF ABCC3 TANC1 ATP7B GRIN2C CD93 CMPK2 AC004551.1 G0S2 KDM5A HAND1 MAK16 ADCY4 CASQ2 LSR OCLN PPP1R1A DIO2 RASD1 NSMF TGFBR3 ETV4 HDHD5 APC2 TNFRSF21 PTPN22 ADAM2 IFIH1 CYTH1 ADAM19 AIRE FBXO8 CD44 TNFRSF8 PLA2G10 C16orf89 UBR1 GABRG2 CPN1 AKR1E2 IRGM NAGK RAI2 DDX46 CDO1 PCDH7 NMT2 TNNI3 OLFM1 GRM8 PCDHA10
NOTCH_DN.V1_DN http://www.gsea-msigdb.org/gsea/msigdb/cards/NOTCH_DN.V1_DN CR2 ID1 HES1 GPR68 NOTCH3 SLPI DDX4 ARSD MYCN CRH CCDC70 ABCB9 SPRY4 PPFIA4 LY6G6E MRM1 ABCA1 FCN1 BAIAP2 FUT3 AIPL1 KLHL12 PSPN WIF1 KLF2 RASSF8 AOPEP FCGRT TMEM92 LMTK2 ZBTB16 ITGA7 SHC2 SUSD4 TMEM132A CD27 SPANXC SLC45A2 SLN CA8 LPAR6 CNGB3 CHST1 CD82 LZTFL1 CST1 CLSTN3 ITGA9 CA4 AMOTL2 GZMB GPR32 CPNE6 MYO1D SH3TC1 HOXA3 MYO1E TBX3 ALLC IL11 RAMP2 CIDEB FAM30A RCBTB2 TBC1D29P FAT2 TRPV6 LINC01140 MYO1A TUBB2B SLC28A3 GPR65 SEMG2 KCNE1 SP140 KCNA5 IL7R VPREB3 CD209 MMP25 TM4SF20 MAGEC3 KRT9 TMPRSS6 CHRND CRHR2 LY6G6C IPO8 LHX6 C3orf18 MAPK13 KLF15 ICOS VNN2 OGDH VN1R1 EPB41L4A OPRPN IGLC2 ORAI2 GNAT3 ZSCAN5A CYP7B1 TRGV7 CA2 RHO ZNF221 UTF1 NSUN6 OR2H1 ZNF74 FSTL3 DOC2A HCRT CXCR3 BBOX1 CLIC2 SPRR2B ORM1 KCNA1 RPL39L TNFRSF8 CRMP1 LGALS4 ZC3H12A CAPN10 GUCY1A1 CYP4F2 ID4 RAB3IL1 PPARGC1A VEGFD E2F2 TRPC3 GUCA2A PGLYRP1 MAN1A1 GPR15 ACKR3 DRAM1 ZFP37 MYO7B SCNN1A BCAS1 PTCRA PBOV1 FAIM KCNN1 SERPINE1 SELE TNFSF10 PLAT ADTRP DNAJC3 GABRG3 IL26 GGTLC1 PHKG1 ATP8B3 HS1BP3 CHIA FMNL2 DIO2 DLK1 DCLK2 POF1B TFPI2 LRP4 IFI30 NID1 ACADL SNED1 CCDC81 PKD2L1 COQ8B PPIEL SAMD4B HLA-DRA INSM1 ARHGAP6 SHCBP1L CCNK C3AR1 CEL
NOTCH_DN.V1_UP http://www.gsea-msigdb.org/gsea/msigdb/cards/NOTCH_DN.V1_UP RAG1 FARP2 ANKH INAVA ADAMTS1 MEFV SRGAP2 CDHR1 DAAM2 CDKN1A GRIN1 PPEF2 KCNH2 ZNF230 TPPP AMPH IFIT1 TNNI3 NR6A1 STAT2 RHCE LHCGR CNTD2 CRYGC CRIP1 ZSCAN31 FKBP9 CCNA1 MN1 LINC01565 C6orf15 TFDP3 GABRR1 SLC5A4 SCTR CHRNA4 ABTB2 SLC17A9 KCNN4 ZNF112 RFPL1 DGKI PROC TENM3 BARX2 RNF39 ANXA2P3 ANGPTL7 KLHL26 RRAD NPTXR COL4A1 NPY6R MYF5 PAIP2B P2RX3 CDHR2 CHST8 SLC9A7 MFSD6 FYB1 GNG11 CAVIN2 HSPA1A KIF13B UPB1 GSN-AS1 PIAS3 SPTBN2 BHLHB9 RGS2 GTF3C5 WDR7 INPP1 LCE2B KCND1 PPP1R13L RAB23 CPA2 STARD8 LMAN1 CRYM HMOX1 RDH5 CALB1 TUBBP5 P3H3 SYT12 C8B CA5B ANGPT4 LMO3 RAG2 CASKIN2 RPS6KA4 CTF1 TLX1 PRKG1 PKDREJ CDC37L1 TTC21B RASGRP1 SCN8A IQCC GSTA3 HTN1 KLHL21 DLGAP1 DCLRE1A KRT8P12 CLMN CPEB3 EFNA3 LDOC1 KLRD1 MTG1 FLT3 HHLA2 KRT18 TGFBR1 MAP3K2 FOXP3 PNPLA3 PF4V1 DPT RP2 GUCA1B SLC30A10 GDNF HABP4 NEUROG3 S100A8 HPCAL4 ETNPPL CLDN16 CRYBG2 CTDP1 PLA2G7 PRKACA ADAP1 TNS1 ADGRG1 KSR1 DHX32 FGF16 YBX2 ZNF671 OR10H3 BBS10 CDKL1 CIB2 ITPKC AC003112.1 RUNX2 FGF13 DGKE CDKAL1 PRSS50 NEU2 SLC6A14 MAP3K1 ZBBX SLC5A1 NME5 TGFB1I1 PIM2 FAM215A ATP1B2 KCNK12 SETD1A TMEM74B ZNF507 SLC39A4 NYNRIN NPTX1 P2RY1 CPB1 TNFSF8 ESM1 EPN3 SSH3 NAA80 ATP8A1 ASB9 RANBP17 BTG2
GSE26351_UNSTIM_VS_WNT_PATHWAY_STIM_HEMATOPOIETIC_PROGENITORS_UP http://www.gsea-msigdb.org/gsea/msigdb/cards/GSE26351_UNSTIM_VS_WNT_PATHWAY_STIM_HEMATOPOIETIC_PROGENITORS_UP DDA1 P3H4 SLC22A17 MESD KLHL18 SPATS2 IL7R TRAF2 RHOC RNPEPL1 DPP7 ERLIN2 DDX3X NCKAP5L PEAK1 TTC17 TENT4B AMBRA1 CHD7 MZT2B MIER2 ALDH3A2 ZNF771 ZNF467 KLF10 DPH2 ZBTB4 ATP9B TBPL1 NFIX ZBTB18 GPR160 TOR4A SCAMP2 AJUBA CTTNBP2NL RHOQ PCBP4 ARHGAP35 SYTL3 AKTIP TCF4 DDIT3 C19orf66 FNTA IKZF1 STRN3 MAN2A2 EIF1 YARS ZDHHC24 PRPS2 TTC8 SEMA3G TENT5C TOGARAM1 ST6GALNAC4 ZNF76 MTMR3 NUPR1 ATP6V0B USP8 PEX6 MPHOSPH9 CASD1 CUL1 MYO7A RNF180 SMAD5 ALAS1 GTPBP2 CIAO1 VPS37B CRK DOCK1 GLB1 YPEL3 PHACTR4 KLF7 IL21R MARS ARHGAP31 TMEM243 UBR1 DHX34 GMPR ZNF445 SPINT1 C19orf44 ROGDI AFTPH TNFRSF1B ADPRM SLC3A2 FAM168B RAD50 SIPA1 LDLRAD4 OGA NFRKB FZD8 TMEM50A RETREG3 CSRNP2 TBL2 EXOG FARP1 RRP1B PSEN1 KLHL7 FERMT3 MMACHC SFT2D1 LAYN TP53INP2 LONRF1 SMAD7 MAP11 LAMP2 MT1E CANT1 FAM71A DEXI DLG3 PREX1 KIAA0753 H3F3C PPP1R10 USP30 PCYOX1 PLOD1 ANKS3 TMCC3 WIZ NCOA5 ATAT1 ZNF703 NOP2 ZNF777 TOM1 UNC93B1 GBP7 DHODH IRF2BPL GPATCH1 STAMBPL1 ASB4 BRI3 POMT1 VPS18 SMIM1 NFYC ARL8B BANK1 GPD1L RERE PDZD8 IFI27 SNTA1 IQSEC2 DNAJC12 CD151 ULK2 ELMOD2 TBC1D17 NECTIN2 ITM2C FAM160B1 SOS2 CALU TCTA AAGAB C11orf54 ABCG2 EEA1 POLRMT FCRL1 KIAA0319L HDAC5 PPP1CC YPEL2 MAST3 CHAMP1 GLIS3 CTNND1 TAF1C RCN2 ZNF658 IGFBP4 TSEN34 NUDT4 TP53I13 TRAPPC2B FAM53B LRPAP1 IVNS1ABP MDM2 DDX5 TMEM9B CALHM2
HALLMARK_NOTCH_SIGNALING http://www.gsea-msigdb.org/gsea/msigdb/cards/HALLMARK_NOTCH_SIGNALING JAG1 NOTCH3 NOTCH2 APH1A HES1 CCND1 FZD1 PSEN2 FZD7 DTX1 DLL1 FZD5 MAML2 NOTCH1 PSENEN WNT5A CUL1 WNT2 DTX4 SAP30 PPARD KAT2A HEYL SKP1 RBX1 TCF7L2 ARRB1 LFNG PRKCA DTX2 ST3GAL6 FBXW11
ST_WNT_BETA_CATENIN_PATHWAY Wnt/beta-catenin Pathway AKT1 AKT2 AKT3 ANKRD6 APC AXIN1 AXIN2 CBY1 CER1 CSNK1A1 CTNNB1 CXXC4 DACT1 DKK1 DKK2 DKK3 DKK4 DVL1 FRAT1 FSTL1 GSK3A GSK3B LRP1 MVP NKD1 NKD2 PIN1 PSEN1 PTPRA RPSA SENP2 SFRP1 TSHB WIF1
BIOCARTA_WNT_PATHWAY http://www.gsea-msigdb.org/gsea/msigdb/cards/BIOCARTA_WNT_PATHWAY SMAD4 HDAC1 FZD1 PPP2CA FRAT1 APC TLE1 MYC CTBP1 NLK WNT1 WIF1 GSK3B CREBBP CTNNB1 TAB1 CCND1 AXIN1 MAP3K7 BTRC CSNK2A1 HNF1A PPARD DVL1
DEMAGALHAES_AGING_DN http://www.gsea-msigdb.org/gsea/msigdb/cards/DEMAGALHAES_AGING_DN CA4 COL1A1 COL4A5 CALB1 CX3CL1 GHITM TFRC NDUFB11 UQCRFS1 ATP5MC3 FABP3 NREP DIABLO UQCRQ COL3A1 ACSS2
DEMAGALHAES_AGING_UP http://www.gsea-msigdb.org/gsea/msigdb/cards/DEMAGALHAES_AGING_UP TMED10 GFAP S100A6 SERPING1 MPEG1 LAPTM5 FCGR2A LGALS3 ADIPOR2 GNS ANXA5 S100A4 CLU NDRG1 ANXA3 APOD HLA-G LITAF CTSS PSMD11 HBA1 LYZ C1QB HIST1H1C VWF DCLK1 FCGR2B GSTA1 MSN EFCAB14 C1QC PTGES3 MT1F HCST GBP2 MGST1 RASA3 NPC2 EFEMP1 VAT1 DERL1 C3 PCSK6 C4A AC124319.1 GPNMB TXNIP SGK1 JCHAIN C1QA SPP1 B2M IL33 CLIC4
FRIDMAN_SENESCENCE_DN http://www.gsea-msigdb.org/gsea/msigdb/cards/FRIDMAN_SENESCENCE_DN CCNB1 BMI1 CCN4 CKS1BP7 ID1 LAMA1 EGR1 MARCKS CDC25B ALDH1A1 E2F4 LDB2 COL3A1
GO_CANONICAL_WNT_SIGNALING_PATHWAY http://www.gsea-msigdb.org/gsea/msigdb/cards/GO_CANONICAL_WNT_SIGNALING_PATHWAY CDH2 CDH3 TMEM170B MIR665 FRAT1 PTPRU G3BP1 PSME3 PSMD14 APC2 YAP1 MAD2L2 RUVBL2 FZD10 BIRC8 CTHRC1 LRRK2 CSNK1A1L PSMB11 COL1A1 TMEM198 AMER1 MAPK14 PSMA8 PRICKLE1 CSNK1A1 CSNK1D CSNK1E CSNK1G2 CSNK1G3 NOTUM DACT3 NKX2-5 CTNNB1 CTNND1 CTNND2 DAB2IP CYLD DAB2 DAPK3 DDIT3 DDX3X TLE5 TMEM64 DLX5 DVL1 DVL2 DVL3 EDA EGF EGFR EGR1 EMD AMER3 AMER2 CCNY FGF8 FGF9 FGF10 FGFR2 ANKRD6 DKK1 HECW1 FOXO1 FOXO3 KANK1 PSME4 TMEM131L CTDNEP1 FRAT2 FOLR1 DAAM2 FRZB FZD2 BAMBI ASPM SOSTDC1 WWTR1 ADGRA2 PYGO1 NPHP4 GATA3 LATS2 GREM1 NPHP3 DKKL1 DKK4 DKK3 DKK2 INVS GPC3 RBMS3 GLI1 GLI3 GNAQ BCL9L RSPO1 RAPGEF1 GSK3A GSK3B HDAC1 APC XIAP RSPO2 RSPO4 APOE IGFBP1 IGFBP2 IGFBP4 IGFBP6 RBPJ ILK ISL1 JUP DRAXIN SHISA6 LRP4 LRP6 LRP5 ARNTL MIR1-1 MIR1-2 MIR145 MIR19B1 MIR19B2 MIR222 MIR29B1 MIR29B2 SMAD3 MCC MITF MLLT3 NRARP MYH6 NDP NFKB1 NOTCH1 ROR2 NR4A2 PRKN SOST LEF1 DACT1 UBR5 GSKIP GPRC5B PFDN5 CDK14 PIN1 CSNK1G1 WNT4 NLE1 TRPM4 MKS1 PPM1A SDHAF2 PPP1CA GID8 USP47 RNF220 LGR4 FERMT1 SCYL2 VPS35 MESP1 SULF2 PROP1 PSEN1 PSMA1 PSMA2 PSMA3 PSMA4 PSMA5 PSMA6 PSMA7 PSMB1 PSMB2 PSMB3 PSMB4 PSMB5 PSMB6 PSMB7 PSMB8 PSMB9 PSMB10 PSMC1 PSMC2 PSMC3 PSMC4 PSMC5 PSMC6 PSMD1 PSMD2 PSMD3 PSMD4 PSMD5 PSMD7 PSMD8 PSMD9 PSMD10 PSMD11 PSMD12 PSMD13 PSME1 PSME2 PTEN PTK7 CHD8 CCAR2 PTPRO RAB5A RARG KLHL12 LGR6 BCL9 RYK RYR2 SDC1 TNN PRDM15 SFRP1 SFRP2 SFRP4 SFRP5 SOX17 SHH SMURF2 SIAH2 PORCN BMP2 WNK1 WNK2 SNAI2 CAPRIN2 SOX2 SOX4 SOX9 SOX10 SRC STK3 STK4 STK11 TBXT TBL1X TCF7 TCF7L2 TLE1 TLE2 TLE3 TLE4 UBE2B KDM6A VCP WNT1 WNT2 WNT3 WNT5A WNT7A WNT7B WNT8A WNT8B WNT10B WNT11 WNT2B WNT9A WNT9B FZD5 KREMEN2 LRRK1 TBL1XR1 FZD3 TLE6 JADE1 WLS BICC1 ZNF703 FUZ WNT10A TNKS2 WNT5B RNF146 AXIN1 AXIN2 FZD1 FZD4 FZD6 FZD7 FZD8 FZD9 TCF7L1 SOX7 KREMEN1 ZNRF3 ZBED3 RECK LZTS2 CUL3 RSPO3 NKD1 NKD2 DIXDC1 LGR5 CAV1 RUVBL1 PLPP3 JRK TNKS SCEL BTRC WNT3A LIMD1 OTULIN SEMA5A IFT20 PYGO2 TBX18 USP8 LATS1 LRP5L TMEM88 NOG KLF4 SLC9A3R1 PSMF1 FAM53B USP34 PSMD6 MED12 RBX1
HALLMARK_INFLAMMATORY_RESPONSE http://www.gsea-msigdb.org/gsea/msigdb/cards/HALLMARK_INFLAMMATORY_RESPONSE CXCL10 CCL2 CCL5 FPR1 CCL20 IL1A CXCL8 CCL7 CCL22 CXCL11 CCR7 EDN1 CD40 CXCL9 IL6 IL1B TLR2 IL1R1 CD69 ICAM1 CCRL2 AQP9 EREG C3AR1 GNA15 CMKLR1 PTGER4 LIF IL15 NAMPT OPRK1 ITGB8 PTAFR ADM PLAUR NFKB1 INHBA OSM TNFSF10 TNFSF15 IFNGR2 ADGRE1 IL12B CSF1 CXCL6 TNFRSF9 LYN ACVR2A LDLR BDKRB1 HRH1 F3 BST2 PTGIR CD55 CALCRL CSF3 GPR132 IL4R NLRP3 IL15RA ADORA2B GCH1 OLR1 PTGER2 CSF3R MYC RELA TNFAIP6 IL7R IL18 GABBR1 CD82 TNFSF9 NMUR1 IL2RB TLR1 LPAR1 IRAK2 RIPK2 MMP14 P2RX7 SLC11A2 SELL P2RY2 ABCA1 FFAR2 PROK2 GNAI3 TACR1 SLC7A1 CDKN1A CYBB TIMP1 HBEGF SCARF1 EBI3 NFKBIA SRI SLC7A2 CCL17 TLR3 APLNR OSMR IL10RA PSEN1 GPR183 ATP2B1 TNFRSF1B BEST1 GPC3 SCN1B ACVR1B HPN SEMA4D KLF6 CD48 CXCR6 SLC1A2 GP1BA TAPBP RGS16 SLAMF1 LCK HIF1A AHR NMI RHOG TPBG NPFFR2 IFNAR1 ICOSLG RASGRP1 IFITM1 KCNJ2 LY6E IL18R1 IL10 KCNA3 HAS2 DCBLD2 LAMP3 VIP CD70 RGS1 SLC31A1 ADRM1 KCNMB2 SERPINE1 MXD1 AXL MEFV PVR CCL24 PDE4B LCP2 PDPN IRF7 MET ATP2A2 SLC31A2 FZD5 ITGA5 SGMS2 MARCO CD14 EIF2AK2 ROS1 ATP2C1 NDP BTG2 MSR1 PTPRE RNF144B PCDH7 SPHK1 IL18RAP RTP4 RAF1 CHST2 ITGB3 KIF1B SELE NOD2 C5AR1 EMP3 CLEC5A TACR3 SLC4A4 MEP1A SELENOS LTA PIK3R5 STAB1 IRF1 ICAM4 P2RX4 ABI1 CX3CL1 SLC28A2
KEGG_WNT_SIGNALING_PATHWAY http://www.gsea-msigdb.org/gsea/msigdb/cards/KEGG_WNT_SIGNALING_PATHWAY JUN LRP5 LRP6 PPP3R2 SFRP2 SFRP1 PPP3CC VANGL1 PPP3R1 FZD1 FZD4 APC2 FZD6 FZD7 SENP2 FZD8 LEF1 CREBBP FZD9 PRICKLE1 CTBP2 ROCK1 CTBP1 WNT9B WNT9A CTNNBIP1 DAAM2 TBL1XR1 MMP7 CER1 MAP3K7 VANGL2 WNT2B WNT11 WNT10B DKK2 CHP2 AXIN1 AXIN2 DKK4 NFAT5 MYC SOX17 CSNK2A1 CSNK2A2 NFATC4 CSNK1A1 NFATC3 CSNK1E BTRC PRKX SKP1 FBXW11 RBX1 CSNK2B SIAH1 TBL1Y WNT5B CCND1 CAMK2A NLK CAMK2B CAMK2D CAMK2G PRKACA APC PRKACB PRKACG WNT16 DAAM1 CHD8 FRAT1 CACYBP CCND2 NFATC2 NFATC1 CCND3 PLCB2 PLCB1 CSNK1A1L PRKCB PLCB3 PRKCA PLCB4 WIF1 PRICKLE2 PORCN RHOA FRAT2 PRKCG MAPK9 MAPK10 WNT3A DVL3 RAC2 DVL2 RAC3 FZD3 DKK1 CXXC4 DVL1 FOSL1 CUL1 WNT10A WNT4 SMAD3 TCF7 SMAD4 RAC1 TCF7L2 SMAD2 WNT1 MAPK8 EP300 WNT7A GSK3B WNT7B PSEN1 WNT8A WNT8B WNT2 WNT3 WNT5A WNT6 CTNNB1 PPP2CB PPP2CA PPP2R1A TBL1X PPP2R1B ROCK2 NKD1 FZD10 FZD5 NKD2 TCF7L1 RUVBL1 PPARD PPP3CB TP53 PPP3CA PPP2R5A PPP2R5E PPP2R5D PPP2R5C PPP2R5B FZD2 SFRP5 SFRP4 CHP1
REACTOME_CELLULAR_SENESCENCE http://www.gsea-msigdb.org/gsea/msigdb/cards/REACTOME_CELLULAR_SENESCENCE CDC27 E2F2 SCMH1 MRE11 MAP2K3 MAPK9 ANAPC4 MAP2K4 MAP4K4 RPS6KA2 UBE2D1 EED MAP2K7 TNRC6C MAPKAPK5 ANAPC5 TNRC6A AL096870.1 AGO1 CDC23 CABIN1 MAPK1 HIRA TNRC6B E2F1 RBBP7 MAPK3 ACD NBN CCNE1 FZR1 ERF CDK6 H2AFV EZH2 MAPK8 MAP2K6 NFKB1 MAPK10 ANAPC15 CDKN1B PHC1 ASF1A MAPK14 E2F3 LMNB1 RAD50 TFDP2 MAPKAPK3 IL1A RPS6KA1 UBN1 RNF2 CDKN2C CDK2 HIST1H1D HIST1H1A HIST1H2BJ CDKN1A ID1 AGO3 POT1 CDKN2D CDC16 H3F3B KDM6B TERF2 CCNA1 PHC2 AGO4 ETS1 CDK4 MDM2 IL6 TXN HMGA1 RB1 MINK1 TP53 ANAPC11 CBX8 CBX4 RPS27A CCNA2 HIST1H2BA TERF1 CDKN2B CDKN2A ATM HMGA2 UBC ANAPC1 TNIK MOV10 ETS2 HIST1H2BD HIST1H4H RBBP4 MAPKAPK2 H3F3A IGFBP7 H2AFZ ANAPC10 ANAPC16 MAPK7 TERF2IP HIST3H3 BMI1 HIST1H1E STAT3 CXCL8 UBE2E1 UBB FOS IFNB1 CEBPB KAT5 RELA PHC3 CBX2 UBE2C CCNE2 ANAPC2 CDC26 RPS6KA3 JUN SUZ12 HIST1H2AC HIST1H2BC EHMT1 EP400 HIST2H3D CBX6 HIST2H2AC HIST1H1B HIST2H2BE HIST1H2BL MAPK11 SP1 HIST1H1C H2AFX H1F0 ANAPC7 HIST1H2AD HIST3H2BB HIST1H4C HIST1H3J HIST1H4J HIST1H3D MAP3K5 HIST4H4 HIST1H2BK TFDP1 MDM4 HIST2H3C HIST2H2AA3 HIST2H3A RING1 EHMT2 UBA52 HIST1H2BN H2BFS H2AFJ HIST2H4B HIST2H4A HIST2H2AA4 HIST1H4K HIST1H2BM HIST1H2BG HIST1H3G H2AFB1 HIST1H2BE HIST1H4F HIST1H2BO HIST1H3E HIST1H4L HIST1H3I HIST1H2BH HIST1H3A HIST1H4I HIST1H2AJ HIST1H2BB HIST1H4E HIST1H2AE HIST1H4D HIST1H2BF HIST1H3F HIST1H2AB HIST1H2BI HIST1H4A HIST1H4B HIST1H3H MIR24-2 MIR24-1
REACTOME_CYTOKINE_SIGNALING_IN_IMMUNE_SYSTEM http://www.gsea-msigdb.org/gsea/msigdb/cards/REACTOME_CYTOKINE_SIGNALING_IN_IMMUNE_SYSTEM ITGA2B CRLF1 MAP3K14 TNFRSF12A RALA NOS2 PSMB1 IL32 RANBP9 CD4 FYN PPP5C ALOX5 PSMC4 IL20RA HGF BIRC3 VIM CD44 IFNGR1 TNFRSF1B NUP160 LRRC7 GAB2 PIAS1 MAP2K3 VCL CUL3 PSMA4 TPR TNFRSF17 KITLG TNFRSF9 MAPK9 PIK3CB CUL1 TAB2 EIF2AK2 IL17RB RASGRF1 CAMK2B NDC1 CNN2 ERBB3 MAP2K4 PPP2R5A FGFR2 SP100 TNFRSF1A FGFR3 IFI35 MEF2A IP6K2 PSME4 PPP2R5B NUP133 RORA NEDD4 SPTB FGF10 FGF22 CAMK2A CDC42 RPS6KA2 PRKACA PTPN18 FBXW11 PTGS2 EIF4G3 NUP37 FGF4 RASAL2 MARK3 FSCN1 ACTB DLG1 PTPN23 STXBP2 MAP2K7 NFKB2 IL4R APBB1IP ACTN2 FGFR1 ARAF PIK3C3 PPP2R5C FGF20 TOLLIP HSP90AA1 CXCL2 MEF2C IL12RB2 DLG3 TXLNA SEH1L PSMC5 MMP2 PTPN4 AC004551.1 RPLP0 PEBP1 BRAP BIRC5 ICAM1 IRAK3 FLT3LG IL5RA PSME1 NUP50 AAAS PSMD5 NUP188 BLNK IL11 JAK2 IL12RB1 PSMD8 MTAP CRKL OSM MAPK1 HMOX1 PDGFB TAB1 CSF2RB IL2RB RBX1 CTSG SOS2 PSMC6 PSMA3 HIF1A PSMC1 RPS6KA5 PSMB5 PSMA6 NFKBIA PSME2 MMP9 CD40 RAE1 PSMA7 TRIB3 HCK SAMHD1 PSMD10 MID1 CD40LG TIMP1 TNFSF13B FGF9 KPNA3 MAPK3 NUP93 CCL22 PSMD7 STX4 IL21R CSK TRIM35 RIPK2 IKBKB IL7 PPP2CB NFKBIB RELB FCER2 IL27RA VRK3 RASAL3 AKT2 EBI3 TGFB1 TYK2 GRIN2D PPP2R1A JAK3 PIK3R2 RASA4 MET STX1A NOD1 PTPRZ1 PSMA2 TRIM14 TNFSF8 DDX58 RAPGEF1 GATA3 MAPK8 FGF8 MAP3K8 CSF3 PSMD3 KPNB1 NUP88 PSMD11 CCL2 DUSP3 MAP2K6 LAMTOR3 NFKB1 AREG MAPK10 GAB1 IL2 TRIM2 RAPGEF2 HSPA8 CCND1 TRIM3 EIF4G2 IL10RA BIRC2 CBL AIP NUP98 PTPN5 VWF PSMD9 IL23A FGF6 DUSP16 AC005840.1 OAS3 OAS2 RASAL1 IL26 IFNG NUP107 PTPN6 NANOG OPRM1 MAPK14 SOD2 IL17A IL17F TRIM38 PPP2R5D VEGFA GHR HSPA9 HBEGF IL12B RASGRF2 LMNB1 PRLR IL4 IL5 SKP1 NUP155 PPP2CA FGF1 LIFR PDGFRB BCL6 CD86 KPNA1 CISH MAPKAPK3 EIF4G1 IL1A CCL20 POMC PSMD14 SPTBN1 FN1 STAT1 IL1R2 IL1R1 IL1RL2 IL1RL1 IL18R1 IL18RAP SDC1 SOS1 ATF2 SUMO1 OPRD1 RAP1A CAPZA1 TRIM62 RHOU LAMTOR2 GBP3 GBP1 ARTN PIK3R3 FASLG TNFSF4 IRF6 RPS6KA1 CREB1 FOXO3 FGF23 CSF3R IFIT3 IFIT2 DUSP1 TEK IFNA6 IFNA8 NUP43 TNFSF18 TCP1 TNFSF11 KBTBD7 EGR1 SOCS2 DUSP4 PTK2B TNFRSF8 TRIM25 TRIM6 CD80 CCR2 PIK3CA FLT3 HNRNPA2B1 TWIST1 CNTFR WDR83 ATF1 IL13RA2 PLCG1 VAMP7 IL9R IL17C F13A1 CDKN1A NUP153 EREG MT2A IRF1 NUP85 IL1B IL37 PSPN TNFSF9 CD70 TNFSF14 PSMF1 GFRA4 PSMB2 IRF3 STAT5A IFI6 ELK1 NUP214 MAP2K2 CANX TRAF2 RAP1B IL22 PIN1 SMARCA4 SEM1 PTPN12 LIF IRF5 PSMA1 SHC2 LBP BST2 LAMA5 UBE2M DUSP9 EDA2R TRAF3 PSME3 IL13RA1 SNRPA1 TRIM21 RAF1 NUP210 TRIM5 TRIM22 XAF1 DLG4 SNAP25 PTPRA KL KRAS CA1 IRAK2 TRIM45 RSAD2 IL6ST IL2RA IL15RA PDGFRA KLB ANXA1 HAVCR2 OASL CD36 MAP3K7 TEC RNASEL EIF4E2 EDAR FLNB LCP1 NUPL2 IL6 GH2 CSH1 IL10 IL36G IL1RN IL36A IL36RN IL36B IL1F10 DAB2IP PSMB7 MYC IL33 IL11RA TLN1 IFNA21 PIM1 FRS3 IRF4 ARRB1 IL18BP TRIM29 PPP2R1B CASP1 SPTBN5 PPM1B DUSP5 STAT4 HERC5 FGF5 IL21 FGF2 NUP54 EGF CD27 DUSP6 NUP58 PELI2 FGF7 PML IQGAP1 ITGAX IRF8 PDPK1 KSR1 ARRB2 TP53 EIF4A3 TNFRSF11A ERBB2 VAV1 IFITM3 IFNAR1 SOD1 APP AKT1 IL19 PSMB6 CNKSR1 IL22RA1 PSMA5 POU2F1 RORC MCL1 DUSP10 ARF1 PTPN7 RPS27A IL17RD UBA3 CAMK2D OSMR PIK3R1 RASA1 TSLP IL9 GFRA3 EGFR MSN IL2RG IFNA5 IFNA16 SHC3 LCN2 ZEB1 CAMK2G GSTO1 NCAM1 MMP3 CNKSR2 ITGB1 TRIM48 CTF1 FCGR1A PDCD4 DLG2 IL18 FOXO1 UBC EIF4E ADAM17 GFRA1 PTPN14 PDE3B RASGRP3 HNRNPDL RANBP2 PPP2R5E ANGPT1 GBP5 PSMA8 NUP205 RASA2 BATF FGF18 UBE2L6 EIF4A2 SEC13 NRG1 IL34 KIT MX1 TAB3 BRAF TNFRSF14 DUSP2 NRG2 EDA FGF17 IFNAR2 IFNGR2 PSMD4 PSMB4 THEM4 TNFRSF13C ITGB2 S100B SPTBN4 SHC1 ADAR IL6R CCR5 FGFR4 SQSTM1 PSMC2 USP41 ALOX15 TNFSF13 EIF4A1 FGF19 JAK1 IL23R GBP2 GBP4 VCAM1 PEA15 MAPKAPK2 IL20 IL24 TRIM17 NUP35 S100A12 TGFA PAQR3 EIF4E3 TRIM46 SPTA1 PTPN13 PSMD6 IL17RE IL17RC CXCL1 CCR1 PRKCD DUSP7 IL15 ABCE1 CASP3 IL3 CSF2 IL22RA2 IL31RA COL1A2 TNFRSF11B YWHAZ SYK INPPL1 RET PSMC3 IFI27 SPRED1 IL25 BTRC FRS2 ARIH1 RAG1 MAPK7 HSP90B1 B2M STAT6 STX3 YWHAB SMAD3 PHB CRK NOD2 TRIM68 NKIRAS2 IRF2 GFRA2 STAT3 GDNF IL7R IL12A TNIP2 INPP5D LGALS9 MAP2K1 IRS1 IL13 CXCL10 IL1RAPL1 PTK2 PTAFR PTPN9 CXCL8 BOLA2B NRG4 HNRNPF ITGAM UBE2E1 UBB FOS STAT2 S1PR1 FPR1 NRTN SOCS5 TRIM8 JUNB KSR2 BCL2L1 FGG FGA FGB PIK3CD RASGRP4 BCL2 IFNB1 CCL11 PRL ISG20 IL16 RASGRP1 CCL19 CFL1 MYD88 RELA MAP3K11 SAA1 PSMD1 STAT5B SPTBN2 PITPNA PELI3 IL20RB HRAS BTC PDE12 RAG2 TRAF6 PSMD2 PTPN2 CLCF1 YES1 GRIN1 TALDO1 RPS6KA3 JUN IL17RA GRB2 UBE2N SH2B1 ERBB4 PTPN11 HLA-DQB1 CIITA PAK2 SOX2 TNFSF15 UBA7 IFNL1 KPNA2 CSF1R EPGN NDN ANXA2 LCK BOLA2 GBP6 MX2 HIST2H3D IFNL2 TBK1 ACTG1 IRAK1 CSF1 DUSP8 SOCS3 USP18 SIGIRR IFITM2 IL3RA SOCS1 MAPK11 IFNLR1 KPNA7 MUC1 IRF7 P4HB PSMD13 BRWD1 NRG3 IFIT1 IFITM1 IRS2 RASA3 EDARADD KPNA4 IFNA10 TNFRSF4 TNFRSF18 FGF3 ISG15 IFNA2 SPRED3 MAOA HMGB1 IL1RAP HLA-DRB3 HLA-DRB1 PPIA POM121 PTPN1 PRTN3 PIK3R4 FGF16 MMP1 NF1 HLA-DQA1 KPNA5 FLNA IFNL3 SRC HIST1H3J PSMD12 IL27 SYNGAP1 PELI1 HIST1H3D PDGFA SERPINB2 SPTAN1 NKIRAS1 IFNA1 IRAK4 FCGR1B CSF2RA SPRED2 HLA-DRB5 CALM1 MAP3K3 RASGEF1A HIST2H3C HIST2H3A PTPN20 PSMB8 HLA-DRA AGER HLA-C HLA-E TRIM10 TRIM31 HLA-G HLA-F IL31 PSMB10 CRLF2 HLA-H HLA-B HLA-A IGHE IGHG4 IGHG1 NUP62 NRAS CHUK GBP7 LAT IRF9 IFNA7 TNFRSF25 IFI30 VAMP2 CEBPD UBA52 PSMB11 HLA-DPB1 LTA HLA-DRB4 LTB IFNA14 HLA-DPA1 HLA-DQB2 TNF IFNA13 TRIM26 IFNA17 IFNA4 HLA-DQA2 TNFSF12 TLR9 PSMB9 TNFRSF13B MIF CNTF TNFRSF6B IL10RB GSTA2 UBE2V1 LYN TRIM34 ITGB3 GH1 IKBKG CCL5 POM121C GRIN2B HIST1H3G HIST1H3E CCL4 HIST1H3I HIST1H3A PSMB3 CCL3L1 NEFL CCL3 CCL3L3 HIST1H3F HIST1H3H
REACTOME_SENESCENCE_ASSOCIATED_SECRETORY_PHENOTYPE_SASP http://www.gsea-msigdb.org/gsea/msigdb/cards/REACTOME_SENESCENCE_ASSOCIATED_SECRETORY_PHENOTYPE_SASP CDC27 ANAPC4 RPS6KA2 UBE2D1 ANAPC5 CDC23 MAPK1 MAPK3 FZR1 CDK6 H2AFV NFKB1 ANAPC15 CDKN1B IL1A RPS6KA1 CDKN2C CDK2 HIST1H2BJ CDKN1A CDKN2D CDC16 H3F3B CCNA1 CDK4 IL6 ANAPC11 RPS27A CCNA2 HIST1H2BA CDKN2B CDKN2A UBC ANAPC1 HIST1H2BD HIST1H4H H3F3A IGFBP7 H2AFZ ANAPC10 ANAPC16 MAPK7 STAT3 CXCL8 UBE2E1 UBB FOS CEBPB RELA UBE2C ANAPC2 CDC26 RPS6KA3 JUN HIST1H2AC HIST1H2BC EHMT1 HIST2H3D HIST2H2AC HIST2H2BE HIST1H2BL H2AFX ANAPC7 HIST1H2AD HIST3H2BB HIST1H4C HIST1H3J HIST1H4J HIST1H3D HIST4H4 HIST1H2BK HIST2H3C HIST2H2AA3 HIST2H3A EHMT2 UBA52 HIST1H2BN H2BFS H2AFJ HIST2H4B HIST2H4A HIST2H2AA4 HIST1H4K HIST1H2BM HIST1H2BG HIST1H3G H2AFB1 HIST1H2BE HIST1H4F HIST1H2BO HIST1H3E HIST1H4L HIST1H3I HIST1H2BH HIST1H3A HIST1H4I HIST1H2AJ HIST1H2BB HIST1H4E HIST1H2AE HIST1H4D HIST1H2BF HIST1H3F HIST1H2AB HIST1H2BI HIST1H4A HIST1H4B HIST1H3H
CHICAS_RB1_TARGETS_SENESCENT http://www.gsea-msigdb.org/gsea/msigdb/cards/CHICAS_RB1_TARGETS_SENESCENT GCH1 TSLP PHF19 IDH2 ANP32B RPA1 PLTP LIF BCL2A1 NET1 RBBP7 CXCL11 ALDH7A1 MCM4 BEAN1 CDC25A CLIP1 HIP1 RFC2 S100PBP GABRB1 IL6ST TMEM132A RBBP8 PRKD3 GFPT2 SRGAP2B RANBP1 CPNE2 ATAD2 RAB27A TIFA VCAM1 HELLS PRPS2 ARMCX2 SESTD1 TMPO CDK2 MALAT1 GCLM IL1A JUN BDKRB2 SLC25A40 BEX1 PDLIM3 CXCL6 POLD3 PTN ST3GAL1 RIF1 NFKBIZ PTGS2 IVNS1ABP CNRIP1 SOX17 MTR PTMA ITPRIPL2 SLAMF8 ANPEP APCDD1L HAUS2 TNFAIP8 LINC00342 HLA-B NLRP1 LRIG1 ERCC1 FAM129A GPD2 TRMT5 POGLUT3 PPBP MMP12 SLC5A3 DONSON BTBD11 TEX30 RFLNB SERPINA1 PDP1 CXCL10 FUT8 CDC6 FBXO21 PRRT2 TGIF1 RMI1 FRMD4A ODC1 MRI1 RDH10 NUP85 MCUR1 RFC4 CLDN5 SF3B1 ACKR3 NAP1L1 CYB5A HPSE MXRA5 FERMT1 SMOC1 TMEM97 CA12 USP1 PI3 HSPB11 RRM2 SAMHD1 TSC22D1 TRIB3 GBP3 PAICS SMC3 CLGN MMP9 LAPTM5 MSH2 BIRC3 CHST2 RGS5 PMS1 TPR GMPS CDK5RAP2 PAQR5 MSH6 RASSF8-AS1 ZBTB38 CBX5 DHFR BTG3 SFR1 KRAS CISD2 BAALC H2AFY ARNTL2 BGN KLHL7 SLC27A3 EHF IL33 ABHD3 KANK2 NSMCE4A CENPX SMAD5 GLUL GINS2 NCAPG2 SYNJ2 FEZ1 CENPV PRIM1 OXCT1 RBM39 EXOSC8 SMC6 WWTR1 PREX1 IL27RA IGF2 PAXIP1-AS1 ACOT7 MMP10 KCNG1 SLC7A2 UBE2L6 SLFN11 GON7 LRFN5 PITPNC1 TREM1 POU2F2 SIMC1 GLIS3 TYMS KPNB1 RNF138 TCEAL3 BBS2 FBXO11 ACBD3 CD69 GLCCI1 FEN1 ADD3 EMP2 FAM114A1 RASSF3 SOX11 DTL SNRPB ESM1 OSMR PDZK1 CTDSPL2 DNM1 CTSS CXCL5 CEP19 IL37 C15orf48 RHOF ALDH1A3 KYAT3 DTX3L TIPIN IL11 AKR7A2 COL6A2 TCEAL7 BRI3BP SERPINB3 POSTN LRP11 SLC2A4RG PELI1 SCG5 NETO2 CRIP1 GJB2 ENO2 SP100 SLC25A37 C11orf96 CTH GUSB SLC20A1 CDCA7 SKA2 ACADM FLT1 TMEM107 CYP3A5 LMNB1 DIRAS3 GSAP COL10A1 TRPC1 LANCL1 LRRC15 MCMBP CST1 FKBP11 TNP1 PRSS3 EIF4EBP1 SKP2 TM6SF1 NQO2 GMNN GK PSIP1 SLBP MTHFD1 WEE1 COL7A1 CYP3A7 SLC7A1 PRXL2A PAWR RAB27B FP236241.1 SERPINB4 CASP8AP2 DIP2C HDGFL3 OSBPL8 MCM2 CD82 MIB1 TSPAN5 TXNIP VEGFA FAH NSD3 FANCI NUDT21 PRKDC OXR1 IGFBP5 COMMD4 MCM5 MBD4 IFIT3 LSP1P4 FADS1 NR2F1-AS1 CXCL3 PPIL3 TCIRG1 PIDD1 TUBG1 HADH CERS6 FBL SPATA18 RNASET2 UBE2T JAM2 SRRM2 CHI3L1 SMARCC1 NAMPT IFITM1 TMEM106C LPP DGAT2 GJC1 QPCT DYNC1H1 SON MELK MCM7 APOBEC3G GXYLT1 CSTF3 BTN3A3 PSMB8 RRM1 PLAGL1 TMEM54 CYP26B1 NRG1 C6orf136 MEA1 GINS1 RNF168 ATP1B1 THBD IFI6 GTPBP10 NUCKS1 SDC3 DDX24 HEY1 CXCL1 GDNF GALNT6 MCUB PRR11 PCNA HMGN2P46 PRTFDC1 BRD3OS DUT STEAP1 PRRX1 IFNGR2 VNN1 AKAP12 C9orf40 FAF2 ISG20 PRSS3P2 SEL1L3 MEDAG CCDC71L RFTN2 RBBP4 KIAA1841 MCM3 RAB38 GAL MLLT6 THAP9-AS1 SMC1A CAMK2N2 FGD5-AS1 NUSAP1 UHRF1 ARL6IP6 SLC39A8 TAGLN3 HAS2 RAB31 PTP4A2 KCTD12 SLC25A36 ISG15 BBX RASD1 DDX60 ZEB1 SERPINB7 LDB2 ENC1 RPA3 CAMTA1 CCNE2 RASL12 TMEM173 MAST2 VASH1 RIPK2 G0S2 ACYP1 FZD1 IGFBP3 EDNRA PLEK2 SERPINA9 VWA5A IL24 SLC43A3 C3 MCM6 IRF9 DCK SDHAF4 PLSCR1 AREG SNX7 PRRC2C VRK2 HIPK2 CCNE1 PI4K2B CDC42BPA FANCG TBX3 MYH10 MLH1 DNAJC1 DIO2 ANKRD9 DERA STMN3 CST4 SRD5A3 ZNF846 RFWD3 BTN3A2 ABI2 SPDL1 NOL4L EMP1 LUZP1 NAV1 RGS17 MAGEF1 TBX2-AS1 ERGIC1 LGALS3BP PCLAF ZDHHC6 RHOBTB3 SLCO4A1 CKLF PSME2 PHGDH SLC14A1 MAP4 SAC3D1 MLPH ITGBL1 CBR3 HMGN2 FBXO45 EGR3 CSF3 FANCL CCL3 CST2 THY1 ACTG2 NASP PTX3 CENPK AGGF1 RBCK1 DSN1 ZG16B GEM NME7 TTC3 JDP2 SEMA4B DUSP5 HSPA4L CYBRD1 SERPINE1 WASL MMD WDR34 TNFSF15 ATRX ZNF367 HMGA2 C3orf14 HACD3 CACYBP WDHD1 DPYSL3 PCOLCE CCNB1IP1 ING2 CYGB NUCB2 MRPS6 NRGN TNFAIP3 DEK GNB1 XIST HAUS1 CDCA7L JARID2 EFS CXCL2 IFT80 INHBA DCBLD1 TOP1 UBR7 C11orf54 EZH2 CDT1 SPEN DDB2 CEBPZ WDFY2 UQCC2 DNAJC18 CKMT2-AS1 ACPP ERAP2 CYB5R2 CD274 WDR76 FBXL21P TRIB2 ENOSF1 GNG2 IL6 PSMC3IP FOXQ1 RNASEH2A FMC1 CDC7 CSF2 CEP57 CCL20 SNORD7 WDR54 CEMIP THRAP3 ZNF83 DDIT4 HPRT1 TRIM56 THBS2 CENPS SPIN4 YY1 HAT1 CENPU
WP_SENESCENCE_AND_AUTOPHAGY_IN_CANCER Senescence and Autophagy in Cancer AKT1S1 AMBRA1 ATG10 ATG12 ATG13 ATG14 ATG16L1 ATG3 ATG5 ATG7 BCL2 BECN1 BMI1 BMP2 BRAF CCL3 CD44 CDC25B CDKN1A CDKN1B CDKN2A CEBPB COL10A1 COL1A1 COL3A1 CREG1 CXCL1 CXCL14 CXCL8 E2F1 FKBP8 FN1 GABARAP GABARAPL1 GABARAPL2 GSK3B GSN HMGA1 HRAS IFI16 IFNB1 IFNG IGF1 IGF1R IGFBP3 IGFBP5 IGFBP7 IL1A IL1B IL24 IL3 IL6 IL6R IL6ST ING1 ING2 INHBA INS IRF1 IRF5 IRF7 JUN KMT2A LAMP1 LAMP2 MAP1LC3A MAP1LC3B MAP1LC3C MAP2K1 MAP2K3 MAPK1 MAPK14 MDM2 MIR29B2 MIR29C MIR3606 MLST8 MMP14 MTOR PCNA PIK3C3 PLAT PLAU PTEN RAF1 RB1 RB1CC1 RNASEL RSL1D1 SERPINB2 SERPINE1 SH3GLB1 SLC39A1 SLC39A2 SLC39A3 SLC39A4 SMAD3 SMAD4 SPARC SQSTM1 SRC TGFB1 THBS1 TNFSF15 TP53 ULK1 UVRAG VTN
scRNAseq/Figure_5H_term2groups.txt 0 → 100755
View file @ 1f735baa
TERM,GROUP
FRIDMAN_SENESCENCE_UP,A
REACTOME_CELLULAR_SENESCENCE,A
REACTOME_SENESCENCE_ASSOCIATED_SECRETORY_PHENOTYPE_SASP,A
CHICAS_RB1_TARGETS_SENESCENT,A
WP_SENESCENCE_AND_AUTOPHAGY_IN_CANCER,A
DEMAGALHAES_AGING_UP,A
DEMAGALHAES_AGING_DN,A
BIOCARTA_P53_PATHWAY,B
INGA_TP53_TARGETS,B
HALLMARK_INFLAMMATORY_RESPONSE,C
REACTOME_INTERFERON_SIGNALING,C
INTERFERON_SIGNALING (REACTOME_INTERFERON_SIGNALLING),C
REACTOME_CYTOKINE_SIGNALING_IN_IMMUNE_SYSTEM,C
WNT_SIGNALING,D
REACTOME_SIGNALING_BY_WNT,D
KEGG_WNT_SIGNALING_PATHWAY,D
BIOCARTA_WNT_PATHWAY,D
PID_WNT_CANONICAL_PATHWAY,D
PID_WNT_SIGNALING_PATHWAY,D
PID WNT SIGNALING PATHWAY,D
GO_CANONICAL_WNT_SIGNALING,D
GO_CANONICAL_WNT_SIGNALING_PATHWAY,D
GSE26351_UNSTIM_VS_WNT_PATHWAY_STIM_HEMATOPOIETIC_PROGENITORS_UP,D
WNT_CELL_GROWTH_AND_PROLIFERATION,D
WNT_SIGNALING_PCR_ARRAY,D
ST_WNT_BETA_CATENIN_PATHWAY,D
WNT_STANFORD,D
WNT_UP.V1_DN,D
HALLMARK_NOTCH_SIGNALING,E
KEGG_NOTCH_SIGNALING_PATHWAY,E
GO_NOTCH_SIGNALING_PATHWAY,E
PID_NOTCH_PATHWAY,E
GO_REGULATION_OF_NOTCH_SIGNALING_PATHWAY,E
NOTCH_TARGETS_PCR_ARRAY,E
VILIMAS_NOTCH1_TARGETS_UP,E
NOTCH_DN.V1_UP,E
NOTCH_DN.V1_DN,E
GO_NEGATIVE_REGULATION_OF_NOTCH_SIGNALING_PATHWAY,E
NGUYEN_NOTCH1_TARGETS_DN,E
KEGG_ECM_RECEPTOR_INTERACTION,F
KEGG_CELL_ADHESION_MOLECULES_CAMS,F
CELL_ADHESION_MOLECULES,F
TSAI_RESPONSE_TO_RADIATION_THERAPY,G
CHIARETTI_T_ALL_REFRACTORY_TO_THERAPY,G
KAN_RESPONSE_TO_ARSENIC_TRIOXIDE,G
KIM_RESPONSE_TO_TSA_AND_DECITABINE_UP,G
CHIBA_RESPONSE_TO_TSA_UP,G
RHEIN_ALL_GLUCOCORTICOID_THERAPY_UP,G
BOROVIAK_DIAPAUSE_UP,H
BOROVIAK_DIAPAUSE_DN,H
REACTOME_REGULATION_OF_MITOTIC_CELL_CYCLE,H
\ No newline at end of file
scRNAseq/Figure_5K.R 0 → 100644
View file @ 1f735baa
# Volcano plot T vs C cluster 1 focus
library(Seurat)
library(ggplot2)
library(ggrepel)
library(RColorBrewer)
library(dplyr)
library(scales)
TvsC_markers <- readRDS(file = "Figure_5K_data.rds")
markers <- TvsC_markers$Cluster_1
markers$p_val_adj[which(markers$p_val_adj == 0)] <- 1e-300
adj.pval.thr=0.05
logfc.thr = 0.15
# Volcano plot
data <- data.frame(gene = row.names(markers), pval = -log10(markers$p_val_adj), lfc = markers$avg_logFC)
data <- na.omit(data)
data <- mutate(data, color = case_when(data$lfc > logfc.thr & data$pval > -log10(adj.pval.thr) ~ "Overexpressed",
data$lfc < -logfc.thr & data$pval > -log10(adj.pval.thr) ~ "Underexpressed",
abs(data$lfc) < logfc.thr | data$pval < -log10(adj.pval.thr) ~ "NonSignificant"))
data <- data[order(data$pval, decreasing = TRUE),]
data <- data[order(data$lfc, decreasing = TRUE),]
highl <- head(subset(data, color != "NonSignificant"), 12)
vol <- ggplot(data, aes(x = lfc, y = pval, colour = color)) +
theme_bw(base_size = 12) +
theme(legend.position = "right") +
ylim(c(0,350)) +
xlab("log2 Fold Change") +
ylab(expression(-log[10]("adjusted p-value"))) +
geom_hline(yintercept = -log10(adj.pval.thr), colour = "darkgray")
tn = function() trans_new('cuberoot', transform = function(x) x^(1/3),
inverse = function(x) x^3)
vol <- vol +
geom_point(size = 2, alpha = 0.8, na.rm = T) +
scale_color_manual(name = "Expression",
values = c(Overexpressed = "#ae0000",
Underexpressed = "#095786",
NonSignificant = "darkgray")) +
geom_text_repel(data = highl, mapping = aes(label = gene), size = 5, segment.alpha = 0.4,force = 50,show.legend = FALSE, max.overlaps = 30) +
theme(panel.grid.major = element_blank(),
axis.text = element_text(size = 12),
panel.grid.minor = element_blank())
plot(vol)
for (i in 1:20) {
png(filename = paste0("Plot_n_",i, ".png"), width = 9, height = 6, units = "in", res = 200)
print(plot(vol))
dev.off()
}
png("Figure_5K.png", units = "in", res = 700, height = 4.5, width = 9)
plot(vol)
dev.off()
scRNAseq/Figure_5M.R 0 → 100644
View file @ 1f735baa
library(Seurat)
library(ggplot2)
library(dittoSeq)
library(dplyr)
library(RColorBrewer)
library(stats)
library(openxlsx)
library(psych)
PDX <- readRDS("PDX_object.rds")
# Analysis of PDX LSC subsets in T vs C
table(PDX$RNA_snn_h.RNA_DonorID_res.1.2, PDX$RNA_Condition)
PDX <- SetIdent(PDX, value = "RNA_snn_h.RNA_DonorID_res.1.2")
cluster6cells = WhichCells(object = PDX, idents = "6")
cluster5cells = WhichCells(object = PDX, idents = "5")
cluster19cells = WhichCells(object = PDX, idents = "19")
PDX <- SetIdent(PDX, value = "RNA_Condition")
T_cells = WhichCells(object = PDX, idents = "Treated")
C_cells = WhichCells(object = PDX, idents = "Control")
cluster6_T = intersect(cluster6cells, T_cells)
cluster6_C = intersect(cluster6cells, C_cells)
cluster5_C = intersect(cluster5cells, C_cells)
Cluster6_TvsC_markers = FindMarkers(object = PDX,
ident.1 = cluster6_T,
ident.2 = cluster6_C)
Cluster6C_vs_5C_markers = FindMarkers(object = PDX,
ident.1 = cluster6_C,
ident.2 = cluster5_C)
PDX = SetIdent(PDX, value = "RNA_snn_h.RNA_DonorID_res.1.2")
cluster5_vs6and19_markers = FindMarkers(object = PDX,
ident.1 = cluster5cells,
ident.2 = c(cluster6cells, cluster19cells))
cluster6_vs5and19_markers = FindMarkers(object = PDX,
ident.1 = cluster6cells,
ident.2 = c(cluster5cells, cluster19cells))
cluster19_vs5and6_markers = FindMarkers(object = PDX,
ident.1 = cluster19cells,
ident.2 = c(cluster5cells, cluster6cells))
# These xlsx are stored in Figure_5M_data.zip
openxlsx::write.xlsx(x = cluster5_vs6and19_markers, file = "res12_cluster5_vs6and19_markers.xlsx", row.names = T)
openxlsx::write.xlsx(x = cluster6_vs5and19_markers, file = "res12_cluster6_vs5and19_markers.xlsx", row.names = T)
openxlsx::write.xlsx(x = cluster19_vs5and6_markers, file = "res12_cluster19_vs5and6_markers.xlsx", row.names = T)
top10markers_eachcluster = c(rownames(cluster5_vs6and19_markers %>% slice_max(order_by = avg_logFC, n = 10)),
rownames(cluster6_vs5and19_markers %>% slice_max(order_by = avg_logFC, n = 10)),
rownames(cluster19_vs5and6_markers %>% slice_max(order_by = avg_logFC, n = 10)))
DotPlot(PDX, features = top15markers_eachcluster, idents = c("5","6","19"))
plotinfo = dittoSeq::dittoDotPlot(object = PDX, vars = top15markers_eachcluster,
group.by = "RNA_snn_h.RNA_DonorID_res.1.2",
cells.use = WhichCells(PDX, idents = c("5","6","19")),
ylab = "LSC subclusters", y.reorder = c(2,3,1),
main = "Top 15 markers by logFC per cluster", data.out = T)
svg(filename = "Figure_5M_left.png", width = 9.9, height = 3)
ggplot(data = plotinfo$data, aes(x = var, y = grouping)) +
geom_point(aes(color = color, size = size)) +
scale_color_gradient2(high = "#b31b2c", mid = "white", low = "#2268ad") +
theme_bw() + theme(panel.grid = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = rel(1.25)),
axis.text.y = element_text(size = rel(1.5))) +
xlab("Top 15 cluster markers by logFC") + ylab("LSC subclusters") +
labs(color = paste("Relative","expression",sep="\n"), size = paste("Percent","expression",sep="\n"))
dev.off()
plotinfo_reduced = dittoSeq::dittoDotPlot(object = PDX, vars = c("CDK6","INKA1","NECTIN2","PAK4","SIRT1"),
group.by = "RNA_snn_h.RNA_DonorID_res.1.2",
cells.use = WhichCells(PDX, idents = c("5","6","19")),
ylab = "LSC subclusters", y.reorder = c(2,3,1),
main = "Focus CDK6 INKA1", data.out = T)
svg(filename = "Figure_5M_right.svg", width = 3.5, height = 3)
ggplot(data = plotinfo_reduced$data, aes(x = var, y = grouping)) +
geom_point(aes(color = color, size = size)) +
scale_color_gradient2(high = "#b31b2c", mid = "white", low = "#2268ad") +
theme_bw() + theme(panel.grid = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = rel(1.25)),
axis.text.y = element_text(size = rel(1.5))) +
xlab("HSC latency markers") + ylab("LSC subclusters") +
labs(color = paste("Relative","expression",sep="\n"), size = paste("Percent","expression",sep="\n"))
dev.off()
scRNAseq/Figure_6At_6Bl_6Er_6H.R 0 → 100644
View file @ 1f735baa
library(Seurat)
library(ggplot2)
library(dplyr)
library(Hmisc)
# Import AML dataset
AML <- readRDS(file = "AML_object.rds")
AMLcells <- rownames(AML@meta.data)
# Import the new updated module scores
MS <- readRDS(file = "Figure_6A_data.rds")
# ##########################
# FeaturePlots of 126High and 126Low signature:
# ##########################
AML <- AddMetaData(AML, metadata = MS %>% filter(PatientID %nin% c("PT19","PT20")) %>% select(-PatientID) %>% select(-Timepoint))
FPdata <- FetchData(AML, vars = c("X126up","X126down", "UMAP_1", "UMAP_2", "Timepoint_corr", "PatientID"))
png(filename = "Figure_6A_top.png", width = 3, height = 3.3, units = "in", res = 1200)
ggplot(FPdata %>% filter(Timepoint_corr == "DX") %>% arrange(X126up))+
geom_point(aes(x = UMAP_1, y = UMAP_2, color = X126up), size =0.01) +
scale_color_viridis_c(option = "inferno", limits = c(-0.1,0.06)) +
guides(color = guide_colorbar(reverse = F)) + theme_void() +
labs(title = "126High signature") +
theme(legend.position = "top", legend.title = element_blank(), plot.title = element_text(hjust = 0.5),
legend.key.width=unit(0.33,"in"), legend.key.height = unit(0.2, "in"))
dev.off()
# ##########################
# ViolinPlots at diagnosis:
# ##########################
AML = SetIdent(AML, value = "Timepoint_corr")
AML$PatientID = factor(AML$PatientID, levels = c("PT01", "PT02", "PT13",
"PT09","PT10","PT08","PT15",
"PT06","PT07","PT12"))
svg(filename = "Figure_6B_left.svg",
width = 5, height = 3.3)
VlnPlot(AML, idents = "DX", features = "X126up",
group.by = "PatientID" , pt.size = 0,
cols = c("#c00000","#c00000","#c00000",
"#ed7d31","#ed7d31","#ed7d31","#ed7d31",
"#70ad47","#70ad47","#70ad47")) +
geom_hline(yintercept=0, linetype="dashed", color = "black", alpha = 0.5) +
theme_bw(base_size = 6) + ggtitle("126High Signature") +
theme(legend.text=element_text(size=rel(1.5)), legend.position = "none") +
theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title.x = element_blank(), axis.text = element_text(size = rel(1.5)),
panel.grid.major.x = element_blank(), panel.grid.minor.y = element_blank())
dev.off()
round(prop.table(table(AML$X126up>0,AML$PatientID,AML$Timepoint_corr)[,,"DX"], margin = 2)*100, digits = 2)
# PT01 PT02 PT13 PT09 PT10 PT08 PT15 PT06 PT07 PT12
# FALSE 56.10 59.67 67.44 78.45 77.86 93.29 94.38 95.84 99.86 99.22
# TRUE 43.90 40.33 32.56 21.55 22.14 6.71 5.62 4.16 0.14 0.78
# ##########################
# Vlnplots dx and rel comparison
# ##########################
# ALL diagnosis vs Relapses single patients
DiagnosisVLN <- MS %>% filter(Timepoint == "DX")
DiagnosisVLN$Facet <- "Diagnosis"
DiagnosisVLNsubset <- DiagnosisVLN %>% group_by(PatientID) %>% sample_n(1400) %>% ungroup()
RelapseVLN <- MS %>% filter(Timepoint == "REL")
RelapseVLN$Facet <- "Relapse"
RelapseVLNsubset <- RelapseVLN %>% group_by(PatientID) %>% sample_n(1200) %>% ungroup()
PT08VLN <- MS %>% filter(PatientID == "PT08" & Timepoint %in% c("DX", "REL", "REL_NR"))
PT08VLN$Facet <- "PT08"
PT15VLN <- MS %>% filter(PatientID == "PT15")
PT15VLN$Facet <- "PT15"
PT19VLN <- MS %>% filter(PatientID == "PT19" & Timepoint == "REL")
PT19VLN$Facet <- "PT19"
PT20VLN <- MS %>% filter(PatientID == "PT20" & Timepoint == "REL1")
PT20VLN$Facet <- "PT20"
PT20VLN$Timepoint <- "REL"
VLN <- rbind(RelapseVLNsubset, DiagnosisVLNsubset, PT08VLN, PT15VLN, PT19VLN, PT20VLN)
VLN$Facet <- factor(VLN$Facet, levels = c("Diagnosis", "Relapse", "PT08", "PT15", "PT19", "PT20"))
# Diagnosis WITHOUT REFR AML
VLNnoREFR <- VLN
VLNnoREFR <- VLNnoREFR %>% filter(!PatientID %in% c("PT01", "PT02", "PT13"))
VLNnoREFR_noRELpool <- VLNnoREFR
VLNnoREFR_noRELpool <- VLNnoREFR_noRELpool %>% filter(!Facet == "Relapse")
VLNnoREFR_noRELpool$Facet <- ifelse(VLNnoREFR_noRELpool$Facet == "Diagnosis", "non Ref. \n AML", paste0(VLNnoREFR_noRELpool$Facet))
svg(filename = "Figure_6G.svg", height = 3.3,
width = 4.4)
ggplot(VLNnoREFR_noRELpool) +
geom_violin(aes(x = Timepoint, y = `126up`, fill = Timepoint)) +
facet_grid(~Facet, scales = "free_x", space = "free_x") +
geom_hline(yintercept=0, linetype="dashed", color = "black", alpha = 0.5) +
scale_fill_manual(values = c(CR = "#70ad47", fut_REL = "#ed7d31", REFR = "#c00000",
DX = "#cfcfcf", REL = "#458fe8", REL_NR = "#19385c")) +
theme_bw(base_size = 6) + ggtitle("126High Signature") +
theme(legend.text=element_text(size=rel(1.5)), legend.position = "none") +
theme(plot.title = element_text(size = rel(2), hjust = 0.5), axis.title.y = element_blank(),
axis.title.x = element_blank(), axis.text = element_text(size = rel(1.5)), strip.text = element_text(size = rel(2)),
panel.grid.minor = element_blank(), panel.grid.major.x = element_blank(), strip.background = element_blank())
dev.off()
# ##########################
# Vlnplot LSC subclusters
# ##########################
AML_0_11_14_final <- readRDS("AML_subclustering.rds")
AML_0_11_14_final_cells <- rownames(AML_0_11_14_final@meta.data)
MS_cl_0_11_14 <- MS[AML_0_11_14_final_cells,]
MS_cl_0_11_14 <- MS_cl_0_11_14 %>% select(-Timepoint) %>% select(-PatientID)
AML_0_11_14_final <- AddMetaData(AML_0_11_14_final, metadata = MS_cl_0_11_14)
AML_0_11_14_final$Timepoint_corr <- factor(AML_0_11_14_final$Timepoint_corr, levels = c("DX","D14","D30","REL","REL_NR"))
# ##########################
# DotPlots
# ##########################
plotinfo_latency = dittoSeq::dittoDotPlot(object = AML_0_11_14_final, vars = c("X126up","CDK6","INKA1"),
group.by = "RNA_snn_h.PatientID_res.0.6",
ylab = "LSC subclusters",
data.out = T)
svg(filename = "Figure_6E_HSC_latency.png", width = 2.5, height = 4)
ggplot(data = plotinfo_latency$data, aes(x = var, y = grouping)) +
geom_point(aes(color = color, size = size)) +
scale_color_gradient2(high = "#b31b2c", mid = "white", low = "#2268ad") +
theme_bw() + theme(panel.grid = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = rel(1.25)),
axis.text.y = element_text(size = rel(1.5))) + scale_x_discrete(labels= c("126High","CDK6", "INKA1")) +
xlab("HSC latency") + ylab("LSC subclusters") +
labs(color = paste("Relative","expression",sep="\n"), size = paste("Percent","expression",sep="\n"))
dev.off()
# ##########################
# CellCycle Barplots
# ##########################
PT20_active <- readRDS("PT20_active.rds")
PT20cells <- rownames(PT20_active@meta.data)
PT19_active <- readRDS("PT19_active.rds")
PT19cells <- rownames(PT19_active@meta.data)
MS_pt19 <- MS[PT19cells,]
MS_pt19 <- MS_pt19 %>% select(-PatientID) %>% select(-Timepoint)
MS_pt20 <- MS[PT20cells,]
MS_pt20 <- MS_pt20 %>% select(-PatientID) %>% select(-Timepoint)
PT19_active <- AddMetaData(object = PT19_active, metadata = MS_pt19)
PT20_active <- AddMetaData(object = PT20_active, metadata = MS_pt20)
PT19data <- FetchData(object = PT19_active, vars = c("RNA_PatientID","RNA_Timepoint", "Phase", "X126up"))
PT19data$AML_up_full_cat <- ifelse(PT19data$X126up > 0, "POS", "NEG")
PT20data <- FetchData(object = PT20_active, vars = c("RNA_PatientID","RNA_Timepoint", "Phase", "X126up"))
PT20data$AML_up_full_cat <- ifelse(PT20data$X126up > 0, "POS", "NEG")
PT20data$RNA_Timepoint <- ifelse(PT20data$RNA_Timepoint == "REL1", "REL", paste0(PT20data$RNA_Timepoint))
AMLdata <- FetchData(object = AML, vars = c("PatientID","Timepoint_corr", "Phase", "X126up"))
AMLdata$AML_up_full_cat <- ifelse(AMLdata$X126up > 0, "POS", "NEG")
colnames(AMLdata) <- colnames(PT19data)
Data <- rbind(AMLdata, PT19data, PT20data)
Data_rel <- filter(Data, RNA_Timepoint == "REL")
Data_rel_summ <- Data_rel %>% group_by(RNA_PatientID, AML_up_full_cat, Phase) %>% summarise(n = n())
png(filename = "Figure_6H_bottom_left.png", units = "in", height = 3, width = 3, res = 300)
ggplot(Data_rel_summ) +
geom_col(aes(x= AML_up_full_cat, y = n, fill = Phase), position="fill", alpha = 0.8) +
scale_fill_manual(values = c("lightgrey","orange","red")) +
scale_y_continuous(labels = function(x) paste0(x*100, "%")) +
facet_wrap(~RNA_PatientID, nrow = 1) + theme_bw() +
ggtitle("Relapse AML") + xlab("126High Signature") +
theme(panel.grid.minor = element_blank(), panel.grid.major.x = element_blank(),
strip.background = element_blank(), axis.title.y = element_blank(),
plot.title = element_text(hjust = 0.5), legend.position = "bottom", axis.text.x = element_text())
dev.off()
Data_dx <- filter(Data, RNA_Timepoint == "DX")
Data_dx_summ <- Data_dx %>% group_by(RNA_PatientID, AML_up_full_cat, Phase) %>% summarise(n = n())
png(filename = "Figure_6H_top.png", units = "in", height = 3, width = 3.3*2.5, res = 300)
ggplot(Data_dx_summ) +
geom_col(aes(x= AML_up_full_cat, y = n, fill = Phase), position="fill") +
scale_fill_manual(values = c("lightgrey","orange","red")) +
scale_y_continuous(labels = function(x) paste0(x*100, "%")) +
facet_wrap(~RNA_PatientID, nrow = 1) + theme_bw() +
ggtitle("Diagnosis AML") + xlab("126High Signature") +
theme(panel.grid.minor = element_blank(), panel.grid.major.x = element_blank(),
strip.background = element_blank(), axis.title.y = element_blank(),
plot.title = element_text(hjust = 0.5), legend.position = "bottom")
dev.off()
Data_relnr <- filter(Data, RNA_Timepoint == "REL_NR")
Data_relnr_summ <- Data_relnr %>% group_by(RNA_PatientID, AML_up_full_cat, Phase) %>% summarise(n = n())
png(filename = "Figure_6H_bottom_right.png", units = "in", height = 3, width = 2.75, res = 300)
ggplot(Data_relnr_summ) +
geom_col(aes(x= AML_up_full_cat, y = n, fill = Phase), position="fill") +
scale_fill_manual(values = c("lightgrey","orange","red")) +
scale_y_continuous(labels = function(x) paste0(x*100, "%")) +
facet_wrap(~RNA_PatientID, nrow = 1) + theme_bw() +
ggtitle("Relapse NR AML") + xlab("126High Signature") +
theme(panel.grid.minor = element_blank(), panel.grid.major.x = element_blank(),
strip.background = element_blank(), axis.title.y = element_blank(),
plot.title = element_text(hjust = 0.5), legend.position = "bottom")
dev.off()
scRNAseq/Figure_S1F.R 0 → 100644
View file @ 1f735baa
library(Seurat)
library(ggplot2)
library(ComplexHeatmap)
library(dplyr)
library(dittoSeq)
AML <- readRDS("AML_object.rds")
markers_res06 <- AML@misc$clustermarkers$res0.6
AML$RNA_snn_res.0.6 <- factor(AML$RNA_snn_res.0.6, levels = c("0","11","14",
"3","1",
"9","7","13",
"2","10",
"5","6","4","8","12"))
markers_res06_up <- list()
for (x in levels(AML$RNA_snn_res.0.6)) {
markers_res06_up[[paste0("cl",x)]] <- markers_res06[[paste0("cl",x)]][["up"]]
}
fun <- function(x) {
x %>% slice_max(order_by = avg_logFC, n=20)
}
markers_res06_top20 <- lapply(markers_res06_up, FUN = fun)
genes_to_plot <- c()
tmp <- c()
for (x in levels(AML$RNA_snn_res.0.6)) {
tmp <- markers_res06_top20[[paste0("cl",x)]]$gene
genes_to_plot <- c(genes_to_plot, tmp)
}
genes_to_plot_unique <- unique(genes_to_plot)
genenames_to_plot <- c("CD52","FAM30A","IGHM","CD99","TNFRSF4","GUCY1A1","EGFL7","ADGRE5","GADD45A","IGFBP2","MDM2",
"BST2","IFIT3","HSPA1A","HSPA1B","RAB31","HLA-A","CD34","LTB","HLA-DRB1","HOPX","IRF1","RPL3",
"MSI2","RPL5","RPS2","RPS18","RPS3","MZB1","RPL10A","SELENOP","RPS5","EIF3E","MPO","ELANE","AZU1",
"CXCL8","SOX4","PRSS57","CAT","AREG","HBD","FCER1A","MS4A3","TPSAB1","GATA2","HBB","HBD","HBA1","HBA2","CA1",
"AHSP","BLVRB","KLF1","PRTN3","IL2RA","IGLL1","MZB1","MPO","PCLAF","TYMS","PCNA","TUBA1B","DUT","TUBB","HMGB2",
"TK1","HELLS","MCM7","MCM4","HMGB1","CDT1","CENPF","TOP2A","ASPM","MKI67","TUBB4B","CTSG","LYZ","CXCR4","HLA-DQA1",
"HLA-DQB1","LGALS2","HLA-DRA","HLA-DPB1","HLA-DPA1","HLA-DRB1","CD74","FCER1A","S100A9","S100A8","LYZ","S100A12",
"CD14","NCF1","GRN","S100A10","FCER1G","S100A6","S100A11","FCGR3A","MS4A7","FCER1G","S100A11","CD48",
"GNLY","NKG7","GZMB","GZMA","CD7")
genenames_to_plot <- unique(genenames_to_plot)
labelindexes <- match(genenames_to_plot,genes_to_plot_unique)
AML <- SetIdent(AML, value = "Timepoint_corr")
cellstoplot<- WhichCells(object = AML,idents = c("DX","D14","D30","REL","REL_NR"))
AML$TimepointID <- AML$Timepoint_corr
AML$TimepointID <- factor(AML$TimepointID, levels = c("DX","D14","D30","REL","REL_NR"))
PandY <- PurpleAndYellow(k = 50)
Set1 <- RColorBrewer::brewer.pal(9, "Set1")
Set2 <- RColorBrewer::brewer.pal(8, "Set2")
Set3 <- RColorBrewer::brewer.pal(12, "Set3")
values = c(Set1, Set2, Set3)[1:length(unique(AML$RNA_snn_res.0.6))]
valuesdonor = c(RColorBrewer::brewer.pal(12, "Paired"))[1:length(unique(AML$PatientID))]
valuestimepoint = c("#e5e5e5","#b2b2b2","#7f7f7f","#4c4c4c","#000000")
genenames_to_plot_reduced <- c("CD52","FAM30A","IGHM","CD99","TNFRSF4","GUCY1A1","EGFL7","ADGRE5","IGFBP2","MDM2",
"IFIT3","HSPA1A","HSPA1B","RAB31","HLA-A","CD34","LTB","HLA-DRB1","HOPX","IRF1","RPL3",
"MSI2","RPL5","RPS2","RPS18","MZB1","EIF3E","MPO","ELANE","AZU1",
"CXCL8","SOX4","PRSS57","CAT","AREG","HBD","FCER1A","GATA2","HBB","HBD","CA1",
"AHSP","BLVRB","KLF1","IL2RA","IGLL1","MZB1","MPO","PCNA","TUBA1B","TUBB","HMGB2",
"HELLS","MCM7","MCM4","HMGB1","CENPF","TOP2A","MKI67","TUBB4B","CTSG","LYZ","CXCR4","HLA-DQA1",
"HLA-DQB1","HLA-DRA","HLA-DPB1","CD74","FCER1A","S100A9","S100A8","LYZ",
"CD14","NCF1","GRN","FCER1G","S100A6","FCGR3A","MS4A7","FCER1G","CD48",
"GNLY","NKG7","GZMB","CD7")
genenames_to_plot_reduced <- unique(genenames_to_plot_reduced)
labelindexes_reduced <- match(genenames_to_plot_reduced,genes_to_plot_unique)
png(filename = "Figure_S1F.png", width = 16, height = 12, res = 700, units = "in")
dittoHeatmap(object = AML, genes = genes_to_plot_unique, complex = TRUE,
order.by = c("RNA_snn_res.0.6"), annot.by = c("RNA_snn_res.0.6","PatientID","TimepointID"),heatmap.colors = PandY,
use_raster = TRUE, cluster_cols = F, cluster_rows = F, scale = "none", slot = "scale.data", fontsize_row = 12,
breaks = seq(-2, 2, length.out = 51), show_rownames = F, cells.use = cellstoplot,
annot.colors = c(values, valuesdonor, valuestimepoint)) +
rowAnnotation(link = anno_mark(at = labelindexes_reduced,
labels = genenames_to_plot_reduced,
labels_gp = gpar(fontsize = 8), padding = unit(1, "mm")))
dev.off()
png(filename = "Figure_1B.png", width = 4, height = 4.4, res = 700, units = "in")
DimPlot(AML, group.by = "RNA_snn_res.0.6", label = T,
pt.size = 0.1, shuffle = T, cols = values, label.size = 5) + theme_void() +
ggtitle("NPM1mut AML blasts") + theme(plot.title = element_text(hjust = 0.5), legend.position = "none")
dev.off()
scRNAseq/Figure_S1_G.R 0 → 100644
View file @ 1f735baa
library(Seurat)
library(ggplot2)
library(ComplexHeatmap)
library(dplyr)
library(dittoSeq)
AML <- readRDS("../activeobj/Del7_active.rds")
markers_res06 <- AML@misc$markers_h$RNA_snn_h.orig.ident_res.0.6
markers_res06 <- subset.data.frame(x = markers_res06, subset = avg_logFC > 0)
markers_res06_l <- split(markers_res06, f = markers_res06$cluster)
names(markers_res06_l) <- paste0("cl",names(markers_res06_l))
pdf("Vlnplot_signature_AML_full_ordered.pdf")
VlnPlot(object = AML, features = "AML_up_full", sort = T, pt.size = 0, group.by = "RNA_snn_h.orig.ident_res.0.6")
dev.off()
AML$RNA_snn_res.0.6 <- factor(AML$RNA_snn_h.orig.ident_res.0.6, levels = c("10","1","0","5","9","3","4","6","2","8","7" ,"11"))
total_levels <- c("10","1","0","5","9","3","4","6","2","8","7" ,"11")
markers_res06_up <- list()
for (x in levels(AML$RNA_snn_res.0.6)) {
markers_res06_up[[paste0("cl",x)]] <- markers_res06_l[[paste0("cl",x)]]
}
for(nel in c(15,20,30,40)){
fun <- function(x) {
x %>% slice_max(order_by = avg_logFC, n = nel)
}
markers_res06_top <- lapply(markers_res06_up, FUN = fun)
genes_to_plot <- c()
tmp <- c()
for (x in total_levels) {
tmp <- markers_res06_top[[paste0("cl",x)]]$gene
genes_to_plot <- c(genes_to_plot, tmp)
}
genes_to_plot_unique <- unique(genes_to_plot)
genenames_to_plot <- c("NPDC1","HOPX","CD52","FAM30A","IGHM","PRDX1","CD99","NRIP1","SPINK2",
"SNHG12","HEMGN","CD34","IGHM","CD164","ANGPT1","S100A9","S100A8","LYZ",
"CD14","AREG","NEGR1","AGR2","CXCR4","FCER1A","CXCL8","GATA2","HBD","ID3",
"ID1","HES1","ID2","HBD","TUBA1B","TUBB","MKI67","TOP2A","CENPF","PCNA",
"MT-ND1","MT-ND2","MT-ATP6","MT-CYB","MPO","AZU1","LYZ","HLA-DPB1","HLA-DRB1",
"MS4A6A","HLA-DQA1","HLA-DRA","HLA-DQB1","HLA-DPA1","LTB","SPINK2",
"JUN","GNLY","GZMB","CD7","KLRD1","GZMK")
genenames_to_plot <- unique(genenames_to_plot)
labelindexes <- match(genenames_to_plot,genes_to_plot_unique)
AML <- SetIdent(AML, value = "RNA_Timepoint")
cellstoplot <- WhichCells(object = AML,idents = c("DX","D14","D30"))
AML$TimepointID <- AML$RNA_Timepoint
AML$TimepointID <- factor(AML$TimepointID, levels = c("DX","D14","D30"))
PandY <- PurpleAndYellow(k = 100)
Set1 <- RColorBrewer::brewer.pal(9, "Set1")
Set2 <- RColorBrewer::brewer.pal(8, "Set2")
Set3 <- RColorBrewer::brewer.pal(12, "Set3")
values = c(Set1, Set2, Set3)[1:length(total_levels)]
valuesdonor = c(RColorBrewer::brewer.pal(12, "Paired"))[1:length(unique(AML$RNA_PatientID))]
valuestimepoint = c("#e5e5e5","#b2b2b2","#7f7f7f")
AML$Timepoint <- AML$TimepointID
AML$Patient <- AML$RNA_PatientID
AML$Cluster <- AML$RNA_snn_res.0.6
png(filename = paste0("Top",nel,"markers_cl_res06_limitPandY.png"), width = 16, height = 12, res = 700, units = "in")
a <- dittoHeatmap(object = AML, genes = genes_to_plot_unique, complex = TRUE, border_color = NA, name = " ",
order.by = c("Cluster"), annot.by = c("Cluster","Patient","Timepoint"),heatmap.colors = PandY,
use_raster = TRUE, cluster_cols = F, cluster_rows = F, scale = "none", slot = "scale.data", fontsize_row = 12,
breaks = seq(-2, 2, length.out = 101), show_rownames = F, cells.use = cellstoplot,
annot.colors = c(values, valuesdonor, valuestimepoint)) +
rowAnnotation(link = anno_mark(at = labelindexes,
labels = genenames_to_plot,
labels_gp = gpar(fontsize = 7), padding = unit(1, "mm")))
draw(a, padding = unit(c(5, 5, 5, 5), "mm"))
dev.off()
}
scRNAseq/Figure_S4D.R 0 → 100644
View file @ 1f735baa
library(DESeq2)
library(openxlsx)
library(ggplot2)
library(reshape2)
library(clusterProfiler)
library(pheatmap)
library(RColorBrewer)
library(dplyr)
dge_obj_full <- readRDS("20210804_202201_DGE.rds")
counts_normalized <- DESeq2::counts(object = dge_obj_full$deseq$ds, normalized = T)
# Gene expression heatmap of genes
# open rds of clustering - fetch terms and retrieve genes
clustering_data <- readRDS(file = "Figure_S4D_data.rds")
# genes2plot
genes2plot <- c(clustering_data$genelist$`GO:0006911_phagocytosis, engulfment`,
clustering_data$genelist$`GO:0099024_plasma membrane invagination`,
clustering_data$genelist$`GO:0006958_complement activation, classical pathway`,
clustering_data$genelist$`GO:0006910_phagocytosis, recognition`,
clustering_data$genelist$`GO:0008037_cell recognition`,
clustering_data$genelist$`GO:0006959_humoral immune response`,
clustering_data$genelist$`GO:0042742_defense response to bacterium`,
clustering_data$genelist$`GO:0050853_B cell receptor signaling pathway`,
clustering_data$genelist$`GO:0042113_B cell activation`,
clustering_data$genelist$`GO:0002250_adaptive immune response`,
clustering_data$genelist$`GO:0002449_lymphocyte mediated immunity`,
clustering_data$genelist$`GO:0051249_regulation of lymphocyte activation`,
clustering_data$genelist$`GO:0032944_regulation of mononuclear cell proliferation`,
clustering_data$genelist$`GO:0070661_leukocyte proliferation`,
clustering_data$genelist$`GO:0046651_lymphocyte proliferation`,
clustering_data$genelist$`GO:0030098_lymphocyte differentiation`,
clustering_data$genelist$`GO:0045580_regulation of T cell differentiation`,
clustering_data$genelist$`GO:0050870_positive regulation of T cell activation`,
clustering_data$genelist$`GO:0007159_leukocyte cell-cell adhesion`,
clustering_data$genelist$`GO:1903039_positive regulation of leukocyte cell-cell adhesion`,
clustering_data$genelist$`GO:0002228_natural killer cell mediated immunity`,
clustering_data$genelist$`GO:0002715_regulation of natural killer cell mediated immunity`,
clustering_data$genelist$`GO:0001910_regulation of leukocyte mediated cytotoxicity`,
clustering_data$genelist$`GO:0001909_leukocyte mediated cytotoxicity`,
clustering_data$genelist$`GO:0031341_regulation of cell killing`)
genes2plot <- base::unique(genes2plot)
counts_rlog_corr_genes2plot <- dge_obj_full$deseq$rlog_corr[genes2plot,]
counts_rlog_corr_genes2plot_df <- as.data.frame(t(counts_rlog_corr_genes2plot))
counts_rlog_corr_genes2plot_df <- merge.data.frame(x =counts_rlog_corr_genes2plot_df, y = dge_obj_full$sample_info)
counts_rlog_corr_genes2plot_df_melt <- melt(data = counts_rlog_corr_genes2plot_df, id.vars = c("patient","condition"))
counts_rlog_corr_genes2plot_df_light <- counts_rlog_corr_genes2plot_df_melt %>%
group_by(variable,condition,patient) %>%
summarize(AvgExpression_ptcondition = mean(value))
counts_rlog_corr_genes2plot_df_light$combo <- paste(counts_rlog_corr_genes2plot_df_light$patient, counts_rlog_corr_genes2plot_df_light$condition, sep = "_")
counts_rlog_corr_genes2plot_df_light_casted <- dcast(data = counts_rlog_corr_genes2plot_df_light, formula = combo ~ variable, value.var = "AvgExpression_ptcondition")
Annotations_colors_4_gexp_heatmap <- readRDS("Figure_4CDE_S4DE_colors.rds")
Annotations_colors_4_gexp_heatmap_nomir = Annotations_colors_4_gexp_heatmap
Annotations_colors_4_gexp_heatmap_nomir$`miR-126 activity` = NULL
SampleInfo_annotation_heatmap_gexp <- readRDS("Figure_4CDE_S4DE_annots.rds")
SampleInfo_annotation_heatmap_gexp_nomir = SampleInfo_annotation_heatmap_gexp
SampleInfo_annotation_heatmap_gexp_nomir$`miR-126 activity` = NULL
SampleInfo_annotation_heatmap_gexp_nomir$condition = NULL
SampleInfo_annotation_heatmap_gexp_nomir$Patient = SampleInfo_annotation_heatmap_gexp_nomir$patient
SampleInfo_annotation_heatmap_gexp_nomir$patient = NULL
colorsZ2bis <- c(seq(-4,4,by=0.1))
my_palette <- c("#021830",colorRampPalette(rev(brewer.pal(n=11,name="RdBu")))(n = length(colorsZ2bis)-3),"#33000f")
png("Figure_S4D.png", height = 12, width = 12, res = 700, units = "in")
pheatmap(mat = counts_rlog_corr_genes2plot, cellwidth = 8, cellheight = 4,fontsize_col = 5,
clustering_distance_cols = "manhattan", annotation_col = SampleInfo_annotation_heatmap_gexp_nomir,
annotation_colors = Annotations_colors_4_gexp_heatmap_nomir,
border_color = FALSE,
color = my_palette, breaks = colorsZ2bis, scale = "row", fontsize_row = 4)
dev.off()
scRNAseq/Figure_S4E.R 0 → 100644
View file @ 1f735baa
# BulkRNAseq data analysis with PathfindR:
library(pathfindR)
library(pathfindR.data)
library(dplyr)
library(DESeq2)
# Load expression matrix:
rawdata <- read.table(file = "Full-gene-counts.txt") # GEO data
rawdata_m <- as.matrix(rawdata)
samples <- colnames(rawdata)
# Sample info
GFPhighsamples <- grep(".+H$", samples, value = T)
GFPlowsamples <- grep(".+L$", samples, value = T)
Treatedsamples <- c(grep(pattern = ".+TH$", samples, value = T), grep(pattern = ".+TL$", samples, value = T))
Controlsamples <- c(grep(pattern = ".+CH$", samples, value = T), grep(pattern = ".+CL$", samples, value = T))
HighOnly_TvsC <- read_excel("Figure_S4E_data.xlsx")
HighOnly_TvsC$Down_regulated <- NA
HighOnly_TvsC$Down_regulated <- as.character(HighOnly_TvsC$Down_regulated)
# load DGE analysis
DGE = readRDS("20210804_202201_DGE.rds")
normcounts = counts(DGE[["deseq"]][["ds"]], normalized=T)
score_matrix_HighOnly_TvsC <- score_terms(enrichment_table = HighOnly_TvsC,
exp_mat = normcounts[,GFPhighsamples],
cases = intersect(GFPhighsamples, Treatedsamples),
use_description = TRUE, # default FALSE
label_samples = FALSE, # default = TRUE
case_title = "Treated", # default = "Case"
control_title = "Control", # default = "Control"
low = "#f7797d", # default = "green"
mid = "#fffde4", # default = "black"
high = "#1f4037") # default = "red"
# annotation vectors/colors
Annotations_colors_4_gexp_heatmap <- readRDS("Figure_4CDE_S4DE_colors.rds")
Annotations_colors_4_gexp_heatmap_nomir = Annotations_colors_4_gexp_heatmap
Annotations_colors_4_gexp_heatmap_nomir$`miR-126 activity` = NULL
SampleInfo_annotation_heatmap_gexp <- readRDS("Figure_4CDE_S4DE_annots.rds")
SampleInfo_annotation_heatmap_gexp_nomir = SampleInfo_annotation_heatmap_gexp
SampleInfo_annotation_heatmap_gexp_nomir$`miR-126 activity` = NULL
SampleInfo_annotation_heatmap_gexp_nomir$condition = NULL
SampleInfo_annotation_heatmap_gexp_nomir$Patient = SampleInfo_annotation_heatmap_gexp_nomir$patient
SampleInfo_annotation_heatmap_gexp_nomir$patient = NULL
colorsZ2bis <- c(seq(-2,2,by=0.1))
my_palette <- c("#021830",colorRampPalette(rev(brewer.pal(n=11,name="RdBu")))(n = length(colorsZ2bis)-3),"#33000f")
png(filename = "Figure_S4E.png", width = 16, height = 9, res = 700, units = "in")
pheatmap::pheatmap(score_matrix_HighOnly_TvsC, scale = "none",
border_color = NA, fontsize_row = 8,
color = my_palette, breaks = colorsZ2bis,
show_rownames = T, annotation_names_col = T,
annotation_col = SampleInfo_annotation_heatmap_gexp_nomir,
annotation_colors = Annotations_colors_4_gexp_heatmap_nomir, legend = T,
cellwidth = 9, cellheight = 7,
cluster_rows = T, cutree_cols = 2, cutree_rows = 2)
dev.off()
callback = function(hc, score_matrix_HighOnly_TvsC){
sv = svd(t(score_matrix_HighOnly_TvsC))$v[,1]
dend = reorder(as.dendrogram(hc), wts = sv)
as.hclust(dend)
}
png(filename = "Figure_S4E_callback.png", width = 16, height = 9, res = 700, units = "in")
pheatmap::pheatmap(score_matrix_HighOnly_TvsC, scale = "none",
border_color = NA, fontsize_row = 8,
color = my_palette, breaks = colorsZ2bis,
show_rownames = T, annotation_names_col = T,
annotation_col = SampleInfo_annotation_heatmap_gexp_nomir,
annotation_colors = Annotations_colors_4_gexp_heatmap_nomir, legend = T,
cellwidth = 9, cellheight = 7,
cluster_rows = T, cutree_cols = 1, cutree_rows = 2, clustering_callback = callback)
dev.off()
scRNAseq/Figure_S5A_B_C_D.R 0 → 100644
View file @ 1f735baa
library(Seurat)
library(SingleR)
library(Seurat)
library(SingleR)
library(scuttle)
# read all raw matrices and create a merge object
# on this dataset add metadata and remove all cells that do not have the annotation field
# Perform log normalization
# Convert to singlecellexperiment
# Perform singleR annotation by using custom dataset from VanGalen
# Downaload all raw data from VanGalen (GSE116256)
sampleids <- gsub(x = grep(x = grep(x = unlist(strsplit(x = list.files(path = "GSE116256_RAW/"), split = "_")),
pattern = "AML.*dem\\.txt$", value = T, perl = T),
pattern = "nanopore", value = T, invert = T), pattern = ".dem.txt", replacement = "")
# populated annotation and counts
all.objects <- list()
for (sid in sampleids) {
all.objects[[sid]][["annot"]] <- read.delim(file = list.files(path = "GSE116256_RAW/", pattern = paste0(sid,".anno"), full.names = T))
all.objects[[sid]][["matrix"]] <- read.delim(file = list.files(path = "GSE116256_RAW/", pattern = paste0(sid,".dem"), full.names = T), header = T, row.names = 1)
}
dir.create("rds_from_raw/")
for(samples in sampleids){
colnames(all.objects[[samples]][["matrix"]]) <- gsub(pattern = "\\.", replacement = "-", x = colnames(all.objects[[samples]][["matrix"]]))
all.objects[[samples]][["annot"]]$Cell <- as.character(all.objects[[samples]][["annot"]]$Cell)
rownames(all.objects[[samples]][["annot"]]) <- all.objects[[samples]][["annot"]]$Cell
all.objects[[samples]][["rds"]] <- CreateSeuratObject(counts = all.objects[[samples]][["matrix"]], project = paste0("VG_",samples),
min.cells = 0, min.features = 0, meta.data = all.objects[[samples]][["annot"]] )
all.objects[[samples]][["rds"]][["percent.mt"]] <- PercentageFeatureSet(all.objects[[samples]][["rds"]], pattern = "^MT-")
saveRDS(object = all.objects[[samples]][["rds"]], file = paste0("rds_from_raw/", samples,".rds"))
}
rds_objects <- list()
for(samples in sampleids[-1]){
rds_objects[[samples]] <- all.objects[[samples]][["rds"]]
}
obj <- merge(x = all.objects[[sampleids[1]]][["rds"]], y = rds_objects, project = "FullVGalen")
############# Creating singleR reference dataset ################
# Convert seurat object to singlecellexperiment
obj.sce <- as.SingleCellExperiment(obj)
obj.sce <- logNormCounts(obj.sce)
obj <- readRDS(file = "PDX_full.rds") # PDF full seurat object
obj@misc[["VGalen_CellType"]] <- SingleR(test = obj@assays$RNA@data,
ref = obj.sce,
labels = obj.sce$CellType, de.method="wilcox")
obj@meta.data[["VGalen_CellType"]] <- obj@misc[["VGalen_CellType"]]$pruned.labels
obj@misc[["VGalen_PredictionRefined"]] <- SingleR(test = obj@assays$RNA@data,
ref = obj.sce,
labels = obj.sce$PredictionRefined, de.method="wilcox")
obj@meta.data[["VGalen_PredictionRefined"]] <- obj@misc[["VGalen_PredictionRefined"]]$pruned.labels
obj.sce$CellPred_CellType <- paste0(obj.sce$PredictionRefined,"_",obj.sce$CellType)
obj@misc[["CellPred_CellType"]] <- SingleR(test = obj@assays$RNA@data,
ref = obj.sce,
labels = obj.sce$CellPred_CellType, de.method="wilcox")
obj@meta.data[["CellPred_CellType"]] <- obj@misc[["CellPred_CellType"]]$pruned.labels
Figure5A_B_C_D_data <- FetchData(object = obj, vars(c("CellID","RNA_DonorID","UMAP_1","UMAP_2","UMAPh_1","UMAPh_2",
"SingleRrefined_BlueprintEncodeData_labels","VGalen_CellType","VGalen_PredictionRefined",
"RNA_snn_h.RNA_DonorID_res.1.2")))
Figure5A_B_C_D_data$CellID <- rownames(Figure5A_B_C_D_data)
colnames(Figure5A_B_C_D_data)[1] <- "Patient"
colnames(Figure5A_B_C_D_data)[6] <- "BPE_refined"
colnames(Figure5A_B_C_D_data)[9] <- "Cluster_res_1.2"
write.xlsx(x = Figure5A_B_C_D_data, file = "Figure_S5_A_B_C_D_data.xlsx")
scRNAseq/Figure_S6E.R 0 → 100644
View file @ 1f735baa
library(Seurat)
library(ggplot2)
library(ComplexHeatmap)
library(dplyr)
library(dittoSeq)
library(patchwork)
library(RColorBrewer)
# Load LSC subclustering object
obj <- readRDS("AML_LSC_subclustering.rds")
# Load markers from LSC subclustering object at resolution 0.6
markers <- readRDS("Figure_S6E_data.rds")
markers_split <- list()
for (x in unique(markers$cluster)) {
markers_split[[paste0("Cluster_",x)]] <- markers %>% filter(cluster == x)
write.table(markers_split[[paste0("Cluster_",x)]], file = paste0("Cluster_",x,"_markers.txt"),
sep = '\t', quote = FALSE)
}
fun <- function(x) {
x %>% slice_max(order_by = avg_logFC, n=10)
}
markers_split_up_top10 <- lapply(markers_split, FUN = fun)
genes_to_plot <- c()
tmp <- c()
for (x in names(markers_split_up_top10)) {
tmp <- markers_split_up_top10[[x]]$gene
genes_to_plot <- c(genes_to_plot, tmp)
}
genes_to_plot_unique <- unique(genes_to_plot)
PandY <- PurpleAndYellow(k = 50)
Set1 <- RColorBrewer::brewer.pal(9, "Set1")
Set2 <- RColorBrewer::brewer.pal(8, "Set2")
Set3 <- RColorBrewer::brewer.pal(12, "Set3")
values = c(Set1, Set2, Set3)[1:length(unique(obj$RNA_snn_h.PatientID_res.0.6))]
valuesdonor = c(RColorBrewer::brewer.pal(12, "Paired"))[1:length(unique(obj$PatientID))]
valuestimepoint = c("#e5e5e5","#b2b2b2","#7f7f7f","#4c4c4c","#000000")
obj$TimepointID <- obj$Timepoint_corr
png(filename = "Figure_S6E.png", width = 8, height = 8, res = 700, units = "in")
dittoHeatmap(object = obj, genes = genes_to_plot_unique, complex = TRUE,
order.by = "RNA_snn_h.PatientID_res.0.6", annot.by = c("RNA_snn_h.PatientID_res.0.6","PatientID", "TimepointID"), heatmap.colors = PandY,
use_raster = TRUE, cluster_cols = F, cluster_rows = F, scale = "none", slot = "scale.data", fontsize_row = 12,
breaks = seq(-2, 2, length.out = 51), show_rownames = T,
annot.colors = c(values, valuesdonor, valuestimepoint))
dev.off()
scRNAseq/Figure_S7ABC_a.R 0 → 100644
View file @ 1f735baa
NMF <- function(inbam, sampleid, statsfile, Chromium_10X_CB_indexes, tags.of.interest = c("CB","UB","GN","RE")){
# load libraries
require(Rsamtools)
require(plyr)
require(ggplot2)
# define interval for fetching reads
which <- IRangesList("5"=IRanges(171410538L, 171410543L))
# Extract interesting tags
param <- ScanBamParam(which=which,tag=tags.of.interest, what=scanBamWhat())
filteredbam <- scanBam(inbam, param=param)
# Defining NPM1 mutated patterns
NPM1_patterns <- c("^CTCTGTCTGGCAG","^TCTGTCTGGCAG", "^CTGTCTGGCAG",
"^TGTCTGGCAG", "^GTCTGGCAG", "TCTCTGTCTGGC",
"GATCTCTGTCTGG$","GATCTCTGTCTG$", "GATCTCTGTCT$", "GATCTCTGTC$")
WT_pattern <- "TCTCTGGCAG"
# Reads matching NPM1 patterns
matches_NPM1_patterns_index <- grep(x = filteredbam$`5:171410538-171410543`$seq, pattern = paste(NPM1_patterns,collapse="|"), perl = T)
#cat(paste("Sampleid","Variable", "Value", sep=","), file=paste0(sampleid,"_stats.txt"), append=FALSE, sep = "\n")
cat(paste(sampleid,"N_Reads_matching_NPM1_patterns", length(matches_NPM1_patterns_index), sep=","), file=statsfile, append=TRUE, sep = "\n")
# Reads not matchingNPM1 pattern
matches_NO_NPM1_patterns_index <- grep(x = filteredbam$`5:171410538-171410543`$seq,
pattern = paste(NPM1_patterns,collapse="|"), perl = T, invert = T)
cat(paste(sampleid, "N_Reads_NOT_matching_NPM1_patterns", length(matches_NO_NPM1_patterns_index), sep=","), file=statsfile, append=TRUE, sep = "\n")
# Create temporal data.frame to retrieve WT patterns on original dataset
WT_tmp_df <- cbind(as.data.frame(filteredbam$`5:171410538-171410543`$seq[matches_NO_NPM1_patterns_index]), matches_NO_NPM1_patterns_index)
base::colnames(WT_tmp_df) <- c("seq","index_no_NPM1_pattern_main_bam")
matches_WT_patterns_index <- WT_tmp_df[grep(x = WT_tmp_df$seq, pattern = "TCTCTGGCAG"),]$index_no_NPM1_pattern_main_bam
cat(paste(sampleid,"N_Reads_NOT_matching_NPM1_patterns_but_matching_WT_pattern", length(matches_WT_patterns_index), sep=","), file=statsfile, append=TRUE, sep = "\n")
# subsetting values according to index from matches
mydata <- list()
for(i in names(filteredbam$`5:171410538-171410543`$tag)){
if(length(matches_NPM1_patterns_index) > 0){
mydata[["MUT"]][[i]] <- filteredbam$`5:171410538-171410543`$tag[[i]][matches_NPM1_patterns_index]
}
if(length(matches_WT_patterns_index) > 0){
mydata[["WT"]][[i]] <- filteredbam$`5:171410538-171410543`$tag[[i]][matches_WT_patterns_index]
}
}
# for both mut and wt:
# 1. convert list of tags to data.frame
# 2. add sampled id
# 3. filtering out reads that have NULL CB
# 4. filtering out reads with BC not in the sample 4-indexes seed (non possibile con certi bams)
# 5. Discard UMI duplicates
# 6. Counting N° UMI (UB) belonging to each barcode
df <- list()
counting <- list()
flag <- NULL
for(k in c("MUT", "WT")){
if(k == "MUT" & length(matches_NPM1_patterns_index) == 0){
flag <- "MUT"
cat(paste0(sampleid,",","N_Reads_supporting_", k, ",", 0),
file=statsfile, append=TRUE, sep = "\n")
cat(paste0(sampleid,",","N_Reads_supporting_", k, "_CB_BC_filt", ",", 0),
file=statsfile, append=TRUE, sep = "\n")
cat(paste0(sampleid,",","N_UMI_supporting_", k, ",", 0),
file=statsfile, append=TRUE, sep = "\n")
cat(paste0(sampleid,",","N_CB_supporting_", k, ",", 0),
file=statsfile, append=TRUE, sep = "\n")
next}
if(k == "WT" & length(matches_WT_patterns_index) == 0){
flag <- "WT"
cat(paste0(sampleid,",","N_Reads_supporting_", k, ",", 0),
file=statsfile, append=TRUE, sep = "\n")
cat(paste0(sampleid,",","N_Reads_supporting_", k, "_CB_BC_filt", ",", 0),
file=statsfile, append=TRUE, sep = "\n")
cat(paste0(sampleid,",","N_UMI_supporting_", k, ",", 0),
file=statsfile, append=TRUE, sep = "\n")
cat(paste0(sampleid,",","N_CB_supporting_", k, ",", 0),
file=statsfile, append=TRUE, sep = "\n")
next
}
if(length(matches_NPM1_patterns_index) > 0 & length(matches_WT_patterns_index) > 0){
flag <- "WT_MUT"
}
# 1. convert list of tags to data.frame
df[[k]] <- do.call(cbind, lapply(mydata[[k]], as.data.frame))
base::colnames(df[[k]]) <- tags.of.interest
# 2. add sampled id
df[[k]]$SM <- sampleid
cat(paste0(sampleid,",","N_Reads_supporting_", k, ",", base::nrow(df[[k]])),
file=statsfile, append=TRUE, sep = "\n")
# 3. filtering out reads that have NULL CB
# 4. filtering out reads with BC not in the sample 4-indexes seed
# df[[k]] <- base::subset.data.frame(x = df[[k]], subset = !is.na(CB) & BC %in% Chromium_10X_CB_indexes)
df[[k]] <- base::subset.data.frame(x = df[[k]], subset = !is.na(CB))
cat(paste0(sampleid,",","N_Reads_supporting_", k, "_CB_BC_filt", ",", base::nrow(df[[k]])),
file=statsfile, append=TRUE, sep = "\n")
# 5. Discard UMI duplicates
df[[k]] <- base::unique(df[[k]][,c("CB","UB")])
cat(paste0(sampleid,",","N_UMI_supporting_", k, ",", base::nrow(df[[k]])),
file=statsfile, append=TRUE, sep = "\n")
# 6. Counting N° UMI (UB) belonging to each barcode
counting[[k]] <- plyr::count(df = df[[k]], "CB")
cat(paste0(sampleid,",","N_CB_supporting_", k, ",", base::nrow(counting[[k]])),
file=statsfile, append=TRUE, sep = "\n")
if(k == "MUT"){
base::colnames(counting[[k]]) <- c("CB","MUT")
}
if(k == "WT"){
base::colnames(counting[[k]]) <- c("CB","WT")
}
}
print(paste0("Sample: ",sampleid ,"My flag: ", flag))
if(flag == "MUT"){
total <- counting[["WT"]]
total$MUT <- 0
}
if(flag == "WT"){
total <- counting[["MUT"]]
total$WT <- 0
}
if(!flag %in% c("WT","MUT")){
total <- merge.data.frame(x = counting[["MUT"]], y = counting[["WT"]], by = 'CB', all = T)
}
cat(paste0(sampleid,",","N_CB_supporting_both_MUT_and_WT", ",", base::nrow(total)),
file=statsfile, append=TRUE, sep = "\n")
# converting NA to 0
total[is.na(total)] <- 0
# recoding Cellbarcodes in order to fit with Seurat metadata
total$CB <- gsub(x = gsub(x = total$CB, pattern = "^", replacement = paste0(sampleid,"_"), perl = T), pattern = "-1$", replacement = "", perl = T)
return(total)
}
scRNAseq/Figure_S7ABC_b.R 0 → 100644
View file @ 1f735baa
# Performing analysis on all samples
# loading function:
source(file = "NMF.R")
# write header stats.txt file
cat(paste("Sampleid","Variable", "Value", sep=","), file="NMF_full_stats.txt", append=FALSE, sep = "\n")
# read samplesheet file to match sampleid ---> 10X indexes
samplesheet <- read.csv(file = "SampleSheet_PT19.csv", stringsAsFactors = F)
# read 10x indexes
indexes_10X <- read.csv(file = "Chromium_10X_indexes.txt", header = F, stringsAsFactors = F)
# initialize list of results
npm1_metadata <- list()
for(sid in samplesheet$Sample){
current_SI <- subset.data.frame(x = samplesheet, subset = Sample == sid, select = 'Index')$Index
current_cb_seed <- as.character(subset.data.frame(x = indexes_10X, subset = V1 == current_SI, select = c("V2","V3","V4","V5")))
npm1_metadata[[sid]] <- NMF(inbam = paste0("/beegfs/scratch/ric.gentner/ric.gentner/scRNA_AML/M84toM97/results/PT19/00-NPM1_classification/input/",sid,"_possorted_genome_bam.bam"), sampleid = sid, Chromium_10X_CB_indexes = current_cb_seed, statsfile = "NMF_full_stats.txt")
}
# Merging all data frames in a single one:
NPM1_complete_data <- do.call(rbind, lapply(npm1_metadata, as.data.frame))
base::rownames(NPM1_complete_data) <- NPM1_complete_data$CB
NPM1_complete_data$Classification <- ifelse(NPM1_complete_data$MUT > 0, "MUT", ifelse(NPM1_complete_data$WT > 5, "WT","ND"))
write.table(x = NPM1_complete_data, file = "NPM1_PT19_data.txt", quote = F, row.names = F)
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