correlationAnalyzeR
is the R interface to the Correlation AnalyzeR database and web application. The web version can be accessed here.
This package is designed to allow greater customization and control over the functions in the web interface. This vignette will demonstrate each function using an example. Additional info can be found in the reference manual.
To speed up the analysis, it is useful to generate a TERM2GENE
object ahead of time. The GSEA_Type
argument specifies which gene set databases to pull annotations from. See the details of ?getTERM2GENE
to see the different options.
library(correlationAnalyzeR)
<- getTERM2GENE(GSEA_Type = c("GO:BP")) # GO Biological Process TERM2GENE
correlationAnalyzeR
can be used to predict gene function using analyzeSingleGenes()
(the equivalent of Single Gene Mode in the web application). In this example, Tissue
and Sample_Type
arguments were set in order to limit the analysis to co-expression correlations in normal brain samples.
<- analyzeSingleGenes(genesOfInterest = c("BRCA1"),
res Tissue = "brain", Sample_Type = "normal",
TERM2GENE = TERM2GENE)
##
## Retrieving any missing correlation data...
##
## Analyzing: BRCA1
## GSEA
##
## Returning ... EGMT GSEA_up GSEA_down eres
This runs most of the core tasks for predicting gene functionality using this analysis mode. This includes running “corGSEA”, an implementation of GSEA developed in this package for use on genome-wide co-expression correlations.
The results are a list containing several items:
head(res$correlations)
## BRCA1
## A1BG -0.1510
## A1CF 0.0763
## A2M -0.2170
## A2ML1 -0.2330
## A2MP1 -0.1740
## A3GALT2 0.1160
head(res$`P values`)
## BRCA1
## A1BG 5.738861e-05
## A1CF 4.504482e-04
## A2M 1.866402e-05
## A2ML1 1.491089e-05
## A2MP1 3.711901e-05
## A3GALT2 1.279990e-04
head(res$`BRCA1, Brain - Normal`$GSEA$eres)
## ID
## GO_SKELETAL_SYSTEM_DEVELOPMENT GO_SKELETAL_SYSTEM_DEVELOPMENT
## GO_MRNA_PROCESSING GO_MRNA_PROCESSING
## GO_COVALENT_CHROMATIN_MODIFICATION GO_COVALENT_CHROMATIN_MODIFICATION
## GO_PROTEASOMAL_PROTEIN_CATABOLIC_PROCESS GO_PROTEASOMAL_PROTEIN_CATABOLIC_PROCESS
## GO_GLAND_DEVELOPMENT GO_GLAND_DEVELOPMENT
## GO_ORGANELLE_FISSION GO_ORGANELLE_FISSION
## Description
## GO_SKELETAL_SYSTEM_DEVELOPMENT GO_SKELETAL_SYSTEM_DEVELOPMENT
## GO_MRNA_PROCESSING GO_MRNA_PROCESSING
## GO_COVALENT_CHROMATIN_MODIFICATION GO_COVALENT_CHROMATIN_MODIFICATION
## GO_PROTEASOMAL_PROTEIN_CATABOLIC_PROCESS GO_PROTEASOMAL_PROTEIN_CATABOLIC_PROCESS
## GO_GLAND_DEVELOPMENT GO_GLAND_DEVELOPMENT
## GO_ORGANELLE_FISSION GO_ORGANELLE_FISSION
## setSize enrichmentScore NES
## GO_SKELETAL_SYSTEM_DEVELOPMENT 497 0.4124234 1.858152
## GO_MRNA_PROCESSING 491 0.5529121 2.488549
## GO_COVALENT_CHROMATIN_MODIFICATION 459 0.5048403 2.266618
## GO_PROTEASOMAL_PROTEIN_CATABOLIC_PROCESS 466 0.3708896 1.665522
## GO_GLAND_DEVELOPMENT 440 0.3842980 1.720649
## GO_ORGANELLE_FISSION 440 0.5901463 2.642311
## pvalue p.adjust
## GO_SKELETAL_SYSTEM_DEVELOPMENT 0.0005291005 0.005065758
## GO_MRNA_PROCESSING 0.0005291005 0.005065758
## GO_COVALENT_CHROMATIN_MODIFICATION 0.0005324814 0.005065758
## GO_PROTEASOMAL_PROTEIN_CATABOLIC_PROCESS 0.0005336179 0.005065758
## GO_GLAND_DEVELOPMENT 0.0005336179 0.005065758
## GO_ORGANELLE_FISSION 0.0005336179 0.005065758
## core_enrichment
## GO_SKELETAL_SYSTEM_DEVELOPMENT TYMS/ANO6/MED1/ATP7A/TP53/FBN2/CREB3L2/USP1/BBX/RYK/SMAD5/EVC/HMGA2/LOXL2/SP3/BMPR1A/CHSY1/AKAP13/SHOX2/ADAMTS12/TMEM38B/P3H1/POC1A/TGFBR1/NKX3-2/SLC35D1/NIPBL/GLI2/NOTCH2/HIF1A/SGPL1/MTHFD1/IRX5/OSR2/SLC10A7/FBN1/GLI3/MMP2/MBTD1/FAT4/CHD7/LRP6/PITX2/ASXL1/ANXA2/PITX1/HOXD9/MKS1/EPHA2/SETD2/EIF4A3/HOXD13/MED12/HOXD8/RDH10/EXT1/FST/TULP3/HAS2/SERPINH1/EIF2AK3/TEAD4/ADAMTS7/COL1A1/MEX3C/SH3PXD2B/HOXA11/SLC39A1/MCPH1/LRP5/LRRC17/VCAN/BMP1/HOXD10/AMER1/COL2A1/PSEN1/SIX4/TIPARP/SIX1/GSC/TRIP11/RUNX1/HOXC6/PDGFC/TGFBI/SIX2/TFAP2A/EP300/MMP14/COL3A1/ZMPSTE24/NEUROG1/ALX1/TCOF1/MTHFD1L/INPPL1/NPR3/SMAD1/SNAI2/HOXC11/HOXA1/HOXD4/HOXA2/VDR/CD44/STC1/HOXC4/HOXC8/HOXB3/LOX/HOXC10/SOX4/GLG1/PPIB/SH2B3/HOXD3/HOXA3/COL1A2/HOXC9/WNT5A/HOXB2/LUM/THBS3/TRIM45/WASF2/HOXA13/HSD17B7/NAB1/ROR2/LRP5L/SMAD2/EN1/MDFI/HOXB4/HYAL2/CTNNB1/TRPS1/WNT5B/TGFB1/SNAI1/PTGER4/MDK/TWIST1/DYM/HOXA10/NDST1/RAB23/GDF5/HOXD12/SLC39A14/GUSBP3/HOXB7/HOXC5/FLVCR1/MAPK14/FOXC2/SOX11/ALX3/RPS6KA3/HAND2/HOXA4/ZEB1/MEIS1/SLC38A10/FGF2/TGFBR2/RHOA/HSPB11/IFT80/FGFR1/PAX5/HEXB/MBTPS2/SNX19/VKORC1/ALX4/ACVR2B/HAND1/MMP13/FGFRL1/PRPSAP2/CLDN18/SLC2A10/GDF11/RELA/HOXA9/INSIG1/PLS3/GDF6/HOXB9/TIMP1/DLG1/HOXB6/RAB33B/SPARC/COL5A2/TRPV4/HOXA6/HOXB5/SFRP1/TBX15/SERP1/PAPSS2/SUFU/HOXA5/SMAD3
## GO_MRNA_PROCESSING TTF2/BARD1/REST/NUP98/CPSF2/SCAF11/CMTR2/MBNL3/DHX8/CCNB1/METTL16/PRPF40A/GEMIN5/DBR1/LSM8/XRN2/TGS1/PDCD11/SF3B3/NCBP1/ZCCHC8/THOC2/CPSF3/DHX15/HNRNPF/CCNT1/SRSF1/SNRNP40/CDK12/SYNCRIP/TRA2B/IWS1/RPRD1B/GTF2H3/DHX35/RBM7/CDC73/NCBP3/AQR/MAGOHB/RBM15/SNRNP200/ZC3H10/HNRNPU/RRP1B/CSTF1/PTBP1/DHX9/DDX46/PRPF4B/SF3A3/PRPF38A/SRSF10/THOC5/YBX1/DDX20/RBMX/GEMIN4/PABPC1/PNPT1/SNRNP48/HNRNPR/ECD/PRPF3/PTBP3/SON/CSTF2/GTF2H2/ZNF473/PAPOLA/POLR2D/RBM28/CCAR1/ALYREF/PRPF4/EIF4A3/FXR1/U2SURP/CTR9/DDX23/SF3B1/SART3/CRNKL1/ISY1/CLP1/BUD13/PHF5A/USP39/ERN1/RBM27/METTL14/ZC3H11A/CWC22/HNRNPH1/NCL/SLTM/PCF11/SNW1/EFTUD2/WDR33/DDX39A/NONO/CDK13/CSTF2T/KDM1A/DHX16/HNRNPUL1/LARP7/METTL3/SCAF8/SNRPD1/RBM8A/GCFC2/SFPQ/HNRNPA1/RBM39/CDC5L/THRAP3/RBM41/RNGTT/SREK1/DDX47/RNF20/DCPS/PRPF8/SRSF6/PABPC1L/SPEN/HMX2/SNIP1/SETX/DHX36/CWF19L1/WDR77/COIL/HNRNPC/ZC3H14/LSM5/GEMIN2/RBM3/AAR2/U2AF1L5/DHX38/SMU1/FIP1L1/SRPK1/POLR2B/PPP4R2/HNRNPM/PDE12/CTNNBL1/RAVER1/ACIN1/MTPAP/PNN/RBM15B/SMNDC1/TSEN15/PPWD1/SRSF3/HNRNPK/KHDRBS1/SNRPG/SNRPD3/SREK1IP1/GEMIN6/GTF2F2/HNRNPL/SUPT6H/RBMX2/MFAP1/CPSF6/PRMT5/SRSF2/TIA1/TSEN34/PRPF18/SNRPA/SNRPB/SCAF4/MAGOH/PLRG1/ZNF326/RALY/THOC6/WTAP/RBM19/YTHDC1/CWF19L2/RBMXL1/HNRNPLL/RBBP6/CWC27/SNRPE/SRSF9/LEO1/TFIP11/SRSF7/ELAVL1/PPP1R8/PAN2/THOC1/U2AF2/DDX5/GPATCH1/ESRP2/DDX17/CLNS1A/DAZAP1/LSM6/C1QBP/RNF40/PAN3/POLDIP3/SNRPB2/SRRM1/PAPOLG/RBM38/APOBEC2/HNRNPA2B1/PRMT9/PPIH/TBRG4/ZFP36L1/CWC25/PPIL3/SRRT/HNRNPA3/NUDT21/NCBP2/TSEN2/FASTKD5/MBNL1/SRSF4/TCERG1/DDX39B/PHRF1/PRPF6/QKI/SF3B6/KHSRP/KIN/SF3A1/HNRNPD/DDX42/FRG1/RBM22/NSRP1/SYF2/ADAR/RBM17/NELFE/IK/PRPF38B/SF3B2/SARNP/CDK7/SNRPF/PTCD2/RBM5/TUT1/MNAT1/U2AF1/SART1/ZNF830/CHTOP/TSEN54/USP49/AKAP17A/GTF2H1/RBM10/SAFB/ZC3H13/SMN1/LUC7L2/SRSF11/LSM3/CELF1/JMJD6/RSRC1/SF3A2/THOC3/ERCC2/POLR2C/PPIL1/RBM4/CSTF3/RBM26/CASC3/PRPF31/SFSWAP/SNRPC/TARDBP/LSM2/PRDX6
## GO_COVALENT_CHROMATIN_MODIFICATION BRCA2/CHEK1/UHRF1/AURKB/AURKA/REST/BAZ1A/CDK1/HELLS/CCNA2/ACTL6A/HAT1/CDK2/EZH2/TP53/DNMT1/CCNB1/CHD1/HMGA2/HDAC1/JADE3/SUV39H2/BAZ1B/SIN3A/KDM5A/WDR5B/DCAF1/EHMT1/RUVBL1/ZZZ3/RCOR1/PCGF6/BCOR/SUZ12/IWS1/NCOA3/NIPBL/CDC73/VRK1/BRD7/PADI3/EYA4/PHF8/PRKAA1/SETDB1/KDM3B/EYA3/KAT6A/INCENP/DNMT3B/TRIM16/KDM6A/ASXL1/ELK4/TRIP12/TRRAP/ATM/TAF5L/SIRT1/ATF7IP/DDB2/KDM3A/MYB/SETD2/TET2/MTA2/PHF19/CTR9/WDR70/SMAD4/TAF1/USP3/BEND3/SART3/LMNA/PAXBP1/RIF1/DOT1L/ZNF274/GATA3/HLCS/UIMC1/CTCF/PAXIP1/KDM4A/TAF5/NAA50/UFL1/CPA4/SNW1/MSL2/SMARCAD1/TBL1X/BAZ2A/UBR5/KDM1A/TET3/BRPF1/DR1/SFPQ/GTF3C4/YEATS2/MCM3AP/LIF/RNF168/TET1/MAP3K7/RNF20/SET/DTX3L/PRKD2/EP300/HDAC2/BRMS1/MIER1/SETD5/ZMPSTE24/KANSL2/RLF/EED/PHF20/CBX8/USP16/GFI1/MTF2/NSD1/SNAI2/PRDM13/MRGBP/ZNF451/NAA40/KDM5B/PRMT6/AKAP8/POLE3/ARID4B/CUL4B/KMT5B/VEGFA/KANSL3/KDM1B/ATXN7/SUPT6H/SUDS3/FBL/PRMT5/PARG/EPC1/JADE1/CTNNB1/PADI1/DDB1/SUV39H1/RBBP5/KDM2B/KMT5A/TAF12/ING3/LEO1/TWIST1/ISL1/KANSL1/KDM5C/PHF20L1/PHF14/YEATS4/HDAC3/PRDM5/HUWE1/JMJD1C/SRCAP/RSF1/SMARCB1/RNF40/MORC2/SETD7/MSL3P1/KAT6B/MUC1/PHF2/PRMT1/RNF2/TRIM28/HDAC7/HCFC1/MECOM/BRCC3/KMT2C/UBE2E1/PHB/LDB1/LEF1/PWP1/N6AMT1/PAX5/EPC2/BRD1/KDM2A/RBM14/KMT2E/NELFE/SETMAR/UBR2/KDM4D/PPHLN1/KMT5C/JAK2/PHF10/GATA2/ZNF304/JARID2/MBD2/RAG1/CHTOP/USP49/BRD8/MTA1/KDM4B/NOC2L/USP15/USP36/ING2/JMJD6/KDM7A
## GO_PROTEASOMAL_PROTEIN_CATABOLIC_PROCESS KIF14/AURKB/AURKA/BUB1B/CDC20/FBXO5/UBE2C/PBK/CDK1/SKP2/UGGT1/SENP1/PLK1/UBXN2A/SMARCC1/CDK2/USP13/CCNB1/FBXW8/MAD2L1/PTTG1/RHBDD1/STYX/MDM2/ERLIN1/PMAIP1/CCNF/PSME4/ANAPC1/DET1/CUL4A/XPO1/SMURF2/ERCC8/CD2AP/ERLIN2/SCO1/TOPORS/SIRT1/EDEM1/FBXO38/SOCS4/BFAR/TAF1/FBXL4/SOCS6/STT3B/UBXN4/TLK2/HFE/ITCH/HSP90B1/WWTR1/KLHL20/RNF216/TMF1/UFL1/FBXO22/CDC23/TBL1X/DDI2/PSEN1/BUB3/NFE2L2/RNFT1/EDEM2/ANKIB1/DNAJC10/PLK3/PSMD9/SPOPL/DERL2/LAMP3/TMEM67/BIRC2/KLHL15/LTN1/UBE2S/KCTD5/CDC27/ARIH2/FAF2/RACK1/NPLOC4/YOD1/CUL4B/UGGT2/ARAF/PELI1/TRIM25/UBE4B/UBE2J1/NUB1/SMURF1/CTNNB1/PLAA/RNF122/UBR1/DDB1/UBE2G1/HSPA5/RNF121/FBXL13/FBXL12/SPSB4/PSMA3/VCP/RNF139/HUWE1/PSMA2/PSMD6/RFFL/RNF40/TRIM39/HM13/RAD23B/SPSB2/RNF4/UBE2G2/ANAPC7/SPSB1/RPL11/AREL1/BAG2/UBE2E1/CAV1/NUDT15/PSMA4/GID8/N4BP1/CDC26/PSMA7/SIAH1/EDEM3/RNF217/TMTC3/NHLRC3/PSMD5/FBXO4/PSME1/USP19/UBR2/PSME3/NEDD4/PSMD2/POMT2/PSMD14/CCDC47/DERL1/TRIB3/AKT1/DAB2/KCTD10/TOR1A/PSMB1/PSMD11/CDC20B/PSMD1/MTA1/TRIM38/HERC2/SDF2L1/CSNK1A1/GCLC/JKAMP/PSME2/RNF34/OS9/ANAPC16/MTM1/PSMC2/ANAPC4/PSMD7/CUL1/CUL5/UBE2D1/FBXL7/SEL1L/SEC61B/UBE2D3/USP7/CUL3
## GO_GLAND_DEVELOPMENT BRCA2/CCNB2/AURKA/E2F8/TYMS/IQGAP3/E2F7/UMPS/IGF2R/MED1/ATP7A/SMARCC1/EZH2/PRKDC/HK2/BCL2L11/CPS1/SP3/BMPR1A/WDR35/TWSG1/BTBD7/TGFBR1/RREB1/CAD/ZBTB1/GLI2/TNC/NOTCH2/DHODH/HIF1A/PPAT/GLI3/PCNA/MSN/LAMA1/LRP6/PITX2/ASXL1/CCND1/NPHP3/PNPT1/ATM/PITX1/HOXD9/EPHA2/HOXD13/SMAD4/HFE/GATA3/RAF1/FOXA1/LRP5/NOTCH1/STRA6/MPST/ITGA2/PKD2/GATA6/GNPNAT1/ORAI1/PSEN1/SIX4/CREB1/RBPJ/SIX1/FOXE1/FADD/WNT3A/MAN2A1/PCK2/XBP1/ZMPSTE24/ABL1/LATS1/WDR77/UPF2/WLS/AR/SNAI2/TNFAIP3/VDR/PLAG1/HOXB3/ETS1/NKX2-5/KDM5B/NRP1/ANXA1/HOXD3/HOXA3/VEGFA/ADA/FRS2/SRP54/WNT5A/FOXB1/LRP5L/PRMT5/SMAD2/ROBO1/ARF6/BAX/CTNNB1/DAG1/TGFB1/MAD1L1/PCSK9/AKT2/MDK/FOXN1/ISL1/SEMA3A/PHB2/SOX3/CEBPG/HLX/PRKCSH/HAND2/ESRP2/OTP/XDH/FPGS/HESX1/CSNK2A2/PHF2/RPS6KA1/TGFBR2/SMO/CYP19A1/CAV1/EGFR/HMOX1/SEMA3C/DDX39B/RPL30/FGFR1/LEF1/NKX2-1/HNRNPD/PIK3CA/NKX2-3/RPGRIP1L/JAK2/RELA/HES1/HOXA9/PTCD2/STAT5B/SEC63/GATA2/TSPO/AKT1/HOXB9/JARID2/SERPINB5/RAG1/TBX19/SLC6A3/HMGCL/RPL10/SFRP1/CRKL/HOXA5/SCRIB/SMAD3
## GO_ORGANELLE_FISSION SMC4/NDC80/KIF18A/ESPL1/SGO1/BRCA2/KIF14/KIF11/BUB1/KNTC1/BIRC5/CHEK1/KIF18B/SGO2/AURKB/KIF23/NUSAP1/BRIP1/CENPE/DLGAP5/CHEK2/FANCD2/NUF2/KIF2C/KIF4A/AURKA/CENPF/KIFC1/CENPK/NCAPH/NCAPD2/BUB1B/MKI67/MYBL2/ASPM/MND1/KIF20B/RAD51/CDC20/FBXO5/CDCA8/TTK/UBE2C/AP3B1/EME1/CDC25C/CCNE2/TOP2A/NCAPG2/MTFR2/NDE1/NEK2/NCAPG/MYBL1/SPDL1/FBXO43/NCAPD3/TPX2/PLK1/RAD51B/CDT1/TACC3/BORA/RACGAP1/RAD54L/MASTL/ZWINT/MTFR1/CDC6/GEN1/TRIP13/TPR/MSH5/CDK5RAP2/NME6/NDC1/CCNB1/SMC1A/SMC2/MAD2L1/STAG1/SMC3/RCC1/PRC1/DSCC1/WAPL/FANCA/DSN1/PDS5A/PTTG1/BCL2L11/MLH1/SMC5/MYO19/PSMC3IP/MTBP/CEP85/ZW10/MIS12/SPICE1/SLF2/SPAG5/ANAPC1/NIPBL/CKS2/KPNB1/KNSTRN/RMI1/FIGNL1/ERCC4/CEP192/DHODH/FLNA/CDCA5/RB1/HNRNPU/FANCM/INO80/RAD54B/ZNF207/KIF22/INCENP/PKMYT1/TEX19/ATM/SPHK1/CDC14A/ANLN/RMDN1/P3H4/STAG2/PIBF1/NSMCE2/CHAMP1/LRP5/NAA50/RIOK2/PHIP/CDC23/MSH3/CDK13/XRCC3/EPS8/CEP97/BUB3/NSL1/DNM2/ACTR3/LIF/CALR/TEX15/NUP62/RAD1/NEUROG1/RAD21/USP16/KIF2A/DMC1/MAD2L2/UBE2S/ARHGEF10/CDC27/AKAP8/CCNE1/WNT5A/EPGN/CENPC/PRMT5/CTDP1/DIS3L2/MTFP1/MAD1L1/TOP2B/CHTF8/BOD1/MIEF1/RRS1/BCCIP/CCNB1IP1/NUMA1/VPS4B/NEK6/SPAST/PHB2/PINX1/XIAP/CCDC8/RANBP1/PDE3A/AGO4/SYCE2/PCID2
$`BRCA1, Brain - Normal`$corrHist res
$`BRCA1, Brain - Normal`$GSEA$GSEA_up res
$`BRCA1, Brain - Normal`$GSEA$GSEA_down res
correlationAnalyzeR
relies on pre-calculated datasets which are stored in a cloud database. However, it is also possible for users to generate predictions from their own datasets. To generate a correlation matrix you can supply a read count matrix to generateCorrelations()
Here is an example using the airway
dataset. We first wrangle the dataset into a raw read count matrix:
library(airway)
library(EnsDb.Hsapiens.v86)
library(dplyr)
data(airway)
<- assay(airway)
cts <- ensembldb::select(EnsDb.Hsapiens.v86, keys = rownames(cts),
ens2gene columns = c("SYMBOL"), keytype = "GENEID") %>%
::distinct(SYMBOL, .keep_all = TRUE) %>%
dplyr::inner_join(y = data.frame("GENEID" = rownames(cts)))
dplyr<- cts[ens2gene$GENEID,]
cts rownames(cts) <- ens2gene$SYMBOL
We then generate the correlation matrix with the generateCorrelations()
function:
<- generateCorrelations(cts) corrMat
## ..will not use multithreading.
## Fraction of slow calculations: 0.000000
Once the correlation matrix is generated, it can be used as the input to analyzeSingleGenes()
via the corrMat
argument with a corrMat_label
set (this is the custom label used during plotting functions).
<- analyzeSingleGenes(genesOfInterest = c("BRCA1"), corrMat = corrMat,
res corrMat_label = "User-supplied DataSet",
TERM2GENE = TERM2GENE)
##
## Retrieving any missing correlation data...
##
## Analyzing: BRCA1
## GSEA
##
## Returning ... EGMT GSEA_up GSEA_down eres
Here is the correlation histogram produced with the custom dataset:
$`BRCA1, User-supplied DataSet`$corrHist res
It is important to note that user-supplied datasets should provide enough samples to ensure robust co-expression calculations. In the above example, it is clear that there are not enough samples within the airway
dataset to support this calculation. In our experience, it is necessary to have at least 30 samples in most cases.
crossCompareMode
allows a user to examine the correlations across multiple tissue and disease conditions. For example, to analyze the correlations of BRCA1 across all tissues, we could do the following:
<- analyzeSingleGenes(genesOfInterest = c("BRCA1"), crossCompareMode = TRUE) res
##
## Retrieving GSEA annotations...
## Retrieving any missing correlation data...
##
## Running cross comparison mode ...
The output is a list containing several tables:
head(res$BRCA1$correlations, n = 3)
## BRCA1_adipose_cancer BRCA1_adipose_normal BRCA1_bladder_cancer
## A1BG -0.464 -0.2310 -0.496
## A1CF -0.267 -0.0873 -0.317
## A2M -0.686 -0.1550 -0.614
## BRCA1_bone_cancer BRCA1_bone_normal BRCA1_brain_cancer BRCA1_brain_normal
## A1BG -0.33400 0.045 -0.1380 -0.1510
## A1CF -0.00562 -0.254 -0.0249 0.0763
## A2M -0.36900 -0.167 -0.5310 -0.2170
## BRCA1_cardiac_normal BRCA1_cartilage_normal BRCA1_endothelial_normal
## A1BG -0.35800 -0.285 -0.1700
## A1CF 0.22000 -0.334 -0.0536
## A2M 0.00311 -0.529 -0.3920
## BRCA1_esophagus_cancer BRCA1_esophagus_normal
## A1BG -0.446 -0.632
## A1CF -0.344 -0.493
## A2M -0.344 -0.806
## BRCA1_female_reproductive_cancer BRCA1_female_reproductive_normal
## A1BG -0.3590 -0.3380
## A1CF 0.0502 -0.0333
## A2M -0.1370 -0.4930
## BRCA1_immune_cancer BRCA1_immune_normal BRCA1_intestines_cancer
## A1BG -0.288 -0.2760 -0.191
## A1CF -0.214 0.0289 -0.131
## A2M -0.142 -0.1530 -0.409
## BRCA1_intestines_normal BRCA1_kidney_cancer BRCA1_kidney_normal
## A1BG -0.0793 -0.455 0.032
## A1CF -0.1290 -0.437 -0.697
## A2M -0.2510 -0.791 -0.815
## BRCA1_liver_cancer BRCA1_liver_normal BRCA1_male_reproductive_normal
## A1BG -0.647 -0.233 -0.2430
## A1CF -0.370 -0.143 -0.0389
## A2M -0.389 -0.220 -0.0328
## BRCA1_mammary_cancer BRCA1_mammary_normal BRCA1_muscle_cancer
## A1BG 0.0448 -0.2740 -0.689
## A1CF -0.0545 0.0283 -0.489
## A2M -0.4580 -0.4650 -0.804
## BRCA1_muscle_normal BRCA1_pancreas_cancer BRCA1_pancreas_normal
## A1BG 0.212 -0.480 -0.378
## A1CF 0.054 -0.395 -0.302
## A2M -0.538 -0.676 -0.387
## BRCA1_prenatal_cancer BRCA1_prenatal_normal BRCA1_prostate_cancer
## A1BG -0.338 -0.146 -0.0486
## A1CF -0.201 0.202 0.1650
## A2M -0.453 -0.191 -0.4640
## BRCA1_prostate_normal BRCA1_respiratory_cancer BRCA1_respiratory_normal
## A1BG -0.286 -0.27500 0.083
## A1CF -0.280 0.00401 -0.132
## A2M -0.730 -0.48800 -0.521
## BRCA1_retina_normal BRCA1_skin_cancer BRCA1_skin_normal
## A1BG 0.334 -0.272 -0.313
## A1CF -0.181 -0.182 -0.335
## A2M -0.496 -0.210 -0.489
## BRCA1_stem_like_cancer BRCA1_stem_like_normal BRCA1_stomach_cancer
## A1BG -0.3290 -0.3100 -0.460
## A1CF -0.0998 0.0505 -0.240
## A2M -0.4000 -0.1750 -0.572
## BRCA1_stomach_normal BRCA1_thyroid_cancer BRCA1_thyroid_normal
## A1BG -0.15 -0.304 -0.322
## A1CF -0.12 -0.107 0.126
## A2M -0.38 -0.777 -0.360
head(res$BRCA1$VST_DF)
## group samples BRCA1_VST tissue sample
## 1: Female Reproductive - All GSM741172 10.69 Female Reproductive All
## 2: Female Reproductive - All GSM741170 11.26 Female Reproductive All
## 3: Female Reproductive - All GSM741171 10.48 Female Reproductive All
## 4: Female Reproductive - All GSM1098188 11.08 Female Reproductive All
## 5: Female Reproductive - All GSM800466 10.60 Female Reproductive All
## 6: Female Reproductive - All GSM988637 10.93 Female Reproductive All
The output list also contains several plots:
$BRCA1$VST_boxPlot res
res$BRCA1$heatmapSmallDataCo
.$BRCA1$heatmapSmallCo res
res$BRCA1$heatmapBigDataCo
.$BRCA1$heatmapBigCo res
res$BRCA1$heatmapSmallDataCo
.$BRCA1$heatmapSmallVar res
res$BRCA1$heatmapBigDataVar
.$BRCA1$heatmapBigVar res
correlationAnalyzeR
can be used to analyze differences between two genes using analyzeGenePairs()
(the equivalent of Gene vs Gene Mode in the web application).
<- analyzeGenePairs(genesOfInterest = c("BRCA1", "BRCA2"),
res Tissue = "all", Sample_Type = "all",
TERM2GENE = TERM2GENE)
##
## Retrieving any missing correlation data...
##
## Analyzing: BRCA1
## GSEA
##
## Returning ... EGMT eres
##
## Analyzing: BRCA2
## GSEA
##
## Returning ... EGMT eres
The analyzeGenePairs()
function performs analyzeSingleGenes()
on both of the supplied genes and then compares the results, generating several tables and figures:
$compared$VST_corrPlot$corrPlot_disease res
$compared$VST_corrPlot$corrPlot_tissue res
$compared$correlations %>%
resarrange(desc(average)) %>%
head()
## BRCA1 BRCA2 average variance
## BRCA1 1.000 0.767 0.8835 0.0271445
## BRCA2 0.767 1.000 0.8835 0.0271445
## POLQ 0.856 0.841 0.8485 0.0001125
## KIF14 0.853 0.831 0.8420 0.0002420
## CASC5 0.822 0.858 0.8400 0.0006480
## MCM10 0.861 0.800 0.8305 0.0018605
head(res$compared$`P values`)
## BRCA1 BRCA2
## A1BG 1.238588e-05 3.916497e-05
## A1CF 1.949842e-05 4.291381e-05
## A2M 7.305334e-07 9.776700e-07
## A2ML1 2.229658e-04 3.202417e-02
## A2MP1 3.044798e-06 3.472622e-06
## A3GALT2 3.380024e-04 1.680602e-04
head(res$compared$correlatedPathwaysDataFrame)
## ID NES_BRCA1 pvalue_BRCA1
## 409 GO_GENERATION_OF_PRECURSOR_METABOLITES_AND_ENERGY 1.502239 0.0005405405
## 1535 GO_REGULATION_OF_NEUROTRANSMITTER_TRANSPORT -1.345640 0.0219780220
## 275 GO_DETECTION_OF_ABIOTIC_STIMULUS -1.545303 0.0022123894
## 212 GO_CHEMOKINE_PRODUCTION -2.521010 0.0019305019
## 383 GO_FATTY_ACID_CATABOLIC_PROCESS -1.607786 0.0021008403
## 135 GO_CD4_POSITIVE_ALPHA_BETA_T_CELL_DIFFERENTIATION -2.223843 0.0018484288
## p.adjust_BRCA1
## 409 0.005632642
## 1535 0.040663790
## 275 0.007440358
## 212 0.007321919
## 383 0.007380703
## 135 0.007321919
## core_enrichment_BRCA1
## 409 CDK1/MTFR2/CCNB1/NUP155/NDC1/NUP107/NUP205/MSH2/NUP43/NUP37/NUP160/NUP188/NUP93/NUP153/NUP85/NUP35/PNPT1/NUP88/NUP50/NUP133/SEH1L/TPR/RAE1/NUP58/NUP54/NUP62/NUP98/ADSL/RPE/PPP1CC/SHMT2/MYBBP1A/MTFR1/MTOR/TRAP1/GLRX3/FOXK2/SLC25A13/IREB2/ENO1/TEFM/PASK/DLAT/STOML2/POLG2/PGM2/CYB5B/IDE/TXNRD1/CIAPIN1/TKT/COX15/PHB2/PHGDH/PKM/GSR/MYC/CYCS/RANBP2/AAAS/GLRX2/PGD/G6PD/PRELID1/FH/PGAM1/PANK2/RPIA/NOA1/TACO1/PPIF/FXN/VCP/CS/COX10/NDUFAF2/NUP214/FDXR/POM121/LDHA/COA6/PDE12/XYLB/SHPK/TPI1/TIGAR/GPD2/ETFA/GAPDH/COX11/DLD/GNPDA1/FOXK1/PPP1CA/UQCRH/GPI/TP53/CYC1/EIF6/NUP210/MDH2/PGK1/NDUFS6/GAPDHS/ALDH1B1/POM121C/PFKP/DHTKD1/PDHA1/PRKAA1/UQCC3/SLC25A22/ADPGK/IDH3B/NRF1/SEC13/GSK3B/SLC25A14/NCOR1/DERA/PTGES2/FECH/LYRM7/NDUFS1/NDUFAB1/PYGL/HK2/UQCC2/FDX2/IDH3A/PFKM/SDHB/GLRX5/RB1CC1/PHLDA2/SRD5A1/ME2/NDUFA12/COX5A/OXCT1/SDHA/VGF/TSTA3/ADH5/NDUFS3/PARK7/BID/CISD1/PFKFB4/PHKA1/NDUFA8/IDH1/TALDO1/GFPT1/NDUFB9/NDUFB6/COX6A1/NDUFS5/SLC25A33/UQCRFS1/DLST/GYS1/GALK1/ATP7A/DEGS1/PHKG2
## 1535 APBA1/LRRK2/ATP1A2/CACNA1D/MEF2C/ADRA1A/TACR2/HTR2A/P2RX1/CACNB2/PRKCB/SYT11/SNCAIP/SYN1/CAMK2A/BAIAP3/KMO/PER2/GPER1/PRRT2/ADORA2A/KCNC4/CNR1/ARL6IP5/GRIK5/DGKI/CHRNA4/NOS1/CHRM2/SYT9/CPLX1/CPLX3/SYT7/ITGB3/SNCG/FBXL20/SYT4/GRIN3A/PPFIA2/GPM6B/DRD1/MCTP2/RIMS1/SYP/NAPA/VAMP1/P2RY4/STX1B/SNCA/RAP1A/BACE1/KCNMB4/ADCY1/RAB3A/CHRNB3/PNKD/NAPB/CPLX2/DRD4
## 275 CXCL12/HPN/BEST1/PDE6B/TRPM3/PHF24/GPR88/ANO1/HTR2A/CXCR4/TAC1/KIT/TACR1/PLEKHB1/KCNA1/CNGA1/CABP4/ANO3/GNGT2/OPN4/EPHB1/SEMA5B/CALCA/CACNA1F/TMEM120A/GRM6/PKD2L1/CACNA2D4/PKD1L1/PKD1L2/LXN/TTN/STRC/CALM1/TCAP/RCVRN/RGR/PDE6C/CRB1/TMC2/PKD1L3/TRPV1/ARRB1/ASIC2/SAG/ATP8A2/ABCA4/FYN/GRIK2/EYS/TMC1/SCN1A
## 212 CD74/CSF1R/HAVCR2/APOD/IL33/TLR2/AIF1/ACKR1/LGALS9/TREM2/CLEC7A/TLR4/TLR7/C1QTNF3/IL1RL1/ADCYAP1/FFAR2/LPL/FFAR3/S100A9/MBP/S100A8/IL10/FOXP1/IL6R/SIRPA/EGR1/DEFB124/MEFV/TLR3/ALOX15B/IL7/ADIPOQ/IFNG/PYCARD/KLF4/LBP/TLR9/TNF/SIGIRR/AIRE/CXCL6/TRPV4/SYK/NR1H4/CHIA/TSLP
## 383 PLIN5/ECHDC2/ETFBKMT/ACOX2/ACADL/MLYCD/MMAA/ACACB/PCK1/FAAH/CYP4F12/CYP4A11/PPARA/BDH2/ABCD2/ACAA1/NUDT7/HAO2/ADIPOQ/ACADS/LEP/PHYH/CYP4F3/CNR1/CRAT/FABP1/PCCA/LIPE/ACOX1/ETFDH/HPGD/HAO1/DECR1/IRS2/CYP4F2/AIG1/ADTRP/ABCD4/ABCB11/MCEE/SCP2/LPIN3/PPARD/AUH/ACADVL/ABHD1/ACAD11
## 135 RORA/RORC/BCL6/GPR183/CD86/LGALS9/SATB1/SASH3/CCL19/JAK3/RSAD2/NCKAP1L/SEMA4A/TBX21/SLAMF6/IL18R1/IL12RB1/IL27/FOXP1/LY9/NLRP3/HLX/BATF/NFKBIZ/PTGER4/IRF4/IFNG/SMAD7/BCL3/CD80/STAT3/FUT7/PLA2G2D/TMEM98/STAT6/TNFSF4/TOX/SOCS1/SPN
## NES_BRCA2 pvalue_BRCA2 p.adjust_BRCA2
## 409 -1.399468 0.001795332 0.005231600
## 1535 -2.155775 0.001322751 0.004752628
## 275 -2.337813 0.001333333 0.004752628
## 212 -1.757913 0.001295337 0.004752628
## 383 -2.359113 0.001324503 0.004752628
## 135 -1.481065 0.016393443 0.032169003
## core_enrichment_BRCA2
## 409 MAOB/COX7A1/PINK1/PPP1R1A/COX4I2/ALDH2/ADGRF5/GPD1/SORBS1/ADH1B/PCDH12/KL/AVPR1A/DMGDH/NR1D1/PGAM2/NUPR1/IGF1/DHRS3/HMGCLL1/PYGM/PPARGC1A/MTFR1L/MT3/MT-ND3/STEAP4/POMC/NDUFA4L2/SIRT3/ALDH1A1/ETFBKMT/SLC4A4/MT-ND6/ACSS3/PPP1R3E/MT-CYB/MLXIPL/MT-ND1/SLC25A4/HMGCL/ACSS1/PPP1R3F/ME3/SLC25A18/ADH1C/CYBB/INSR/MT-ND2/FBP1/MT-ND4/INPP5K/PPP1R3C/MT-ATP6/HAAO/COX6A2/MT-CO1/ACSM1/QDPR/ZBTB20/THTPA/PPARA/MT-ND5/ALDOB/PID1/HK3/ALDOC/MT-CO3/RBKS/ISCU/ADH1A/NHLRC1/CHCHD10/ACADVL/SLC25A23/GCK/MT-ND4L/CBFA2T3/PFKFB1/GCGR/AK9/BDH2/ESRRB/STBD1/H6PD/NOX4/NFATC4/NR4A3/MT-ATP8/HMGCS2/PPP1R3G/IGF2/ACSS2/PHKG1/PRDM16/MT-CO2/SDHAF4/GIPR/ETFDH/COQ10A/CYB5A/GNMT/PPP1R3B/WDR93/NDUFA3/ADIPOQ/P2RX7/IDO1/IRS2/LEP/ETFB/G6PC/GLRX/OGDHL/AKR7A3/BDH1/HTR2A/DNAJC15/GYS2/BLOC1S1/CEBPA/GAA/NCF1/MCHR1/GCKR/ALDH4A1/ZBTB7A/AOX1/STAT3/CAT/ACO2/PPP1R3A
## 1535 ATP1A2/APBA1/SYN1/LRRK2/ADRA1A/RAB3A/CPLX3/BAIAP3/CAMK2A/CHRNA4/CPLX1/GRIK5/CACNB2/SNCG/PRRT2/SYP/MEF2C/SYT7/NAPA/SYT9/CACNA1D/GPER1/DRD1/CNR1/PRKCB/KCNC4/SYT11/NAPB/ADCY1/P2RX1/STXBP1/PNKD/ARL6IP5/STX1B/VAMP1/SYT4/HTR2A/CHRM2/TACR2/BACE1/CPLX2/ADORA2A/DGKI/FLOT1/PPFIA2/SNCAIP/GPM6B/DRD4/UNC13B/CPLX4/DTNBP1/CHRNB3/SNCA/PPP1R9A/PER2
## 275 CXCL12/PLEKHB1/HPN/PHF24/OPN4/TMEM120A/PDE6B/CNGA1/TRPM3/TCAP/TRPV1/CACNA1F/SEMA5B/GPR88/ANO1/GUCA1B/GUCA1C/RS1/GNGT2/PKD2L1/RCVRN/ANO3/TAC1/CABP4/BEST1/KCNA1/GRM6/RHO/GUCY2D/ABCA4/CRB1/PDE6G/EPHB1/ASIC3/ASIC2/CACNA2D4/GNAT1/WHRN/HTR2A/TACR1/PKD1L1/CXCR4/KIT/GUCA1A/CALCA/BACE1/REEP6/GRK1/CSRP3/ADORA1/NGFR/NR2E3/GRK4/RGR/GUCY2F/PDC/ATP8A2/RRH/PDE6C/CALM1/GRK7/STRC/CNGB1/OPN1MW/RP1/KCNK4/SAG/PKD1L2/PKD1/GRIK2/NTRK1/FYN/AIPL1/UNC119
## 212 ACKR1/CD74/APOD/CSF1R/IL33/LPL/AIF1/HAVCR2/TREM2/LGALS9/C1QTNF3/SIRPA/MBP/ADCYAP1/TLR2/EGR1/DEFB124/SIGIRR/FFAR3/LBP/TLR4/ADIPOQ/S100A8/IL1RL1/S100A9/CLEC7A/ALOX15B/IL6R/TLR7/TSLP/TLR9/NR1H4/TLR3/TUSC2/FFAR2/AGER/KLF4/SYK/CHIA/MAP2K5/MEFV/PYCARD/AIRE/CXCL6
## 383 ECHDC2/ACOX2/PLIN5/MLYCD/ACACB/FAAH/ACADL/ETFBKMT/ACAA1/NUDT7/PHYH/PCK1/CYP4F12/ACADS/CRAT/ABCD2/PPARA/ACADVL/BDH2/LIPE/CYP4A11/AIG1/CPT1B/CNR1/AUH/ETFDH/MMAA/DECR1/ECH1/ACAD11/HAO2/ADIPOQ/IRS2/ABCD4/LEP/ETFB/PCCA/IVD/ABHD16A/MCEE/ACAD10/ACOT8/HADHB/FABP1/PPARD/SCP2/CYP4F3/HAO1/ABCB11/LPIN3/HPGD/ACAA2/HADH/PLA2G15/CYP4F2/ABHD1/ACOX1/CROT/AMACR/ALDH3A2/LONP2/EHHADH/ILVBL/CPT1C/CYP4F11/ECI2/DECR2
## 135 RORC/BCL6/RORA/SATB1/LGALS9/CCL19/CD86/SASH3/SEMA4A/JAK3/RSAD2/GPR183/TBX21/IL18R1/BCL3/SMAD7/TMEM98/NCKAP1L/STAT3/HLX/IL27/TOX/STAT6/SLAMF6/PRKCZ/ZBTB7B/IL12RB1/FUT7/CTSL/RARA/NLRP3/BATF
## NES_average NES_variance
## 409 0.05138567 4.2099503
## 1535 -1.75070718 0.3281596
## 275 -1.94155818 0.3140364
## 212 -2.13946184 0.2911586
## 383 -1.98344954 0.2822456
## 135 -1.85245403 0.2758598
head(res$compared$VST_Data)
## Group samples VST Gene
## 1: Female Reproductive - All GSM741172 10.69 BRCA1
## 2: Female Reproductive - All GSM741170 11.26 BRCA1
## 3: Female Reproductive - All GSM741171 10.48 BRCA1
## 4: Female Reproductive - All GSM1098188 11.08 BRCA1
## 5: Female Reproductive - All GSM800466 10.60 BRCA1
## 6: Female Reproductive - All GSM988637 10.93 BRCA1
$compared$correlationPlot res
$compared$correlationPlotBin res
$compared$correlationVarianceHeatmap res
$compared$correlationSimilarityHeatmap res
$compared$pathwayVarianceHeatmap res
$compared$pathwaySimilarityHeatmap res
In analyzeGenePairs()
, cross-compare mode allows the user to analyze the co-expression of two genes across all tissue-disease conditions (geneVsGene
) or one gene in cancer vs normal (normalVsCancer
).
When genesOfInterest
is supplied with two different genes and crossCompareMode=TRUE
, then geneVsGene
mode is executed.
<- analyzeGenePairs(genesOfInterest = c("BRCA1", "BRCA2"),
res crossCompareMode = TRUE)
##
## Running cross comparison mode ...
##
## Gene one is not the same as gene two ...
##
## Will perform comparison of BRCA1 and BRCA2 across all available tissue-disease conditions...
##
## Retrieving any missing correlation data...
##
## Analyzing: BRCA1...1
## Analyzing: BRCA1...2
## Analyzing: BRCA1...3
## Analyzing: BRCA1...4
## Analyzing: BRCA1...5
## Analyzing: BRCA1...6
## Analyzing: BRCA1...7
## Analyzing: BRCA1...8
## Analyzing: BRCA1...9
## Analyzing: BRCA1...10
## Analyzing: BRCA1...11
## Analyzing: BRCA1...12
## Analyzing: BRCA1...13
## Analyzing: BRCA1...14
## Analyzing: BRCA1...15
## Analyzing: BRCA1...16
## Analyzing: BRCA1...17
## Analyzing: BRCA1...18
## Analyzing: BRCA1...19
## Analyzing: BRCA1...20
## Analyzing: BRCA1...21
## Analyzing: BRCA1...22
## Analyzing: BRCA1...23
## Analyzing: BRCA1...24
## Analyzing: BRCA1...25
## Analyzing: BRCA1...26
## Analyzing: BRCA1...27
## Analyzing: BRCA1...28
## Analyzing: BRCA1...29
## Analyzing: BRCA1...30
## Analyzing: BRCA1...31
## Analyzing: BRCA1...32
## Analyzing: BRCA1...33
## Analyzing: BRCA1...34
## Analyzing: BRCA1...35
## Analyzing: BRCA1...36
## Analyzing: BRCA1...37
## Analyzing: BRCA1...38
## Analyzing: BRCA1...39
## Analyzing: BRCA1...40
## Analyzing: BRCA1...41
## Analyzing: BRCA1...42
## Analyzing: BRCA1...43
## Analyzing: BRCA1...44
## Analyzing: BRCA2...45
## Analyzing: BRCA2...46
## Analyzing: BRCA2...47
## Analyzing: BRCA2...48
## Analyzing: BRCA2...49
## Analyzing: BRCA2...50
## Analyzing: BRCA2...51
## Analyzing: BRCA2...52
## Analyzing: BRCA2...53
## Analyzing: BRCA2...54
## Analyzing: BRCA2...55
## Analyzing: BRCA2...56
## Analyzing: BRCA2...57
## Analyzing: BRCA2...58
## Analyzing: BRCA2...59
## Analyzing: BRCA2...60
## Analyzing: BRCA2...61
## Analyzing: BRCA2...62
## Analyzing: BRCA2...63
## Analyzing: BRCA2...64
## Analyzing: BRCA2...65
## Analyzing: BRCA2...66
## Analyzing: BRCA2...67
## Analyzing: BRCA2...68
## Analyzing: BRCA2...69
## Analyzing: BRCA2...70
## Analyzing: BRCA2...71
## Analyzing: BRCA2...72
## Analyzing: BRCA2...73
## Analyzing: BRCA2...74
## Analyzing: BRCA2...75
## Analyzing: BRCA2...76
## Analyzing: BRCA2...77
## Analyzing: BRCA2...78
## Analyzing: BRCA2...79
## Analyzing: BRCA2...80
## Analyzing: BRCA2...81
## Analyzing: BRCA2...82
## Analyzing: BRCA2...83
## Analyzing: BRCA2...84
## Analyzing: BRCA2...85
## Analyzing: BRCA2...86
## Analyzing: BRCA2...87
## Analyzing: BRCA2...88
This analysis produces a list containing several figures and tables:
$Correlations %>%
resarrange(desc(average)) %>%
head(n=3)
## BRCA1_Adipose_Cancer BRCA2_Adipose_Cancer BRCA1_Adipose_Normal
## BRCA2 0.891 1.000 0.568
## BRCA1 1.000 0.891 1.000
## CASC5 0.817 0.813 0.576
## BRCA2_Adipose_Normal BRCA1_Bladder_Cancer BRCA2_Bladder_Cancer
## BRCA2 1.000 0.813 1.000
## BRCA1 0.568 1.000 0.813
## CASC5 0.833 0.902 0.849
## BRCA1_Bone_Cancer BRCA2_Bone_Cancer BRCA1_Bone_Normal BRCA2_Bone_Normal
## BRCA2 0.592 1.000 0.857 1.000
## BRCA1 1.000 0.592 1.000 0.857
## CASC5 0.674 0.893 0.888 0.847
## BRCA1_Brain_Cancer BRCA2_Brain_Cancer BRCA1_Brain_Normal
## BRCA2 0.819 1.000 0.738
## BRCA1 1.000 0.819 1.000
## CASC5 0.822 0.875 0.742
## BRCA2_Brain_Normal BRCA1_Cardiac_Normal BRCA2_Cardiac_Normal
## BRCA2 1.000 0.451 1.000
## BRCA1 0.738 1.000 0.451
## CASC5 0.907 0.456 0.811
## BRCA1_Cartilage_Normal BRCA2_Cartilage_Normal BRCA1_Endothelial_Normal
## BRCA2 0.638 1.000 0.764
## BRCA1 1.000 0.638 1.000
## CASC5 0.732 0.684 0.861
## BRCA2_Endothelial_Normal BRCA1_Esophagus_Cancer BRCA2_Esophagus_Cancer
## BRCA2 1.000 0.060 1.00
## BRCA1 0.764 1.000 0.06
## CASC5 0.840 0.654 0.36
## BRCA1_Esophagus_Normal BRCA2_Esophagus_Normal
## BRCA2 0.707 1.000
## BRCA1 1.000 0.707
## CASC5 0.953 0.817
## BRCA1_Female_reproductive_Cancer BRCA2_Female_reproductive_Cancer
## BRCA2 0.623 1.000
## BRCA1 1.000 0.623
## CASC5 0.710 0.785
## BRCA1_Female_reproductive_Normal BRCA2_Female_reproductive_Normal
## BRCA2 0.841 1.000
## BRCA1 1.000 0.841
## CASC5 0.882 0.911
## BRCA1_Immune_Cancer BRCA2_Immune_Cancer BRCA1_Immune_Normal
## BRCA2 0.585 1.000 0.610
## BRCA1 1.000 0.585 1.000
## CASC5 0.815 0.651 0.841
## BRCA2_Immune_Normal BRCA1_Intestines_Cancer BRCA2_Intestines_Cancer
## BRCA2 1.000 0.778 1.000
## BRCA1 0.610 1.000 0.778
## CASC5 0.682 0.706 0.794
## BRCA1_Intestines_Normal BRCA2_Intestines_Normal BRCA1_Kidney_Cancer
## BRCA2 0.821 1.000 0.842
## BRCA1 1.000 0.821 1.000
## CASC5 0.527 0.614 0.883
## BRCA2_Kidney_Cancer BRCA1_Kidney_Normal BRCA2_Kidney_Normal
## BRCA2 1.000 0.931 1.000
## BRCA1 0.842 1.000 0.931
## CASC5 0.926 0.820 0.822
## BRCA1_Liver_Cancer BRCA2_Liver_Cancer BRCA1_Liver_Normal
## BRCA2 0.821 1.000 0.505
## BRCA1 1.000 0.821 1.000
## CASC5 0.767 0.857 0.477
## BRCA2_Liver_Normal BRCA1_Male_reproductive_Normal
## BRCA2 1.000 0.409
## BRCA1 0.505 1.000
## CASC5 0.612 0.643
## BRCA2_Male_reproductive_Normal BRCA1_Mammary_Cancer BRCA2_Mammary_Cancer
## BRCA2 1.000 0.690 1.000
## BRCA1 0.409 1.000 0.690
## CASC5 0.629 0.799 0.713
## BRCA1_Mammary_Normal BRCA2_Mammary_Normal BRCA1_Muscle_Cancer
## BRCA2 0.845 1.000 0.764
## BRCA1 1.000 0.845 1.000
## CASC5 0.908 0.883 0.895
## BRCA2_Muscle_Cancer BRCA1_Muscle_Normal BRCA2_Muscle_Normal
## BRCA2 1.000 0.755 1.000
## BRCA1 0.764 1.000 0.755
## CASC5 0.796 0.773 0.888
## BRCA1_Pancreas_Cancer BRCA2_Pancreas_Cancer BRCA1_Pancreas_Normal
## BRCA2 0.771 1.000 0.708
## BRCA1 1.000 0.771 1.000
## CASC5 0.760 0.834 0.778
## BRCA2_Pancreas_Normal BRCA1_Prenatal_Cancer BRCA2_Prenatal_Cancer
## BRCA2 1.000 0.701 1.000
## BRCA1 0.708 1.000 0.701
## CASC5 0.879 0.775 0.858
## BRCA1_Prenatal_Normal BRCA2_Prenatal_Normal BRCA1_Prostate_Cancer
## BRCA2 0.571 1.000 0.808
## BRCA1 1.000 0.571 1.000
## CASC5 0.538 0.742 0.793
## BRCA2_Prostate_Cancer BRCA1_Prostate_Normal BRCA2_Prostate_Normal
## BRCA2 1.000 0.898 1.000
## BRCA1 0.808 1.000 0.898
## CASC5 0.861 0.942 0.918
## BRCA1_Respiratory_Cancer BRCA2_Respiratory_Cancer
## BRCA2 0.785 1.000
## BRCA1 1.000 0.785
## CASC5 0.779 0.832
## BRCA1_Respiratory_Normal BRCA2_Respiratory_Normal BRCA1_Retina_Normal
## BRCA2 0.765 1.000 0.755
## BRCA1 1.000 0.765 1.000
## CASC5 0.854 0.855 0.779
## BRCA2_Retina_Normal BRCA1_Skin_Cancer BRCA2_Skin_Cancer BRCA1_Skin_Normal
## BRCA2 1.000 0.724 1.000 0.768
## BRCA1 0.755 1.000 0.724 1.000
## CASC5 0.959 0.800 0.815 0.878
## BRCA2_Skin_Normal BRCA1_Stem_like_Cancer BRCA2_Stem_like_Cancer
## BRCA2 1.000 0.585 1.000
## BRCA1 0.768 1.000 0.585
## CASC5 0.846 0.583 0.869
## BRCA1_Stem_like_Normal BRCA2_Stem_like_Normal BRCA1_Stomach_Cancer
## BRCA2 0.848 1.000 0.771
## BRCA1 1.000 0.848 1.000
## CASC5 0.849 0.841 0.778
## BRCA2_Stomach_Cancer BRCA1_Stomach_Normal BRCA2_Stomach_Normal
## BRCA2 1.000 0.637 1.000
## BRCA1 0.771 1.000 0.637
## CASC5 0.867 0.740 0.904
## BRCA1_Thyroid_Cancer BRCA2_Thyroid_Cancer BRCA1_Thyroid_Normal
## BRCA2 0.895 1.000 0.519
## BRCA1 1.000 0.895 1.000
## CASC5 0.924 0.941 0.547
## BRCA2_Thyroid_Normal average variance
## BRCA2 1.000 0.8548523 0.03366140
## BRCA1 0.519 0.8548523 0.03366140
## CASC5 0.921 0.7892500 0.01469241
head(res$`P values`, n=3)
## BRCA1_Adipose_Cancer BRCA2_Adipose_Cancer BRCA1_Adipose_Normal
## A1BG 1.429101e-06 1.233671e-06 1.532356e-05
## A1CF 9.647610e-06 1.633674e-04 3.011732e-04
## A2M 2.451212e-07 7.303493e-08 5.297196e-05
## BRCA2_Adipose_Normal BRCA1_Bladder_Cancer BRCA2_Bladder_Cancer
## A1BG 1.375515e-05 1.102002e-06 5.193783e-05
## A1CF 1.597039e-05 5.497299e-06 1.488720e-01
## A2M 4.301172e-06 4.363948e-07 2.630311e-06
## BRCA1_Bone_Cancer BRCA2_Bone_Cancer BRCA1_Bone_Normal BRCA2_Bone_Normal
## A1BG 4.613896e-06 2.781100e-05 2.136895e-03 0.0003726911
## A1CF 2.017450e-01 2.781100e-05 1.132711e-05 0.0001633674
## A2M 3.280996e-06 1.339545e-05 4.212776e-05 0.0003754446
## BRCA1_Brain_Cancer BRCA2_Brain_Cancer BRCA1_Brain_Normal
## A1BG 7.554884e-05 2.161610e-04 5.738861e-05
## A1CF 1.133493e-02 6.496327e-05 4.504482e-04
## A2M 8.347306e-07 1.489341e-06 1.866402e-05
## BRCA2_Brain_Normal BRCA1_Cardiac_Normal BRCA2_Cardiac_Normal
## A1BG 5.180937e-04 3.642654e-06 5.977836e-05
## A1CF 1.575096e-05 1.787498e-05 7.596938e-07
## A2M 1.184767e-06 2.908913e-01 2.240981e-06
## BRCA1_Cartilage_Normal BRCA2_Cartilage_Normal BRCA1_Endothelial_Normal
## A1BG 7.807097e-06 6.745699e-06 3.987952e-05
## A1CF 4.613896e-06 1.077346e-05 1.281731e-03
## A2M 8.479733e-07 5.920962e-06 2.654175e-06
## BRCA2_Endothelial_Normal BRCA1_Esophagus_Cancer BRCA2_Esophagus_Cancer
## A1BG 1.588460e-04 1.65979e-06 3.974747e-06
## A1CF 1.544883e-04 4.17508e-06 3.160264e-06
## A2M 5.271963e-06 4.17508e-06 3.342691e-05
## BRCA1_Esophagus_Normal BRCA2_Esophagus_Normal
## A1BG 3.787528e-07 1.339545e-05
## A1CF 1.128868e-06 6.859702e-03
## A2M 8.137162e-08 1.852708e-06
## BRCA1_Female_reproductive_Cancer BRCA2_Female_reproductive_Cancer
## A1BG 3.607860e-06 5.581658e-04
## A1CF 1.553299e-03 8.649631e-05
## A2M 7.724159e-05 1.943984e-04
## BRCA1_Female_reproductive_Normal BRCA2_Female_reproductive_Normal
## A1BG 4.431929e-06 8.565095e-06
## A1CF 5.070298e-03 1.462351e-04
## A2M 1.128868e-06 2.752223e-06
## BRCA1_Immune_Cancer BRCA2_Immune_Cancer BRCA1_Immune_Normal
## A1BG 7.544636e-06 0.042529271 8.666226e-06
## A1CF 1.949842e-05 0.007912909 7.539760e-03
## A2M 6.924488e-05 0.001377529 5.512242e-05
## BRCA2_Immune_Normal BRCA1_Intestines_Cancer BRCA2_Intestines_Cancer
## A1BG 2.607465e-05 2.781100e-05 6.601797e-04
## A1CF 3.867317e-04 8.851973e-05 4.875614e-04
## A2M 1.943984e-04 2.280738e-06 2.341926e-06
## BRCA1_Intestines_Normal BRCA2_Intestines_Normal BRCA1_Kidney_Cancer
## A1BG 4.014808e-04 3.616344e-03 1.539589e-06
## A1CF 9.275709e-05 1.418043e-02 1.790749e-06
## A2M 1.176621e-05 1.839613e-05 9.506695e-08
## BRCA2_Kidney_Cancer BRCA1_Kidney_Normal BRCA2_Kidney_Normal
## A1BG 8.367240e-06 5.672677e-03 2.404929e-04
## A1CF 1.132711e-05 2.236988e-07 2.817186e-07
## A2M 2.451212e-07 7.384143e-08 2.004448e-07
## BRCA1_Liver_Cancer BRCA2_Liver_Cancer BRCA1_Liver_Normal
## A1BG 3.362405e-07 8.614376e-07 1.491089e-05
## A1CF 3.250303e-06 2.447611e-05 6.777763e-05
## A2M 2.727315e-06 1.866402e-05 1.787498e-05
## BRCA2_Liver_Normal BRCA1_Male_reproductive_Normal
## A1BG 1.632188e-06 1.304766e-05
## A1CF 4.093509e-06 3.255528e-03
## A2M 3.375179e-06 5.291571e-03
## BRCA2_Male_reproductive_Normal BRCA1_Mammary_Cancer BRCA2_Mammary_Cancer
## A1BG 7.544636e-06 2.164735e-03 1.279990e-04
## A1CF 4.096017e-03 1.220516e-03 4.856115e-04
## A2M 4.061116e-05 1.501728e-06 4.660806e-06
## BRCA1_Mammary_Normal BRCA2_Mammary_Normal BRCA1_Muscle_Cancer
## A1BG 8.873033e-06 5.057882e-06 2.391115e-07
## A1CF 7.990411e-03 4.454423e-05 1.165805e-06
## A2M 1.417383e-06 2.854583e-06 8.311306e-08
## BRCA2_Muscle_Cancer BRCA1_Muscle_Normal BRCA2_Muscle_Normal
## A1BG 2.752223e-06 2.008148e-05 2.854583e-06
## A1CF 1.779931e-04 1.254035e-03 1.354603e-03
## A2M 1.613024e-07 7.900646e-07 2.981349e-07
## BRCA1_Pancreas_Cancer BRCA2_Pancreas_Cancer BRCA1_Pancreas_Normal
## A1BG 1.253850e-06 1.984152e-06 3.016722e-06
## A1CF 2.583337e-06 2.383783e-06 6.455803e-06
## A2M 2.661080e-07 5.063062e-07 2.777400e-06
## BRCA2_Pancreas_Normal BRCA1_Prenatal_Cancer BRCA2_Prenatal_Cancer
## A1BG 6.526801e-06 4.431929e-06 1.707612e-03
## A1CF 1.093202e-06 2.372429e-05 4.136039e-05
## A2M 8.453325e-05 1.565426e-06 1.839613e-05
## BRCA1_Prenatal_Normal BRCA2_Prenatal_Normal BRCA1_Prostate_Cancer
## A1BG 6.361346e-05 1.921505e-05 1.707612e-03
## A1CF 2.335956e-05 1.501728e-06 4.371910e-05
## A2M 2.781100e-05 2.459463e-04 1.429101e-06
## BRCA2_Prostate_Cancer BRCA1_Prostate_Normal BRCA2_Prostate_Normal
## A1BG 3.269712e-01 7.718359e-06 2.230795e-05
## A1CF 3.231604e-03 8.270464e-06 2.447611e-05
## A2M 1.790749e-06 1.685892e-07 2.127000e-07
## BRCA1_Respiratory_Cancer BRCA2_Respiratory_Cancer BRCA1_Respiratory_Normal
## A1BG 8.768863e-06 1.893696e-05 3.503310e-04
## A1CF 2.588889e-01 9.481094e-02 8.649631e-05
## A2M 1.175244e-06 6.526801e-06 9.032033e-07
## BRCA2_Respiratory_Normal BRCA1_Retina_Normal BRCA2_Retina_Normal
## A1BG 2.804638e-04 4.613896e-06 4.130566e-07
## A1CF 1.369830e-03 3.285870e-05 1.147174e-06
## A2M 5.858029e-06 1.102002e-06 9.700721e-08
## BRCA1_Skin_Cancer BRCA2_Skin_Cancer BRCA1_Skin_Normal BRCA2_Skin_Normal
## A1BG 9.086085e-06 4.805074e-06 5.734624e-06 1.071841e-04
## A1CF 3.230297e-05 3.987952e-05 4.567563e-06 1.502870e-04
## A2M 2.068719e-05 5.076420e-04 1.165805e-06 1.866402e-05
## BRCA1_Stem_like_Cancer BRCA2_Stem_like_Cancer BRCA1_Stem_like_Normal
## A1BG 4.854374e-06 1.984152e-06 5.920962e-06
## A1CF 2.015195e-04 3.156615e-02 1.526441e-03
## A2M 2.470155e-06 7.051971e-06 3.646805e-05
## BRCA2_Stem_like_Normal BRCA1_Stomach_Cancer BRCA2_Stomach_Cancer
## A1BG 6.248484e-06 1.477069e-06 4.854374e-06
## A1CF 3.122766e-05 1.357378e-05 8.514839e-02
## A2M 7.390447e-05 6.057403e-07 2.300924e-06
## BRCA1_Stomach_Normal BRCA2_Stomach_Normal BRCA1_Thyroid_Cancer
## A1BG 5.856752e-05 4.662892e-03 6.316660e-06
## A1CF 1.155136e-04 4.854374e-06 1.633674e-04
## A2M 2.961488e-06 8.175089e-06 1.092494e-07
## BRCA2_Thyroid_Cancer BRCA1_Thyroid_Normal BRCA2_Thyroid_Normal
## A1BG 4.805074e-06 5.217415e-06 2.970022e-05
## A1CF 3.070451e-03 9.962777e-05 2.260758e-06
## A2M 1.168885e-07 3.573465e-06 8.682541e-07
::ggarrange(res$crossCompareVST$VST_boxPlotOne, res$crossCompareVST$VST_boxPlotTwo,
ggpubrnrow = 2)
head(res$crossCompareVST$VST_DF)
## Group Tissue sampleType Samples
## 1: female reproductive - cancer Female Reproductive Cancer GSM741172
## 2: female reproductive - cancer Female Reproductive Cancer GSM741170
## 3: female reproductive - cancer Female Reproductive Cancer GSM741171
## 4: female reproductive - cancer Female Reproductive Cancer GSM1098188
## 5: female reproductive - cancer Female Reproductive Cancer GSM800466
## 6: female reproductive - cancer Female Reproductive Cancer GSM988637
## BRCA1_VST BRCA2_VST
## 1: 10.69 9.232
## 2: 11.26 9.475
## 3: 10.48 8.994
## 4: 11.08 9.474
## 5: 10.60 10.560
## 6: 10.93 9.703
$pairResList$`Bone Cancer`$scatterPlot res
$pairResList$`Bone Cancer`$heatMap res
When genesOfInterest
is supplied with only one gene, Tissue
includes Cancer
and Normal
, and crossCompareMode=TRUE
, then normalVsCancer
mode is executed.
<- analyzeGenePairs(genesOfInterest = c("BRCA1", "BRCA1"),
res Tissue = c("Cancer", "Normal"),
crossCompareMode = TRUE)
##
## Running cross comparison mode ...
##
## Gene one is the same as gene two ...
##
## Will perform normal vs cancer comparison on BRCA1 ...
##
## Retrieving any missing correlation data...
##
## Analyzing: BRCA1...1
## Analyzing: BRCA1...2
## Analyzing: BRCA1...3
## Analyzing: BRCA1...4
## Analyzing: BRCA1...5
## Analyzing: BRCA1...6
## Analyzing: BRCA1...7
## Analyzing: BRCA1...8
## Analyzing: BRCA1...9
## Analyzing: BRCA1...10
## Analyzing: BRCA1...11
## Analyzing: BRCA1...12
## Analyzing: BRCA1...13
## Analyzing: BRCA1...14
## Analyzing: BRCA1...15
## Analyzing: BRCA1...16
## Analyzing: BRCA1...17
## Analyzing: BRCA1...18
## Analyzing: BRCA1...19
## Analyzing: BRCA1...20
## Analyzing: BRCA1...21
## Analyzing: BRCA1...22
## Analyzing: BRCA1...23
## Analyzing: BRCA1...24
## Analyzing: BRCA1...25
## Analyzing: BRCA1...26
## Analyzing: BRCA1...27
## Analyzing: BRCA1...28
## Analyzing: BRCA1...29
## Analyzing: BRCA1...30
## Analyzing: BRCA1...31
## Analyzing: BRCA1...32
## Analyzing: BRCA1...33
## Analyzing: BRCA1...34
## Analyzing: BRCA1...35
## Analyzing: BRCA1...36
## Analyzing: BRCA1...37
## Analyzing: BRCA1...38
## Analyzing: BRCA1...39
## Analyzing: BRCA1...40
The primary difference betwen normalVsCancer
and geneVsGene
is that analyzeGenePairs
will output:
$crossCompareVST$VST_boxPlot res
$pairResList$`Immune - Normal`$scatterPlot res
$pairResList$`Immune - Normal`$heatMap res
This mode provides an empirical approach for determining whether a gene is significantly correlated with a list of genes. This is an alternative to the typical Pearson correlation p value which can only determine whether any two genes are significantly co-expressed. To run this mode, use the geneVsGeneListAnalyze()
function:
<- geneVsGeneListAnalyze(pairedGenesList = list("BRCA1" = c("BRCA2", "EZH2", "CCND1",
res "SLC7A11", "GCLC", "CDKN1A")),
Sample_Type = "cancer",
Tissue = "bone")
##
## BRCA1
This returns several plots and tables:
head(res$correlations)
## BRCA1
## A1BG -0.33400
## A1CF -0.00562
## A2M -0.36900
## A2ML1 -0.03070
## A2MP1 -0.14200
## A3GALT2 -0.17600
head(res$`P values`)
## BRCA1
## A1BG 4.613896e-06
## A1CF 2.017450e-01
## A2M 3.280996e-06
## A2ML1 6.372330e-03
## A2MP1 6.924488e-05
## A3GALT2 3.583187e-05
$BRCA1$Correlation_histogram res
$BRCA1$sigTest$tTest_pvalsPlot res
$BRCA1$sigTest$meansPlot res
$BRCA1$sigTest$mediansPlot res
data.frame(means = res$BRCA1$sigTest$means,
medians = res$BRCA1$sigTest$medians,
pvals = res$BRCA1$sigTest$tTest_pvals) %>% head()
## means medians pvals
## 1 0.068300000 0.03140 0.01434408
## 2 -0.001123333 0.01548 0.01742272
## 3 0.060450000 0.03335 0.09604538
## 4 -0.089833333 -0.06665 0.02779362
## 5 -0.047033333 -0.06365 0.02306242
## 6 0.080900000 0.10330 0.03642956
Many methods for dimensionality reduction exist, but most are focused on sample-level comparisons and few methods for analyzing feature-space topology exist. In the final analysis mode, correlationAnalyzeR
uses gene co-expression correlation values as a metric for dimensionality reduction via PCA
and tSNE
with agglomerative clustering to determine the topology of a list of genes.
The analysis can be accessed using the analyzeGenesetTopology()
function from this package:
<- c("CDK12", "AURKB", "SFPQ", "NFKB1", "BRCC3", "BRCA2", "PARP1",
genesOfInterest "EZH2", "CCND1", "SLC7A11", "GCLC", "CDKN1A", "MTAP",
"DHX9", "SON", "AURKA", "SETX", "BRCA1", "ATMIN")
<- analyzeGenesetTopology(genesOfInterest = genesOfInterest,
res Sample_Type = "cancer", Tissue = "bone")
##
## Retrieving TERM2GENE...
##
## Retrieving correlation data...
##
## Starting cross comparison
This produces several tables and figures:
head(res$Correlation_Data)
## CDK12 AURKB SFPQ NFKB1 BRCC3 BRCA2 PARP1 EZH2 CCND1
## A1BG -0.3020 -0.00278 -0.0733 -0.1760 -0.324 -0.191 -0.1790 -0.208 0.145
## A1CF 0.0304 -0.35900 -0.3700 -0.0765 -0.185 0.191 -0.4850 -0.266 -0.379
## A2M -0.2210 -0.35200 -0.1770 0.1520 -0.122 -0.241 -0.2630 -0.316 -0.345
## A2ML1 -0.1720 0.00964 0.2400 -0.4040 -0.177 0.355 0.0113 0.499 0.469
## A2MP1 -0.1550 -0.32100 -0.4150 -0.0834 -0.111 -0.232 -0.3550 -0.327 -0.471
## A3GALT2 -0.2530 -0.02010 0.0612 -0.1290 -0.118 -0.113 -0.1110 0.229 0.106
## SLC7A11 GCLC CDKN1A MTAP DHX9 SON AURKA SETX
## A1BG -0.0709 -0.1650 -0.0318 -0.1840 -0.2200 -0.39200 -0.206 -0.1600
## A1CF -0.0222 0.0385 -0.0092 0.1890 -0.3150 -0.23000 -0.124 0.0645
## A2M -0.2830 0.0868 0.2670 0.1180 -0.2720 0.00385 -0.346 -0.0429
## A2ML1 0.1820 -0.3260 -0.5230 -0.0161 0.1810 -0.19900 -0.115 0.2600
## A2MP1 -0.3310 0.0580 0.1440 0.1820 -0.3630 -0.15500 -0.231 -0.2190
## A3GALT2 -0.1460 -0.3610 -0.0927 0.0407 -0.0379 -0.02750 -0.311 -0.0688
## BRCA1 ATMIN
## A1BG -0.33400 -0.07740
## A1CF -0.00562 -0.11600
## A2M -0.36900 -0.00942
## A2ML1 -0.03070 -0.06380
## A2MP1 -0.14200 -0.05840
## A3GALT2 -0.17600 -0.21200
head(res$`P values`)
## CDK12 AURKB SFPQ NFKB1 BRCC3
## A1BG 6.455803e-06 3.019223e-01 5.076420e-04 3.583187e-05 5.110386e-06
## A1CF 6.549620e-03 3.607860e-06 3.250303e-06 4.469452e-04 3.070741e-05
## A2M 1.762152e-05 3.860133e-06 3.521007e-05 5.624057e-05 1.098700e-04
## A2ML1 3.846702e-05 9.911473e-02 1.357378e-05 2.383783e-06 3.521007e-05
## A2MP1 5.297196e-05 5.271963e-06 2.163862e-06 3.453293e-04 1.462351e-04
## A3GALT2 1.147113e-05 1.992120e-02 8.670981e-04 9.275709e-05 1.215453e-04
## BRCA2 PARP1 EZH2 CCND1 SLC7A11
## A1BG 2.781100e-05 3.400797e-05 2.131666e-05 6.496327e-05 5.605105e-04
## A1CF 2.781100e-05 1.204068e-06 9.765120e-06 2.988953e-06 1.539160e-02
## A2M 1.339545e-05 1.012866e-05 5.555476e-06 4.134050e-06 7.988439e-06
## A2ML1 3.749491e-06 7.411171e-02 1.075831e-06 1.371571e-06 3.230297e-05
## A2MP1 1.511542e-05 3.749491e-06 4.954847e-06 1.349286e-06 4.756385e-06
## A3GALT2 1.385537e-04 1.462351e-04 1.575096e-05 1.680602e-04 6.361346e-05
## GCLC CDKN1A MTAP DHX9 SON
## A1BG 4.371910e-05 5.773534e-03 3.122766e-05 1.787498e-05 2.654175e-06
## A1CF 3.353589e-03 1.072106e-01 2.873548e-05 5.614415e-06 1.553538e-05
## A2M 3.064001e-04 9.647610e-06 1.215453e-04 9.086085e-06 2.646928e-01
## A2ML1 5.006040e-06 8.890480e-07 3.444274e-02 3.285870e-05 2.447611e-05
## A2MP1 1.016110e-03 6.635088e-05 3.230297e-05 3.472622e-06 5.297196e-05
## A3GALT2 3.539464e-06 2.515643e-04 2.858100e-03 3.508101e-03 8.647497e-03
## AURKA SETX BRCA1 ATMIN
## A1BG 2.197104e-05 4.805570e-05 4.613896e-06 4.316206e-04
## A1CF 1.045840e-04 7.422648e-04 2.017450e-01 1.279990e-04
## A2M 4.093509e-06 2.454713e-03 3.280996e-06 1.030775e-01
## A2ML1 1.313956e-04 1.050948e-05 6.372330e-03 7.666517e-04
## A2MP1 1.532356e-05 1.813315e-05 6.924488e-05 9.957179e-04
## A3GALT2 5.858029e-06 6.129114e-04 3.583187e-05 2.008148e-05
$variantGenesHeatmap_Top res
head(res$variantGenesHeatmap_Top_MAT)
## CDK12 AURKB SFPQ NFKB1 BRCC3 BRCA2 PARP1 EZH2 CCND1 SLC7A11
## KIAA1456 -0.217 0.157 0.411 -0.454 -0.03340 0.312 0.179 0.734 0.671 0.298
## HOOK1 -0.188 0.176 0.434 -0.396 0.05760 0.269 0.270 0.782 0.674 0.302
## GDF6 -0.161 0.245 0.456 -0.371 0.00420 0.242 0.287 0.761 0.668 0.298
## DCDC2 -0.159 0.175 0.439 -0.425 0.00941 0.300 0.252 0.714 0.691 0.276
## LIPI -0.180 0.228 0.445 -0.399 0.06830 0.287 0.300 0.767 0.688 0.320
## UGT3A2 -0.194 0.261 0.436 -0.384 0.03570 0.227 0.308 0.773 0.703 0.291
## GCLC CDKN1A MTAP DHX9 SON AURKA SETX BRCA1 ATMIN
## KIAA1456 -0.328 -0.651 -0.04730 0.324 -0.05300 -0.08060 0.327 0.02680 -0.04460
## HOOK1 -0.354 -0.626 0.02920 0.400 0.00424 -0.06330 0.337 0.02200 0.00424
## GDF6 -0.358 -0.638 -0.07600 0.387 0.03430 -0.03340 0.297 0.04750 -0.05040
## DCDC2 -0.316 -0.658 -0.05990 0.383 -0.04020 -0.03100 0.335 0.04220 -0.00426
## LIPI -0.298 -0.638 0.00739 0.415 0.02420 -0.00376 0.319 0.05240 0.01260
## UGT3A2 -0.321 -0.609 -0.07610 0.406 0.03660 -0.01810 0.270 0.00819 -0.01660
$variantGenesHeatmap res
head(res$variantGenesHeatmap_MAT)
## CDK12 AURKB SFPQ NFKB1 BRCC3 BRCA2 PARP1 EZH2 CCND1 SLC7A11
## KIAA1456 -0.217 0.157 0.411 -0.454 -0.03340 0.312 0.179 0.734 0.671 0.298
## HOOK1 -0.188 0.176 0.434 -0.396 0.05760 0.269 0.270 0.782 0.674 0.302
## GDF6 -0.161 0.245 0.456 -0.371 0.00420 0.242 0.287 0.761 0.668 0.298
## DCDC2 -0.159 0.175 0.439 -0.425 0.00941 0.300 0.252 0.714 0.691 0.276
## LIPI -0.180 0.228 0.445 -0.399 0.06830 0.287 0.300 0.767 0.688 0.320
## UGT3A2 -0.194 0.261 0.436 -0.384 0.03570 0.227 0.308 0.773 0.703 0.291
## GCLC CDKN1A MTAP DHX9 SON AURKA SETX BRCA1 ATMIN
## KIAA1456 -0.328 -0.651 -0.04730 0.324 -0.05300 -0.08060 0.327 0.02680 -0.04460
## HOOK1 -0.354 -0.626 0.02920 0.400 0.00424 -0.06330 0.337 0.02200 0.00424
## GDF6 -0.358 -0.638 -0.07600 0.387 0.03430 -0.03340 0.297 0.04750 -0.05040
## DCDC2 -0.316 -0.658 -0.05990 0.383 -0.04020 -0.03100 0.335 0.04220 -0.00426
## LIPI -0.298 -0.638 0.00739 0.415 0.02420 -0.00376 0.319 0.05240 0.01260
## UGT3A2 -0.321 -0.609 -0.07610 0.406 0.03660 -0.01810 0.270 0.00819 -0.01660
$cocorrelativeGenesHeatmap res
head(res$cocorrelativeGenesHeatmap_MAT)
## CDK12 AURKB SFPQ NFKB1 BRCC3 BRCA2 PARP1 EZH2 CCND1
## AASDHPPT 0.0916 0.264 0.594 -0.0507 0.452 0.52400 0.472 0.608 0.516
## ABCD3 0.1600 0.151 0.538 0.0447 0.563 0.52300 0.487 0.497 0.302
## ABCE1 0.1810 0.297 0.648 0.1230 0.436 0.59700 0.483 0.617 0.516
## ABCF1 0.5350 0.522 0.592 0.4030 0.443 0.08270 0.672 0.330 0.255
## AC004022.8 -0.2830 -0.396 -0.533 -0.2970 -0.338 -0.00953 -0.536 -0.231 -0.325
## AC096579.13 -0.3140 -0.313 -0.490 -0.2360 -0.329 -0.10100 -0.479 -0.175 -0.239
## SLC7A11 GCLC CDKN1A MTAP DHX9 SON AURKA SETX BRCA1
## AASDHPPT 0.471 0.193 -0.2850 0.28300 0.707 0.133 0.386 0.554 0.425
## ABCD3 0.442 0.293 -0.0801 0.34500 0.660 0.152 0.405 0.598 0.487
## ABCE1 0.518 0.204 -0.1510 0.25000 0.758 0.157 0.443 0.598 0.479
## ABCF1 0.158 0.412 0.2380 0.08260 0.587 0.496 0.546 0.170 0.359
## AC004022.8 -0.160 -0.229 -0.2160 0.06300 -0.467 -0.406 -0.358 -0.175 -0.229
## AC096579.13 -0.165 -0.265 -0.2000 0.00711 -0.442 -0.393 -0.359 -0.226 -0.269
## ATMIN
## AASDHPPT 0.535
## ABCD3 0.587
## ABCE1 0.520
## ABCF1 0.327
## AC004022.8 -0.373
## AC096579.13 -0.361
$PCA_plot res
$PCA_data res
## geneNames PC1 PC2 clusters
## 1 CDK12 0.08785426 0.24414259 1
## 2 AURKB -0.14233336 0.06205815 2
## 3 SFPQ -0.28904305 0.12649761 2
## 4 NFKB1 0.24055804 0.24030287 1
## 5 BRCC3 -0.03354104 0.26391350 1
## 6 BRCA2 -0.17340668 0.28311924 3
## 7 PARP1 -0.18967139 0.11271175 2
## 8 EZH2 -0.50201755 -0.05092973 2
## 9 CCND1 -0.39594993 -0.03714854 2
## 10 SLC7A11 -0.15724525 0.23344593 3
## 11 GCLC 0.22148318 0.35545070 1
## 12 CDKN1A 0.39638795 0.17942647 1
## 13 MTAP 0.03198465 0.19728059 3
## 14 DHX9 -0.26763275 0.22077471 2
## 15 SON -0.04835706 0.06004943 2
## 16 AURKA 0.02053370 0.34491863 1
## 17 SETX -0.21287102 0.22704985 3
## 18 BRCA1 -0.05307863 0.30243257 1
## 19 ATMIN 0.01395277 0.36058617 1
$inputGenes_pathwayEnrich_dotplot res
head(res$inputGenes_pathwayEnrich_data)
## ID
## GO_POSITIVE_REGULATION_OF_CELL_CYCLE GO_POSITIVE_REGULATION_OF_CELL_CYCLE
## GO_RESPONSE_TO_RADIATION GO_RESPONSE_TO_RADIATION
## GO_NEGATIVE_REGULATION_OF_CELL_CYCLE_PHASE_TRANSITION GO_NEGATIVE_REGULATION_OF_CELL_CYCLE_PHASE_TRANSITION
## GO_RESPONSE_TO_IONIZING_RADIATION GO_RESPONSE_TO_IONIZING_RADIATION
## GO_POSITIVE_REGULATION_OF_CELL_CYCLE_PROCESS GO_POSITIVE_REGULATION_OF_CELL_CYCLE_PROCESS
## BLUM_RESPONSE_TO_SALIRASIB_DN BLUM_RESPONSE_TO_SALIRASIB_DN
## Description
## GO_POSITIVE_REGULATION_OF_CELL_CYCLE GO_POSITIVE_REGULATION_OF_CELL_CYCLE
## GO_RESPONSE_TO_RADIATION GO_RESPONSE_TO_RADIATION
## GO_NEGATIVE_REGULATION_OF_CELL_CYCLE_PHASE_TRANSITION GO_NEGATIVE_REGULATION_OF_CELL_CYCLE_PHASE_TRANSITION
## GO_RESPONSE_TO_IONIZING_RADIATION GO_RESPONSE_TO_IONIZING_RADIATION
## GO_POSITIVE_REGULATION_OF_CELL_CYCLE_PROCESS GO_POSITIVE_REGULATION_OF_CELL_CYCLE_PROCESS
## BLUM_RESPONSE_TO_SALIRASIB_DN BLUM_RESPONSE_TO_SALIRASIB_DN
## GeneRatio BgRatio
## GO_POSITIVE_REGULATION_OF_CELL_CYCLE 8/19 396/22755
## GO_RESPONSE_TO_RADIATION 8/19 446/22755
## GO_NEGATIVE_REGULATION_OF_CELL_CYCLE_PHASE_TRANSITION 7/19 270/22755
## GO_RESPONSE_TO_IONIZING_RADIATION 6/19 144/22755
## GO_POSITIVE_REGULATION_OF_CELL_CYCLE_PROCESS 7/19 302/22755
## BLUM_RESPONSE_TO_SALIRASIB_DN 7/19 341/22755
## pvalue p.adjust
## GO_POSITIVE_REGULATION_OF_CELL_CYCLE 5.012634e-10 8.281177e-07
## GO_RESPONSE_TO_RADIATION 1.279966e-09 8.281177e-07
## GO_NEGATIVE_REGULATION_OF_CELL_CYCLE_PHASE_TRANSITION 1.366879e-09 8.281177e-07
## GO_RESPONSE_TO_IONIZING_RADIATION 1.466344e-09 8.281177e-07
## GO_POSITIVE_REGULATION_OF_CELL_CYCLE_PROCESS 2.974362e-09 1.343817e-06
## BLUM_RESPONSE_TO_SALIRASIB_DN 6.890156e-09 2.594144e-06
## qvalue
## GO_POSITIVE_REGULATION_OF_CELL_CYCLE 3.357156e-07
## GO_RESPONSE_TO_RADIATION 3.357156e-07
## GO_NEGATIVE_REGULATION_OF_CELL_CYCLE_PHASE_TRANSITION 3.357156e-07
## GO_RESPONSE_TO_IONIZING_RADIATION 3.357156e-07
## GO_POSITIVE_REGULATION_OF_CELL_CYCLE_PROCESS 5.447778e-07
## BLUM_RESPONSE_TO_SALIRASIB_DN 1.051655e-06
## geneID
## GO_POSITIVE_REGULATION_OF_CELL_CYCLE AURKB/SFPQ/BRCA2/EZH2/CCND1/CDKN1A/AURKA/BRCA1
## GO_RESPONSE_TO_RADIATION AURKB/BRCC3/BRCA2/PARP1/CCND1/SLC7A11/CDKN1A/BRCA1
## GO_NEGATIVE_REGULATION_OF_CELL_CYCLE_PHASE_TRANSITION AURKB/BRCC3/EZH2/CCND1/CDKN1A/AURKA/BRCA1
## GO_RESPONSE_TO_IONIZING_RADIATION BRCC3/BRCA2/PARP1/CCND1/CDKN1A/BRCA1
## GO_POSITIVE_REGULATION_OF_CELL_CYCLE_PROCESS AURKB/SFPQ/EZH2/CCND1/CDKN1A/AURKA/BRCA1
## BLUM_RESPONSE_TO_SALIRASIB_DN AURKB/SFPQ/PARP1/EZH2/DHX9/AURKA/BRCA1
## Count
## GO_POSITIVE_REGULATION_OF_CELL_CYCLE 8
## GO_RESPONSE_TO_RADIATION 8
## GO_NEGATIVE_REGULATION_OF_CELL_CYCLE_PHASE_TRANSITION 7
## GO_RESPONSE_TO_IONIZING_RADIATION 6
## GO_POSITIVE_REGULATION_OF_CELL_CYCLE_PROCESS 7
## BLUM_RESPONSE_TO_SALIRASIB_DN 7
clusterProfiler
during pathway enrichment (this is compatible with the other functions in the clusterProfiler
package):$inputGenes_pathwayEnrich res
## #
## # over-representation test
## #
## #...@organism UNKNOWN
## #...@ontology UNKNOWN
## #...@gene chr [1:19] "CDK12" "AURKB" "SFPQ" "NFKB1" "BRCC3" "BRCA2" "PARP1" "EZH2" ...
## #...pvalues adjusted by 'BH' with cutoff <0.05
## #...938 enriched terms found
## 'data.frame': 938 obs. of 9 variables:
## $ ID : chr "GO_POSITIVE_REGULATION_OF_CELL_CYCLE" "GO_RESPONSE_TO_RADIATION" "GO_NEGATIVE_REGULATION_OF_CELL_CYCLE_PHASE_TRANSITION" "GO_RESPONSE_TO_IONIZING_RADIATION" ...
## $ Description: chr "GO_POSITIVE_REGULATION_OF_CELL_CYCLE" "GO_RESPONSE_TO_RADIATION" "GO_NEGATIVE_REGULATION_OF_CELL_CYCLE_PHASE_TRANSITION" "GO_RESPONSE_TO_IONIZING_RADIATION" ...
## $ GeneRatio : chr "8/19" "8/19" "7/19" "6/19" ...
## $ BgRatio : chr "396/22755" "446/22755" "270/22755" "144/22755" ...
## $ pvalue : num 5.01e-10 1.28e-09 1.37e-09 1.47e-09 2.97e-09 ...
## $ p.adjust : num 8.28e-07 8.28e-07 8.28e-07 8.28e-07 1.34e-06 ...
## $ qvalue : num 3.36e-07 3.36e-07 3.36e-07 3.36e-07 5.45e-07 ...
## $ geneID : chr "AURKB/SFPQ/BRCA2/EZH2/CCND1/CDKN1A/AURKA/BRCA1" "AURKB/BRCC3/BRCA2/PARP1/CCND1/SLC7A11/CDKN1A/BRCA1" "AURKB/BRCC3/EZH2/CCND1/CDKN1A/AURKA/BRCA1" "BRCC3/BRCA2/PARP1/CCND1/CDKN1A/BRCA1" ...
## $ Count : int 8 8 7 6 7 7 7 6 6 4 ...
## #...Citation
## Guangchuang Yu, Li-Gen Wang, Yanyan Han and Qing-Yu He.
## clusterProfiler: an R package for comparing biological themes among
## gene clusters. OMICS: A Journal of Integrative Biology
## 2012, 16(5):284-287
Unlike the web application version of Correlation AnalyzeR, the R package is capable of handling arbitrarily-large gene lists for analyzeGenesetTopology()
. One instance where one might wish to perform an analysis like this could be in parsing an existing gene set from curated sources like Gene Ontology.
To obtain the list of genes for this analysis, it is convenient to use the msigdbr
package in the following manner:
library(tidyverse)
## Warning: package 'tidyr' was built under R version 4.0.4
<- msigdbr::msigdbr(category = "C2", subcategory = "CGP")
MDF <- MDF %>%
geneList filter(gs_name == "RIGGI_EWING_SARCOMA_PROGENITOR_UP") %>%
pull(gene_symbol)
We have now obtained a vector with the 434 genes in the “RIGGI_EWING_SARCOMA_PROGENITOR_UP” gene set from the Chemical and Genetic Perturbations (CGP) database in the “C2” collection of MSigDB. A link to the info page for this gene set can be found here. Now, we can use this list as the input for analyzeGenesetTopology()
. NOTE: when a gene in our gene list is not found in the correlation data, it will automatically be skipped.
<- analyzeGenesetTopology(genesOfInterest = geneList,
res Sample_Type = "cancer",
Tissue = "bone")
## Warning in analyzeGenesetTopology(genesOfInterest = geneList, Sample_Type = "cancer", :
## 'C15orf48, C1orf226, ECRG4, H2AC6, H2BC4, H2BC5, H2BC6, H2BC7, H2BS1, H4C8, ITGB2-AS1, JCAD, KASH5, KRTAP4-9, LINC00839, LINC01503, LINC02762, LRATD1, LRATD2, MALAT1, MARCHF1, PALM2AKAP2, PRXL2A, RUBCNL, STING1, TRMT9B'
## not found in correlation data and is not an official MSIGDB name.
## Please check available gene data with getAvailableGenes().
## Your gene(s) of interest may have an updated name or
## have a species-specific identifier. Find offical MSIGDB
## names by examining the MSIGDB_Geneset_Names object.
##
## Continuing without this/these gene(s)...
##
## Retrieving TERM2GENE...
##
## Retrieving correlation data...
##
## Starting cross comparison
##
## Using TSNE instead of PCA for large sample sizes.
## To disable this behavior, set 'alternativeTSNE' to FALSERead the 410 x 410 data matrix successfully!
## OpenMP is working. 1 threads.
## Using no_dims = 2, perplexity = 30.000000, and theta = 0.500000
## Computing input similarities...
## Building tree...
## Done in 0.10 seconds (sparsity = 0.270518)!
## Learning embedding...
## Iteration 50: error is 66.030554 (50 iterations in 0.04 seconds)
## Iteration 100: error is 66.021746 (50 iterations in 0.03 seconds)
## Iteration 150: error is 66.034030 (50 iterations in 0.03 seconds)
## Iteration 200: error is 66.046966 (50 iterations in 0.03 seconds)
## Iteration 250: error is 66.031184 (50 iterations in 0.03 seconds)
## Iteration 300: error is 1.315898 (50 iterations in 0.03 seconds)
## Iteration 350: error is 1.313036 (50 iterations in 0.02 seconds)
## Iteration 400: error is 1.312164 (50 iterations in 0.02 seconds)
## Iteration 450: error is 1.312486 (50 iterations in 0.02 seconds)
## Iteration 500: error is 1.312154 (50 iterations in 0.02 seconds)
## Iteration 550: error is 1.311493 (50 iterations in 0.02 seconds)
## Iteration 600: error is 1.310892 (50 iterations in 0.02 seconds)
## Iteration 650: error is 1.311133 (50 iterations in 0.02 seconds)
## Iteration 700: error is 1.311040 (50 iterations in 0.02 seconds)
## Iteration 750: error is 1.311216 (50 iterations in 0.02 seconds)
## Iteration 800: error is 1.311071 (50 iterations in 0.02 seconds)
## Iteration 850: error is 1.310981 (50 iterations in 0.02 seconds)
## Iteration 900: error is 1.311059 (50 iterations in 0.02 seconds)
## Iteration 950: error is 1.311291 (50 iterations in 0.02 seconds)
## Iteration 1000: error is 1.311196 (50 iterations in 0.02 seconds)
## Iteration 1050: error is 1.311270 (50 iterations in 0.02 seconds)
## Iteration 1100: error is 1.311291 (50 iterations in 0.02 seconds)
## Iteration 1150: error is 1.311232 (50 iterations in 0.02 seconds)
## Iteration 1200: error is 1.311371 (50 iterations in 0.02 seconds)
## Iteration 1250: error is 1.311285 (50 iterations in 0.02 seconds)
## Iteration 1300: error is 1.311283 (50 iterations in 0.02 seconds)
## Iteration 1350: error is 1.311351 (50 iterations in 0.02 seconds)
## Iteration 1400: error is 1.311289 (50 iterations in 0.02 seconds)
## Iteration 1450: error is 1.311180 (50 iterations in 0.02 seconds)
## Iteration 1500: error is 1.311216 (50 iterations in 0.02 seconds)
## Iteration 1550: error is 1.311275 (50 iterations in 0.02 seconds)
## Iteration 1600: error is 1.311164 (50 iterations in 0.02 seconds)
## Iteration 1650: error is 1.311133 (50 iterations in 0.02 seconds)
## Iteration 1700: error is 1.311301 (50 iterations in 0.02 seconds)
## Iteration 1750: error is 1.311126 (50 iterations in 0.02 seconds)
## Iteration 1800: error is 1.311186 (50 iterations in 0.02 seconds)
## Iteration 1850: error is 1.311227 (50 iterations in 0.02 seconds)
## Iteration 1900: error is 1.311182 (50 iterations in 0.02 seconds)
## Iteration 1950: error is 1.311244 (50 iterations in 0.02 seconds)
## Iteration 2000: error is 1.311241 (50 iterations in 0.02 seconds)
## Iteration 2050: error is 1.311374 (50 iterations in 0.02 seconds)
## Iteration 2100: error is 1.311317 (50 iterations in 0.02 seconds)
## Iteration 2150: error is 1.311174 (50 iterations in 0.02 seconds)
## Iteration 2200: error is 1.311245 (50 iterations in 0.02 seconds)
## Iteration 2250: error is 1.311224 (50 iterations in 0.03 seconds)
## Iteration 2300: error is 1.311291 (50 iterations in 0.02 seconds)
## Iteration 2350: error is 1.311273 (50 iterations in 0.02 seconds)
## Iteration 2400: error is 1.311338 (50 iterations in 0.02 seconds)
## Iteration 2450: error is 1.311061 (50 iterations in 0.02 seconds)
## Iteration 2500: error is 1.311321 (50 iterations in 0.02 seconds)
## Iteration 2550: error is 1.311296 (50 iterations in 0.02 seconds)
## Iteration 2600: error is 1.311127 (50 iterations in 0.02 seconds)
## Iteration 2650: error is 1.311084 (50 iterations in 0.02 seconds)
## Iteration 2700: error is 1.311179 (50 iterations in 0.02 seconds)
## Iteration 2750: error is 1.311254 (50 iterations in 0.02 seconds)
## Iteration 2800: error is 1.311297 (50 iterations in 0.02 seconds)
## Iteration 2850: error is 1.311312 (50 iterations in 0.02 seconds)
## Iteration 2900: error is 1.311294 (50 iterations in 0.02 seconds)
## Iteration 2950: error is 1.311299 (50 iterations in 0.02 seconds)
## Iteration 3000: error is 1.311214 (50 iterations in 0.02 seconds)
## Iteration 3050: error is 1.311350 (50 iterations in 0.02 seconds)
## Iteration 3100: error is 1.311362 (50 iterations in 0.02 seconds)
## Iteration 3150: error is 1.311311 (50 iterations in 0.02 seconds)
## Iteration 3200: error is 1.311370 (50 iterations in 0.02 seconds)
## Iteration 3250: error is 1.311332 (50 iterations in 0.02 seconds)
## Iteration 3300: error is 1.311287 (50 iterations in 0.02 seconds)
## Iteration 3350: error is 1.311254 (50 iterations in 0.02 seconds)
## Iteration 3400: error is 1.311193 (50 iterations in 0.02 seconds)
## Iteration 3450: error is 1.311179 (50 iterations in 0.02 seconds)
## Iteration 3500: error is 1.311226 (50 iterations in 0.02 seconds)
## Iteration 3550: error is 1.311170 (50 iterations in 0.02 seconds)
## Iteration 3600: error is 1.311106 (50 iterations in 0.02 seconds)
## Iteration 3650: error is 1.311283 (50 iterations in 0.02 seconds)
## Iteration 3700: error is 1.311098 (50 iterations in 0.02 seconds)
## Iteration 3750: error is 1.311229 (50 iterations in 0.02 seconds)
## Iteration 3800: error is 1.311374 (50 iterations in 0.02 seconds)
## Iteration 3850: error is 1.311064 (50 iterations in 0.02 seconds)
## Iteration 3900: error is 1.311335 (50 iterations in 0.02 seconds)
## Iteration 3950: error is 1.311230 (50 iterations in 0.02 seconds)
## Iteration 4000: error is 1.311175 (50 iterations in 0.02 seconds)
## Iteration 4050: error is 1.311167 (50 iterations in 0.02 seconds)
## Iteration 4100: error is 1.311235 (50 iterations in 0.02 seconds)
## Iteration 4150: error is 1.311152 (50 iterations in 0.02 seconds)
## Iteration 4200: error is 1.311208 (50 iterations in 0.02 seconds)
## Iteration 4250: error is 1.311051 (50 iterations in 0.02 seconds)
## Iteration 4300: error is 1.311399 (50 iterations in 0.02 seconds)
## Iteration 4350: error is 1.311245 (50 iterations in 0.02 seconds)
## Iteration 4400: error is 1.311308 (50 iterations in 0.02 seconds)
## Iteration 4450: error is 1.311240 (50 iterations in 0.02 seconds)
## Iteration 4500: error is 1.311352 (50 iterations in 0.02 seconds)
## Iteration 4550: error is 1.311195 (50 iterations in 0.02 seconds)
## Iteration 4600: error is 1.311136 (50 iterations in 0.02 seconds)
## Iteration 4650: error is 1.311086 (50 iterations in 0.02 seconds)
## Iteration 4700: error is 1.311216 (50 iterations in 0.02 seconds)
## Iteration 4750: error is 1.311057 (50 iterations in 0.02 seconds)
## Iteration 4800: error is 1.311276 (50 iterations in 0.02 seconds)
## Iteration 4850: error is 1.311271 (50 iterations in 0.02 seconds)
## Iteration 4900: error is 1.311345 (50 iterations in 0.02 seconds)
## Iteration 4950: error is 1.311254 (50 iterations in 0.02 seconds)
## Iteration 5000: error is 1.311303 (50 iterations in 0.02 seconds)
## Fitting performed in 2.15 seconds.
Because of the large number of genes supplied, a tSNE was calculated instead of PCA. This behavior can be prevented by setting the alternativeTSNE
parameter to FALSE
. The visualization is designed to allow easier cluster interpretation and does not include gene labels:
$TSNE_plot res
However, the underlying plot data is supplied as well:
head(res$TSNE_data)
## geneNames tsne1 tsne2 hclust
## 1 A2M -1.221786599 -0.04844189 1
## 2 ABCA5 -0.657285426 -1.28988016 2
## 3 ABHD6 0.658190185 0.45528606 3
## 4 ACACB 0.209647628 0.05357400 4
## 5 ADAMTS3 -0.416643908 0.58589809 5
## 6 ADARB1 0.007257496 0.09008766 6
Which means that, using plotly
, it is straightforward to create an interactive visualization that includes gene name information:
<- (res$TSNE_data %>%
plt ggplot(aes(x = tsne1, y = tsne2, color = hclust, label = geneNames)) +
geom_point()) %>%
::ggplotly() plotly
sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] forcats_0.5.1 stringr_1.4.0
## [3] purrr_0.3.4 readr_1.4.0
## [5] tidyr_1.1.3 tibble_3.0.6
## [7] ggplot2_3.3.3 tidyverse_1.3.0
## [9] dplyr_1.0.4 EnsDb.Hsapiens.v86_2.99.0
## [11] ensembldb_2.14.0 AnnotationFilter_1.14.0
## [13] GenomicFeatures_1.42.1 AnnotationDbi_1.52.0
## [15] airway_1.10.0 SummarizedExperiment_1.20.0
## [17] Biobase_2.50.0 GenomicRanges_1.42.0
## [19] GenomeInfoDb_1.26.2 IRanges_2.24.1
## [21] S4Vectors_0.28.1 BiocGenerics_0.36.0
## [23] MatrixGenerics_1.2.1 matrixStats_0.58.0
## [25] correlationAnalyzeR_1.0.0
##
## loaded via a namespace (and not attached):
## [1] SMVar_1.3.3 rappdirs_0.3.3 rtracklayer_1.49.5
## [4] bit64_4.0.5 knitr_1.31 DelayedArray_0.16.1
## [7] data.table_1.13.6 rpart_4.1-15 RCurl_1.98-1.2
## [10] doParallel_1.0.16 generics_0.1.0 preprocessCore_1.52.1
## [13] callr_3.5.1 cowplot_1.1.1 usethis_2.0.1
## [16] RSQLite_2.2.3 shadowtext_0.0.7 bit_4.0.4
## [19] enrichplot_1.10.2 lubridate_1.7.10 xml2_1.3.2
## [22] assertthat_0.2.1 viridis_0.5.1 xfun_0.21
## [25] hms_1.0.0 jquerylib_0.1.3 evaluate_0.14
## [28] fansi_0.4.2 progress_1.2.2 caTools_1.18.1
## [31] dbplyr_2.1.0 readxl_1.3.1 igraph_1.2.6
## [34] DBI_1.1.1 geneplotter_1.68.0 htmlwidgets_1.5.3
## [37] ellipsis_0.3.1 crosstalk_1.1.1 ggpubr_0.4.0
## [40] backports_1.2.1 annotate_1.68.0 biomaRt_2.46.3
## [43] vctrs_0.3.6 remotes_2.2.0 abind_1.4-5
## [46] cachem_1.0.4 withr_2.4.1 ggforce_0.3.3
## [49] checkmate_2.0.0 GenomicAlignments_1.26.0 prettyunits_1.1.1
## [52] cluster_2.1.0 DOSE_3.16.0 lazyeval_0.2.2
## [55] crayon_1.4.1 genefilter_1.72.1 pkgconfig_2.0.3
## [58] labeling_0.4.2 tweenr_1.0.1 nlme_3.1-149
## [61] pkgload_1.2.0 ProtGenerics_1.22.0 nnet_7.3-14
## [64] devtools_2.3.2 rlang_0.4.10 lifecycle_1.0.0
## [67] downloader_0.4 BiocFileCache_1.14.0 modelr_0.1.8
## [70] cellranger_1.1.0 rprojroot_2.0.2 polyclip_1.10-0
## [73] Matrix_1.2-18 carData_3.0-4 boot_1.3-25
## [76] reprex_1.0.0 base64enc_0.1-3 processx_3.4.5
## [79] pheatmap_1.0.12 png_0.1-7 viridisLite_0.3.0
## [82] bitops_1.0-6 KernSmooth_2.23-17 Biostrings_2.58.0
## [85] blob_1.2.1 qvalue_2.22.0 jpeg_0.1-8.1
## [88] rstatix_0.7.0 gridGraphics_0.5-1 ggsignif_0.6.1
## [91] scales_1.1.1 memoise_2.0.0 magrittr_2.0.1
## [94] plyr_1.8.6 gplots_3.1.1 zlibbioc_1.36.0
## [97] compiler_4.0.3 scatterpie_0.1.5 RColorBrewer_1.1-2
## [100] metaMA_3.1.2 DESeq2_1.30.0 Rsamtools_2.6.0
## [103] cli_2.3.1 XVector_0.30.0 ps_1.6.0
## [106] htmlTable_2.1.0 Formula_1.2-4 MASS_7.3-53
## [109] mgcv_1.8-33 WGCNA_1.70-3 tidyselect_1.1.0
## [112] stringi_1.5.3 highr_0.8 yaml_2.2.1
## [115] GOSemSim_2.16.1 askpass_1.1 locfit_1.5-9.4
## [118] latticeExtra_0.6-29 ggrepel_0.9.1 grid_4.0.3
## [121] sass_0.3.1 fastmatch_1.1-0 tools_4.0.3
## [124] rio_0.5.26 rstudioapi_0.13 foreach_1.5.1
## [127] foreign_0.8-80 gridExtra_2.3 Rtsne_0.15
## [130] farver_2.1.0 ggraph_2.0.5 digest_0.6.27
## [133] rvcheck_0.1.8 BiocManager_1.30.10 SuperExactTest_1.0.7
## [136] Rcpp_1.0.6 car_3.0-10 broom_0.7.5
## [139] httr_1.4.2 colorspace_2.0-0 rvest_1.0.0
## [142] XML_3.99-0.5 fs_1.5.0 splines_4.0.3
## [145] graphlayouts_0.7.1 ggplotify_0.0.5 plotly_4.9.3
## [148] sessioninfo_1.1.1 xtable_1.8-4 jsonlite_1.7.2
## [151] dynamicTreeCut_1.63-1 tidygraph_1.2.0 testthat_3.0.2
## [154] R6_2.5.0 Hmisc_4.5-0 pillar_1.5.1
## [157] htmltools_0.5.1.1 glue_1.4.2 fastmap_1.1.0
## [160] clusterProfiler_3.18.1 BiocParallel_1.24.1 RMySQL_0.10.21
## [163] codetools_0.2-16 fgsea_1.16.0 pkgbuild_1.2.0
## [166] utf8_1.2.1 lattice_0.20-41 bslib_0.2.4
## [169] curl_4.3 gtools_3.8.2 zip_2.1.1
## [172] GO.db_3.12.1 openxlsx_4.2.3 openssl_1.4.3
## [175] limma_3.46.0 survival_3.2-7 rmarkdown_2.7
## [178] desc_1.3.0 munsell_0.5.0 DO.db_2.9
## [181] fastcluster_1.1.25 GenomeInfoDbData_1.2.4 iterators_1.0.13
## [184] impute_1.64.0 haven_2.3.1 reshape2_1.4.4
## [187] gtable_0.3.0 msigdbr_7.2.1
Feel free to email Henry Miller (millerh1@uthscsa.edu) any time with questions, bug reports, or if you want to contribute!