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Analyse result - methylation data (correlation)
Dataset:
Methyl_159
Gene A:
CDKN2A
Gene B:
CDKN2B
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Plot (Beta)
Data
R code
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CGGA.ID Histology Grade Gender Age OS Censor Gene.A.Methylation Gene.B.Methylation Normal1 Normal NA NA NA NA NA 0.0661204344444444 0.2974582146 Normal2 Normal NA NA NA NA NA 0.0565476144444444 0.2881851876 Normal3 Normal NA NA NA NA NA 0.0487030861111111 0.2786802112 Normal4 Normal NA NA NA NA NA 0.124925557777778 0.358224655 Normal5 Normal NA NA NA NA NA 0.129000686666667 0.273756102 Normal6 Normal NA NA NA NA NA 0.203770036666667 0.335271346 Normal7 Normal NA NA NA NA NA 0.123182825555556 0.413006149 Normal8 Normal NA NA NA NA NA 0.134251306666667 0.341617161 CGGA_469 A WHO II Male 30 4464 0 0.22553553 0.388429006 CGGA_285 A WHO II Male 30 1576 0 0.21242121 0.413931933 CGGA_590 A WHO II Male 36 4319 0 0.209135427777778 0.40739835 CGGA_447 A WHO II Female 20 4508 0 0.198739408888889 0.356926394 CGGA_433 A WHO II Male 30 NA NA 0.316439782222222 0.496147714 CGGA_321 A WHO II Male 42 NA NA 0.363018522222222 0.535373642 CGGA_258 A WHO II Female 61 663 1 0.201983574444444 0.42005699 CGGA_522 A WHO II Female 24 3630 0 0.196662443333333 0.343251372 CGGA_317 A WHO II Female 42 4681 0 0.210049954444444 0.395448632 CGGA_601 A WHO II Male 36 1133 1 0.243933855555556 0.453590285 CGGA_461 A WHO II Male 36 1266 1 0.239694575555556 0.395172022 CGGA_357 A WHO II Female 37 2518 1 0.232345704444444 0.38155309 CGGA_542 A WHO II Male 24 NA NA 0.179929624444444 0.28469249 CGGA_544 A WHO II Female 44 4371 0 0.202812164444444 0.379182376 CGGA_626 A WHO II Male 20 4273 0 0.265992961111111 0.444711517 CGGA_673 AA WHO III Male 54 NA NA 0.245067043333333 0.425384983 CGGA_688 A WHO II Male 19 4196 0 0.193844023333333 0.358638597 CGGA_692 AOA WHO III Male 19 NA NA 0.277541423333333 0.433394228 CGGA_505 A WHO II Male 36 2994 1 0.3686275 0.508494953 CGGA_379 A WHO II Female 39 1077 1 0.415425702222222 0.553847641 CGGA_648 A WHO II Female 33 3647 1 0.234271262222222 0.471939422 CGGA_396 A WHO II Female 49 4576 0 0.270908367777778 0.551404905 CGGA_552 A WHO II Female 36 4362 0 0.195748232222222 0.538273168 CGGA_595 A WHO II Female 51 NA NA 0.250865996666667 0.422952212 CGGA_583 A WHO II Female 36 3107 1 0.362997706666667 0.527411012 CGGA_365 A WHO II Female 32 3593 0 0.170477265555556 0.417588347 CGGA_459 A WHO II Male 44 3613 0 0.193033401111111 0.273520789 CGGA_434 A WHO II Male 35 4513 0 0.192256103333333 0.310717177 CGGA_399 A WHO II Male 36 2663 1 0.365635564444444 0.5281978 CGGA_281 A WHO II Female 36 1236 0 0.354941677777778 0.3948981 CGGA_407 A WHO II Male 33 4555 0 0.350404222222222 0.34289283 CGGA_708 A WHO II Female 38 4167 0 0.348583133333333 0.36818584 CGGA_711 A WHO II Male 49 1798 0 0.339118066666667 0.33443285 CGGA_712 A WHO II Male 25 NA NA 0.344867655555556 0.33917869 CGGA_718 A WHO II Male 17 4159 0 0.326718622222222 0.33426826 CGGA_736 A WHO II Male 38 1164 1 0.280396213333333 0.436963145 CGGA_743 A WHO II Male 30 851 1 0.177327121111111 0.166921691 CGGA_746 A WHO II Male 45 332 1 0.356524844444444 0.34559127 CGGA_753 A WHO II Male 24 3217 1 0.335328056666667 0.483676877 CGGA_767 AA WHO III Male 36 1310 1 0.0715341466666667 0.12446373 CGGA_770 A WHO II Male 30 1057 0 0.0772148311111111 0.150954745 CGGA_776 A WHO II Female 48 4091 0 0.376670111111111 0.34602914 CGGA_781 rA WHO II Male 35 4088 0 0.361724255555556 0.36307917 CGGA_792 A WHO II Male 32 816 1 0.361646733333333 0.3520686 CGGA_253 A WHO II Female 37 5159 0 0.393399344444444 0.38234058 CGGA_571 A WHO II Male 38 NA NA 0.16472985 0.54640181 CGGA_592 A WHO II Male 47 4315 0 0.240921426666667 0.535335805 CGGA_663 A WHO II Female 29 4210 0 0.226081301111111 0.363897684 CGGA_316 A WHO II Female 30 4682 0 0.397200075555556 0.561921601 CGGA_402 AA WHO III Male 53 4562 0 0.331515356666667 0.510981907 CGGA_548 A WHO II Male 39 NA NA 0.229460844444444 0.423951247 CGGA_354 AA WHO III Male 62 180 1 0.233171685555556 0.410094809 CGGA_334 AA WHO III Female 35 1450 1 0.198233163333333 0.269469304 CGGA_247 AA WHO III Male 60 609 1 0.21683605 0.375499339 CGGA_486 AA WHO III Male 23 424 1 0.31908289 0.488198398 CGGA_353 AA WHO III Female 60 516 1 0.227388241111111 0.361945667 CGGA_476 A WHO II Female 37 936 1 0.223146643333333 0.398665441 CGGA_405 AA WHO III Male 59 497 1 0.25363738 0.410683129 CGGA_412 AA WHO III Female 42 358 1 0.126793541111111 0.310550378 CGGA_410 AA WHO III Female 70 1099 1 0.254849993333333 0.334486387 CGGA_249 AA WHO III Male 41 312 1 0.125861616666667 0.178267287 CGGA_331 AA WHO III Female 27 1638 1 0.35834936 0.487266944 CGGA_202 AA WHO III Male 41 NA NA 0.369200291111111 0.495074579 CGGA_558 AO WHO III Male 34 188 0 0.204541808888889 0.386412121 CGGA_277 AO WHO III Male 40 4739 0 0.285604732222222 0.448749041 CGGA_364 AO WHO III Male 32 666 1 0.318301634444444 0.498356459 CGGA_508 AO WHO III Female 17 285 1 0.355743638888889 0.464706229 CGGA_260 AO WHO III Female 49 567 1 0.353443064444444 0.452879679 CGGA_414 AO WHO III Male 28 4541 0 0.169933493333333 0.378087974 CGGA_489 AO WHO III Male 31 3804 0 0.318068566666667 0.419365693 CGGA_481 AO WHO III Female 61 1507 1 0.36078337 0.598975605 CGGA_403 AO WHO III Male 43 925 0 0.199006057777778 0.51458225 CGGA_598 AO WHO III Female 61 3677 1 0.308629408888889 0.454294087 CGGA_490 AO WHO III Female 37 3804 0 0.294115327777778 0.458053163 CGGA_474 AO WHO III Male NA 2029 0 0.385258876666667 0.461632861 CGGA_314 rAO WHO III Male 39 2568 1 0.280125821111111 0.43797471 CGGA_329 AA WHO III Male 42 419 1 0.279855051111111 0.436280015 CGGA_352 AA WHO III Male 18 4639 0 0.246578862222222 0.404054252 CGGA_438 AOA WHO III Male 53 686 1 0.240533735555556 0.422851071 CGGA_259 rAA WHO III Female 26 649 0 0.288868234444444 0.475374191 CGGA_279 rAA WHO III Female 49 1314 1 0.266549875555556 0.449398064 CGGA_336 rAO WHO III Female 57 2549 1 0.206919253333333 0.303481788 CGGA_487 rGBM WHO IV Male 46 156 1 0.171206357777778 0.285621795 CGGA_391 AOA WHO III Female 29 2010 1 0.388851674444444 0.381893606 CGGA_232 rAA WHO III Male 28 415 1 0.195086466666667 0.428295392 CGGA_231 AA WHO III Male 60 510 1 0.258753184444444 0.512787659 CGGA_492 rAA WHO III Male 27 652 0 0.232710762222222 0.483454375 CGGA_406 rAA WHO III Female 24 90 1 0.360655467777778 0.525322399 CGGA_393 AOA WHO III Male 65 2190 1 0.2239172 0.345936312 CGGA_471 AA WHO III Male 45 1373 1 0.207067718888889 0.411796926 CGGA_562 AA WHO III Female 62 290 1 0.188723598888889 0.296991143 CGGA_498 AOA WHO III Male 37 356 1 0.357138645555556 0.458276376 CGGA_596 AA WHO III Male 60 1618 1 0.206577235555556 0.350602848 CGGA_351 AA WHO III Female 35 742 1 0.15716386 0.309009839 CGGA_513 AA WHO III Female 44 1374 1 0.345623844444444 0.33940096 CGGA_387 O WHO II Female 51 539 1 0.181689045555556 0.35689115 CGGA_579 O WHO II Female 42 2125 1 0.158282438888889 0.4018347231 CGGA_323 O WHO II Female 48 4338 0 0.29333272 0.46424724 CGGA_484 O WHO II Female 32 4442 0 0.287870814444444 0.467130752 CGGA_485 O WHO II Male 31 2437 0 0.224863397777778 0.391115453 CGGA_639 O WHO II Male 47 4256 0 0.274691931111111 0.478969872 CGGA_672 O WHO II Male 22 4215 0 0.206289477777778 0.379886299 CGGA_633 O WHO II Female 56 1812 1 0.390677548888889 0.544866563 CGGA_589 O WHO II Male 33 4320 0 0.26065713 0.535369539 CGGA_446 O WHO II Female 35 4508 0 0.379195104444444 0.551111631 CGGA_659 O WHO II Female 50 730 0 0.262426134444444 0.377661323 CGGA_275 O WHO II Female 43 4722 0 0.339259755555556 0.32286467 CGGA_543 O WHO II Male 43 4371 0 0.35900082 0.524860907 CGGA_41 AA WHO III Male 53 94 1 0.309364905555556 0.32936105 CGGA_81 rGBM WHO IV Male 39 551 1 0.24409383 0.406371121 CGGA_462 sGBM WHO IV Male 38 361 1 0.183308027777778 0.312102578 CGGA_545 rGBM WHO IV Female 38 555 1 0.337235226666667 0.466954431 CGGA_9 rGBM WHO IV Male 43 408 1 0.247204634444444 0.394591204 CGGA_518 sGBM WHO IV Male 27 212 1 0.20472631 0.423915179 CGGA_104 sGBM WHO IV Male 27 440 0 0.180191163333333 0.377225949 CGGA_220 sGBM WHO IV Male 34 318 1 0.281962876666667 0.431276144 CGGA_822 sGBM WHO IV Male 46 2237 1 0.0701629266666667 0.13437177 CGGA_D59 sGBM WHO IV Female 30 NA NA 0.0701737011111111 0.144464798 CGGA_11 GBM WHO IV Female 57 155 1 0.309241461666667 0.5208120308 CGGA_88 GBM WHO IV Male 20 169 1 0.0605843333333333 0.2546382493 CGGA_156 GBM WHO IV Male 51 179 1 0.265595420222222 0.4799596812 CGGA_161 GBM WHO IV Female 35 329 1 0.0827132033333333 0.273824871 CGGA_182 GBM WHO IV Female 47 128 1 0.0670378055555556 0.2656025115 CGGA_203 GBM WHO IV Male 40 188 1 0.178265207777778 0.4313730501 CGGA_214 GBM WHO IV Female 48 193 1 0.333112673666667 0.5106695827 CGGA_305 GBM WHO IV Female 49 34 1 0.117236956666667 0.4331382742 CGGA_346 GBM WHO IV Male 45 104 1 0.0654127091111111 0.2909813112 CGGA_401 GBM WHO IV Female 24 168 1 0.438367867777778 0.5456384766 CGGA_409 GBM WHO IV Male 63 98 1 0.0447904444444444 0.1837063243 CGGA_525 GBM WHO IV Male 61 138 1 0.0242802482222222 0.1425240359 CGGA_13 GBM WHO IV Male 59 267 1 0.168693875555556 0.3909975346 CGGA_311 GBM WHO IV Male 40 230 1 0.0476996322222222 0.3125524708 CGGA_366 GBM WHO IV Male 54 255 1 0.316539992222222 0.4868649375 CGGA_380 GBM WHO IV Male 52 165 1 0.0501078881111111 0.170745247 CGGA_419 GBM WHO IV Male 56 198 1 0.0442798517777778 0.1929411766 CGGA_444 GBM WHO IV Female 70 225 1 0.0438257701111111 0.2755094379 CGGA_557 GBM WHO IV Female 38 257 1 0.442033829111111 0.5363383353 CGGA_593 GBM WHO IV Male 37 242 1 0.0465396832222222 0.3082272938 CGGA_1 GBM WHO IV Female 43 605 1 0.306795896666667 0.5103926977 CGGA_31 GBM WHO IV Female 9 5127 0 0.13044653 0.3460309961 CGGA_58 GBM WHO IV Male 60 570 1 0.0344827421111111 0.2922200934 CGGA_126 GBM WHO IV Female 50 1177 1 0.286178645777778 0.477278301 CGGA_130 GBM WHO IV Male 45 1262 1 0.156367232 0.396949921 CGGA_139 GBM WHO IV Male 59 694 1 0.0591795455555556 0.2358843735 CGGA_168 GBM WHO IV Male 17 3086 0 0.0590823625555556 0.2459607182 CGGA_178 GBM WHO IV Male 38 972 1 0.254899748444444 0.4705237549 CGGA_225 GBM WHO IV Male 32 1741 1 0.0428181011111111 0.3168126403 CGGA_255 GBM WHO IV Female 42 591 1 0.0444218336666667 0.1402417843 CGGA_308 GBM WHO IV Female 55 823 1 0.061237461 0.2950276653 CGGA_439 GBM WHO IV Female 33 3813 1 0.155424452222222 0.5031757673 CGGA_547 GBM WHO IV Male 29 1337 1 0.126624372222222 0.4283766473
Args <- commandArgs(T) data.file<-"data.txt" geneA<-"CDKN2A" geneB<-"CDKN2B" ## load R package library(ggplot2) library(ggpubr) library(gridExtra) ## import data dat<-read.table(data.file, sep='\t', head=T) rownames(dat)<-dat$CGGA.ID #head(dat) mat<-dat[!is.na(dat$Histology)&dat$Histology!="Normal"& !is.na(dat$Grade)& !is.na(dat$Gender)& !is.na(dat$Age),] mat$Gene.A.Methylation<-mat$Gene.A.Methylation mat$Gene.B.Methylation<-mat$Gene.B.Methylation ### all tmp.data<-mat cortest<-cor.test(tmp.data$Gene.A.Methylation,tmp.data$Gene.B.Methylation) R.value<-round(cortest$estimate[[1]],3) P.value<-format(cortest$p.value,scientific=T,digits=3) ptext<-paste0("R = ",R.value,", P = ",P.value) dat_text <- data.frame(Gene.A.Methylation=mean(tmp.data$Gene.A.Methylation), Gene.B.Methylation=max(tmp.data$Gene.B.Methylation)*1.1, label = ptext, IDH.mutation.status=NA, Histology=NA, Grade = NA, fill_color=NA, CGGA.ID=NA, X1p19q.codel.status=NA, Age=NA, Gender=NA, PRS.type=NA ) plot1<-ggplot(tmp.data,aes(x=Gene.A.Methylation, y=Gene.B.Methylation, Sample=CGGA.ID, Histology=Histology, Grade=Grade, Age=Age, Gender=Gender))+ geom_point()+ geom_text(data=dat_text,aes(label = label))+ geom_smooth(formula = y~x,aes(group=1),method='lm')+ xlab(paste0('Methylation level of ',geneA))+ ylab(paste0('Methylation level of ',geneB))+ labs(title = 'All WHO grade glioma')+ theme(text = element_text(size=10)) ### 2. WHO II tmp.data<-mat[mat$Grade=="WHO II",] cortest<-cor.test(tmp.data$Gene.A.Methylation,tmp.data$Gene.B.Methylation) R.value<-round(cortest$estimate[[1]],3) P.value<-format(cortest$p.value,scientific=T,digits=3) ptext<-paste0("R = ",R.value,", P = ",P.value) dat_text <- data.frame(Gene.A.Methylation=mean(tmp.data$Gene.A.Methylation), Gene.B.Methylation=max(tmp.data$Gene.B.Methylation)*1.1, label = ptext, IDH.mutation.status=NA, Histology=NA, Grade = NA, fill_color=NA, CGGA.ID=NA, X1p19q.codel.status=NA, Age=NA, Gender=NA, PRS.type=NA ) plot2<-ggplot(tmp.data,aes(x=Gene.A.Methylation, y=Gene.B.Methylation, Sample=CGGA.ID, Histology=Histology, Grade=Grade, Age=Age, Gender=Gender))+ geom_point()+ geom_text(data=dat_text,aes(label = label))+ geom_smooth(formula = y~x,aes(group=1),method='lm')+ xlab(paste0('Methylation level of ',geneA))+ ylab(paste0('Methylation level of ',geneB))+ labs(title = 'WHO grade II glioma')+ theme(text = element_text(size=10)) ### 3.III tmp.data<-mat[mat$Grade=="WHO III",] cortest<-cor.test(tmp.data$Gene.A.Methylation,tmp.data$Gene.B.Methylation) R.value<-round(cortest$estimate[[1]],3) P.value<-format(cortest$p.value,scientific=T,digits=3) ptext<-paste0("R = ",R.value,", P = ",P.value) dat_text <- data.frame(Gene.A.Methylation=mean(tmp.data$Gene.A.Methylation), Gene.B.Methylation=max(tmp.data$Gene.B.Methylation)*1.1, label = ptext, IDH.mutation.status=NA, Histology=NA, Grade = NA, fill_color=NA, CGGA.ID=NA, X1p19q.codel.status=NA, Age=NA, Gender=NA, PRS.type=NA ) plot3<-ggplot(tmp.data,aes(x=Gene.A.Methylation, y=Gene.B.Methylation, Sample=CGGA.ID, Histology=Histology, Grade=Grade, Age=Age, Gender=Gender))+ geom_point()+ geom_text(data=dat_text,aes(label = label))+ geom_smooth(formula = y~x,aes(group=1),method='lm')+ xlab(paste0('Methylation level of ',geneA))+ ylab(paste0('Methylation level of ',geneB))+ labs(title = 'WHO grade III glioma')+ theme(text = element_text(size=10)) ### 4.IV tmp.data<-mat[mat$Grade=="WHO IV",] cortest<-cor.test(tmp.data$Gene.A.Methylation,tmp.data$Gene.B.Methylation) R.value<-round(cortest$estimate[[1]],3) P.value<-format(cortest$p.value,scientific=T,digits=3) ptext<-paste0("R = ",R.value,", P = ",P.value) dat_text <- data.frame(Gene.A.Methylation=mean(tmp.data$Gene.A.Methylation), Gene.B.Methylation=max(tmp.data$Gene.B.Methylation)*1.1, label = ptext, IDH.mutation.status=NA, Histology=NA, Grade = NA, fill_color=NA, CGGA.ID=NA, X1p19q.codel.status=NA, Age=NA, Gender=NA, PRS.type=NA ) plot4<-ggplot(tmp.data,aes(x=Gene.A.Methylation, y=Gene.B.Methylation, Sample=CGGA.ID, Histology=Histology, Grade=Grade, Age=Age, Gender=Gender))+ geom_point()+ geom_text(data=dat_text,aes(label = label))+ geom_smooth(formula = y~x,aes(group=1),method='lm')+ xlab(paste0('Methylation level of ',geneA))+ ylab(paste0('Methylation level of ',geneB))+ labs(title = 'WHO grade IV glioma')+ theme(text = element_text(size=10)) ## output pdf grid.arrange(plot1, plot2, plot3, plot4, ncol = 2,nrow=2)
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