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WEseq data
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methylation data
microRNA data
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Analyse result - microRNA data (correlation)
Dataset:
microRNA_array_198
microRNA A:
hsa-miR-15b
microRNA B:
hsa-miR-21
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Plot (Beta)
Data
R code
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CGGA.ID Histology Grade Gender Age OS Censor microRNA.A.Expression microRNA.B.Expression CGGA_253 A WHO II Female 37 5159 0 7609.594 25599.01 CGGA_258 A WHO II Female 61 663 1 2689.349 21557.71 CGGA_281 A WHO II Female 36 1236 0 350.2077 262.5339 CGGA_285 A WHO II Male 30 1576 0 10310.43 17039.76 CGGA_316 A WHO II Female 30 4682 0 8136.912 25599.01 CGGA_317 A WHO II Female 42 4681 0 4183.457 17761.38 CGGA_321 A WHO II Male 42 NA NA 3887.555 17280.88 CGGA_357 A WHO II Female 37 2518 1 3140.002 18253.31 CGGA_365 A WHO II Female 32 3593 0 2308.362 12230.8 CGGA_379 A WHO II Female 39 1077 1 4939.933 11404.41 CGGA_396 A WHO II Female 49 4576 0 4974.688 12906.46 CGGA_399 A WHO II Male 36 2663 1 6485.778 20387.83 CGGA_402 AA WHO III Male 53 4562 0 279.139 17039.76 CGGA_407 A WHO II Male 33 4555 0 4902.749 16350.39 CGGA_433 A WHO II Male 30 NA NA 1307.813 21150.38 CGGA_434 A WHO II Male 35 4513 0 7320.847 24471.77 CGGA_447 A WHO II Female 20 4508 0 927.9802 13402.89 CGGA_459 A WHO II Male 44 3613 0 11187.24 25599.01 CGGA_461 A WHO II Male 36 1266 1 20.86006 11868.88 CGGA_469 A WHO II Male 30 4464 0 7864.118 20387.83 CGGA_505 A WHO II Male 36 2994 1 6668.907 24471.77 CGGA_522 A WHO II Female 24 3630 0 705.7141 14423.27 CGGA_542 A WHO II Male 24 NA NA 7927.285 14423.27 CGGA_544 A WHO II Female 44 4371 0 13732.26 24471.77 CGGA_548 A WHO II Male 39 NA NA 10745.25 25599.01 CGGA_552 A WHO II Female 36 4362 0 11738.86 23528.36 CGGA_590 A WHO II Male 36 4319 0 5673.916 21557.71 CGGA_592 A WHO II Male 47 4315 0 1854.954 20019.25 CGGA_595 A WHO II Female 51 NA NA 4183.457 14988.82 CGGA_601 A WHO II Male 36 1133 1 7056.126 23528.36 CGGA_626 A WHO II Male 20 4273 0 6295.396 16158.83 CGGA_648 A WHO II Female 33 3647 1 6441.13 15757.22 CGGA_663 A WHO II Female 29 4210 0 4683.242 21150.38 CGGA_673 AA WHO III Male 54 NA NA 6857.684 22727.25 CGGA_688 A WHO II Male 19 4196 0 14423.27 22727.25 CGGA_692 AOA WHO III Male 19 NA NA 5632.431 22727.25 CGGA_708 A WHO II Female 38 4167 0 7109.204 25599.01 CGGA_711 A WHO II Male 49 1798 0 11868.88 18508.16 CGGA_712 A WHO II Male 25 NA NA 6958.646 20387.83 CGGA_718 A WHO II Male 17 4159 0 6580.427 25599.01 CGGA_736 A WHO II Male 38 1164 1 3696.362 19031.08 CGGA_743 A WHO II Male 30 851 1 11738.86 22727.25 CGGA_746 A WHO II Male 45 332 1 9355.839 20387.83 CGGA_753 A WHO II Male 24 3217 1 4721.895 15757.22 CGGA_767 AA WHO III Male 36 1310 1 10310.43 21150.38 CGGA_770 A WHO II Male 30 1057 0 5755.375 18767.88 CGGA_776 A WHO II Female 48 4091 0 5310.246 17761.38 CGGA_781 rA WHO II Male 35 4088 0 3334.504 16800.89 CGGA_792 A WHO II Male 32 816 1 7320.847 21557.71 CGGA_275 O WHO II Female 43 4722 0 494.6068 10745.25 CGGA_323 O WHO II Female 48 4338 0 5755.375 16350.39 CGGA_387 O WHO II Female 51 539 1 10310.43 19697.88 CGGA_446 O WHO II Female 35 4508 0 5970.641 20019.25 CGGA_484 O WHO II Female 32 4442 0 9029.006 15540.49 CGGA_485 O WHO II Male 31 2437 0 8341.135 12353.38 CGGA_543 O WHO II Male 43 4371 0 10409.28 24471.77 CGGA_579 O WHO II Female 42 2125 1 13402.89 22090.16 CGGA_589 O WHO II Male 33 4320 0 11868.88 16800.89 CGGA_633 O WHO II Female 56 1812 1 4683.242 20751.5 CGGA_639 O WHO II Male 47 4256 0 4974.688 15757.22 CGGA_659 O WHO II Female 50 730 0 4974.688 19340.83 CGGA_672 O WHO II Male 22 4215 0 5718.289 23528.36 CGGA_41 AA WHO III Male 53 94 1 5928.5 15540.49 CGGA_202 AA WHO III Male 41 NA NA 7492.311 15358.48 CGGA_247 AA WHO III Male 60 609 1 6341.422 14799.65 CGGA_249 AA WHO III Male 41 312 1 13566.81 22090.16 CGGA_331 AA WHO III Female 27 1638 1 3074.53 25599.01 CGGA_334 AA WHO III Female 35 1450 1 3008.753 18767.88 CGGA_353 AA WHO III Female 60 516 1 3769.654 16158.83 CGGA_354 AA WHO III Male 62 180 1 4443.182 19340.83 CGGA_405 AA WHO III Male 59 497 1 8068.169 17530.25 CGGA_410 AA WHO III Female 70 1099 1 4409.101 14423.27 CGGA_412 AA WHO III Female 42 358 1 2689.349 17530.25 CGGA_476 A WHO II Female 37 936 1 6150.667 21557.71 CGGA_486 AA WHO III Male 23 424 1 5196.646 19697.88 CGGA_260 AO WHO III Female 49 567 1 7997.195 19697.88 CGGA_277 AO WHO III Male 40 4739 0 7434.328 19340.83 CGGA_314 rAO WHO III Male 39 2568 1 4215.663 19697.88 CGGA_364 AO WHO III Male 32 666 1 9029.006 20387.83 CGGA_403 AO WHO III Male 43 925 0 4649.482 17761.38 CGGA_414 AO WHO III Male 28 4541 0 11187.24 20387.83 CGGA_474 AO WHO III Male NA 2029 0 14061.22 18508.16 CGGA_481 AO WHO III Female 61 1507 1 9531.431 19340.83 CGGA_489 AO WHO III Male 31 3804 0 3854.834 25599.01 CGGA_490 AO WHO III Female 37 3804 0 3854.834 22090.16 CGGA_508 AO WHO III Female 17 285 1 7320.847 18508.16 CGGA_558 AO WHO III Male 34 188 0 5048.293 20387.83 CGGA_598 AO WHO III Female 61 3677 1 3457.19 17530.25 CGGA_231 AA WHO III Male 60 510 1 5350.444 23528.36 CGGA_232 rAA WHO III Male 28 415 1 3719.83 17530.25 CGGA_259 rAA WHO III Female 26 649 0 3307.882 20387.83 CGGA_279 rAA WHO III Female 49 1314 1 3484.542 18020.93 CGGA_329 AA WHO III Male 42 419 1 7609.594 21557.71 CGGA_336 rAO WHO III Female 57 2549 1 5234.688 17761.38 CGGA_351 AA WHO III Female 35 742 1 3140.002 16350.39 CGGA_352 AA WHO III Male 18 4639 0 2627.646 16158.83 CGGA_391 AOA WHO III Female 29 2010 1 3854.834 22727.25 CGGA_393 AOA WHO III Male 65 2190 1 7220.182 20019.25 CGGA_406 rAA WHO III Female 24 90 1 8341.135 15540.49 CGGA_438 AOA WHO III Male 53 686 1 3915.676 22090.16 CGGA_471 AA WHO III Male 45 1373 1 12773.68 21557.71 CGGA_487 rGBM WHO IV Male 46 156 1 3674.607 18020.93 CGGA_492 rAA WHO III Male 27 652 0 11513.44 24471.77 CGGA_498 AOA WHO III Male 37 356 1 5471.8 21150.38 CGGA_513 AA WHO III Female 44 1374 1 6195.307 15944.5 CGGA_562 AA WHO III Female 62 290 1 8476.147 22727.25 CGGA_596 AA WHO III Male 60 1618 1 6532.001 19340.83 CGGA_1 GBM WHO IV Female 43 605 1 9707.174 25599.01 CGGA_11 GBM WHO IV Female 57 155 1 8476.147 24471.77 CGGA_13 GBM WHO IV Male 59 267 1 8947.796 24471.77 CGGA_24 GBM WHO IV Male 48 387 1 10957.7 28912.2 CGGA_58 GBM WHO IV Male 60 570 1 11404.41 22727.25 CGGA_88 GBM WHO IV Male 20 169 1 8781.178 22727.25 CGGA_100 GBM WHO IV Male 47 335 1 12906.46 28912.2 CGGA_102 GBM WHO IV Male 17 214 1 8619.538 24471.77 CGGA_124 GBM WHO IV Male 53 414 1 5632.431 22727.25 CGGA_126 GBM WHO IV Female 50 1177 1 10622.41 28912.2 CGGA_139 GBM WHO IV Male 59 694 1 7802.637 25599.01 CGGA_144 GBM WHO IV Male 54 363 1 10109.78 24471.77 CGGA_156 GBM WHO IV Male 51 179 1 11997.08 22727.25 CGGA_160 OA WHO II Female 29 4982 0 7927.285 22727.25 CGGA_161 GBM WHO IV Female 35 329 1 5196.646 17761.38 CGGA_168 GBM WHO IV Male 17 3086 0 9809.766 28912.2 CGGA_169 GBM WHO IV Male 59 450 1 12230.8 28912.2 CGGA_172 GBM WHO IV Female 57 462 1 12230.8 23528.36 CGGA_178 GBM WHO IV Male 38 972 1 9193.826 21557.71 CGGA_182 GBM WHO IV Female 47 128 1 11868.88 25599.01 CGGA_188 GBM WHO IV Male 51 397 1 11632.21 28912.2 CGGA_195 GBM WHO IV Male 48 486 1 10846.19 22727.25 CGGA_203 GBM WHO IV Male 40 188 1 10622.41 25599.01 CGGA_205 GBM WHO IV Male 65 292 1 11064.83 22727.25 CGGA_210 GBM WHO IV Female 68 300 1 10310.43 22727.25 CGGA_214 GBM WHO IV Female 48 193 1 14799.65 24471.77 CGGA_218 GBM WHO IV Male 12 313 1 9809.766 28912.2 CGGA_221 GBM WHO IV Male 37 287 1 9707.174 24471.77 CGGA_222 GBM WHO IV Male 51 520 1 11187.24 25599.01 CGGA_225 GBM WHO IV Male 32 1741 1 11513.44 28912.2 CGGA_238 GBM WHO IV Male 53 275 1 10846.19 25599.01 CGGA_240 GBM WHO IV Male 54 386 1 11997.08 28912.2 CGGA_242 GBM WHO IV Male 62 382 1 11404.41 24471.77 CGGA_255 GBM WHO IV Female 42 591 1 16158.83 13.18194 CGGA_264 GBM WHO IV Male 54 383 1 13732.26 24471.77 CGGA_287 GBM WHO IV Female 39 571 1 13566.81 28912.2 CGGA_292 GBM WHO IV Male 37 812 1 11632.21 23528.36 CGGA_306 GBM WHO IV Male 59 901 1 14988.82 24471.77 CGGA_308 GBM WHO IV Female 55 823 1 9355.839 22727.25 CGGA_311 GBM WHO IV Male 40 230 1 8277.667 22727.25 CGGA_324 GBM WHO IV Male 52 336 1 12906.46 23528.36 CGGA_335 GBM WHO IV Male 59 385 1 13044.76 25599.01 CGGA_342 GBM WHO IV Female 49 500 1 11513.44 25599.01 CGGA_345 GBM WHO IV Female 43 1547 0 11064.83 25599.01 CGGA_346 GBM WHO IV Male 45 104 1 11738.86 28912.2 CGGA_349 GBM WHO IV Female 49 869 0 10212.88 23528.36 CGGA_366 GBM WHO IV Male 54 255 1 9444.813 28912.2 CGGA_370 GBM WHO IV Male 51 338 1 9193.826 28912.2 CGGA_371 GBM WHO IV Male 42 811 1 9707.174 25599.01 CGGA_373 GBM WHO IV Female 45 281 1 10504.15 23528.36 CGGA_375 GBM WHO IV Male 57 657 1 15540.49 24471.77 CGGA_377 GBM WHO IV Male 17 398 1 12230.8 22090.16 CGGA_380 GBM WHO IV Male 52 165 1 11404.41 25599.01 CGGA_401 GBM WHO IV Female 24 168 1 12906.46 23528.36 CGGA_409 GBM WHO IV Male 63 98 1 11997.08 25599.01 CGGA_419 GBM WHO IV Male 56 198 1 10622.41 28912.2 CGGA_436 GBM WHO IV Male 48 955 1 10504.15 28912.2 CGGA_437 GBM WHO IV Female 54 1025 1 8547.053 28912.2 CGGA_439 GBM WHO IV Female 33 3813 1 7220.182 24471.77 CGGA_442 GBM WHO IV Male 62 1555 1 10957.7 28912.2 CGGA_444 GBM WHO IV Female 70 225 1 6715.53 22727.25 CGGA_451 GBM WHO IV Male 61 795 1 9912.727 28912.2 CGGA_454 GBM WHO IV Male 37 412 1 14423.27 28912.2 CGGA_464 GBM WHO IV Female 39 403 1 10622.41 28912.2 CGGA_504 GBM WHO IV Female 22 563 1 9707.174 28912.2 CGGA_512 GBM WHO IV Male 33 4371 0 9029.006 28912.2 CGGA_525 GBM WHO IV Male 61 138 1 11404.41 28912.2 CGGA_527 GBM WHO IV Male 33 439 1 13903.78 25599.01 CGGA_547 GBM WHO IV Male 29 1337 1 10622.41 28912.2 CGGA_549 GBM WHO IV Female 34 413 1 8547.053 24471.77 CGGA_557 GBM WHO IV Female 38 257 1 8947.796 28912.2 CGGA_570 GBM WHO IV Female 60 3602 0 15167.79 25599.01 CGGA_573 GBM WHO IV Male 35 946 1 12486.94 25599.01 CGGA_575 GBM WHO IV Male 55 551 1 12617.01 24471.77 CGGA_588 GBM WHO IV Male 58 773 1 9616.705 25599.01 CGGA_593 GBM WHO IV Male 37 242 1 11290.62 28912.2 CGGA_594 GBM WHO IV Female 27 4318 0 10011.48 25599.01 CGGA_597 GBM WHO IV Female 31 733 1 12230.8 28912.2 CGGA_604 GBM WHO IV Male 46 381 1 9531.431 28912.2 CGGA_606 GBM WHO IV Male 62 325 1 11187.24 25599.01 CGGA_609 GBM WHO IV Female 44 512 0 10011.48 21150.38 CGGA_612 GBM WHO IV Male 47 2023 1 9444.813 22727.25 CGGA_9 rGBM WHO IV Male 43 408 1 4550.731 17280.88 CGGA_81 rGBM WHO IV Male 39 551 1 15358.48 24471.77 CGGA_104 sGBM WHO IV Male 27 440 0 7864.118 19340.83 CGGA_220 sGBM WHO IV Male 34 318 1 6958.646 20751.5 CGGA_462 sGBM WHO IV Male 38 361 1 8405.654 21150.38 CGGA_518 sGBM WHO IV Male 27 212 1 8201.735 20751.5 CGGA_545 rGBM WHO IV Female 38 555 1 7056.126 20019.25 CGGA_822 sGBM WHO IV Male 46 2237 1 5471.8 16568.29 CGGA_D59 sGBM WHO IV Female 30 NA NA 11290.62 25599.01
data.file<-"data.txt" geneA<-"hsa-miR-15b" geneB<-"hsa-miR-21" ## 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),] ### all tmp.data<-mat cortest<-cor.test(tmp.data$microRNA.A.Expression,tmp.data$microRNA.B.Expression) 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(microRNA.A.Expression=mean(tmp.data$microRNA.A.Expression), microRNA.B.Expression=max(tmp.data$microRNA.B.Expression)*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=microRNA.A.Expression, y=microRNA.B.Expression, 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('Expression level',geneA))+ ylab(paste0('Expression level',geneB))+ labs(title = 'All WHO grade glioma')+ theme(text = element_text(size=10)) ### 2. WHO II tmp.data<-mat[mat$Grade=="WHO II",] cortest<-cor.test(tmp.data$microRNA.A.Expression,tmp.data$microRNA.B.Expression) R.value<-round(cortest$estimate[[1]],3) P.value<-format(cortest$p.value,scientific=T,digits=3) ptext<-paste0("R = ",R.value,", P = ",P.value) dat_text <- data.frame(microRNA.A.Expression=mean(tmp.data$microRNA.A.Expression), microRNA.B.Expression=max(tmp.data$microRNA.B.Expression)*1.1, label = ptext, IDH.mutation.status=NA, Histology=NA, Grade = NA, fill_color=NA, CGGA.ID=NA, X1p19q.codel.status=NA, Age=NA, Gender=NA, PRS.type=NA ) plot2<-ggplot(tmp.data,aes(x=microRNA.A.Expression, y=microRNA.B.Expression, Sample=CGGA.ID, Histology=Histology, Grade=Grade, Age=Age, Gender=Gender))+ geom_point()+ geom_text(data=dat_text,aes(label = label))+ geom_smooth(formula = y~x,aes(group=1),method='lm')+ xlab(paste0('Expression level',geneA))+ ylab(paste0('Expression level',geneB))+ labs(title = 'WHO grade II glioma')+ theme(text = element_text(size=10)) ### 3.III tmp.data<-mat[mat$Grade=="WHO III",] cortest<-cor.test(tmp.data$microRNA.A.Expression,tmp.data$microRNA.B.Expression) 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(microRNA.A.Expression=mean(tmp.data$microRNA.A.Expression), microRNA.B.Expression=max(tmp.data$microRNA.B.Expression)*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=microRNA.A.Expression, y=microRNA.B.Expression, 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('Expression level',geneA))+ ylab(paste0('Expression level',geneB))+ labs(title = 'WHO grade III glioma')+ theme(text = element_text(size=10)) ### 4.IV tmp.data<-mat[mat$Grade=="WHO IV",] cortest<-cor.test(tmp.data$microRNA.A.Expression,tmp.data$microRNA.B.Expression) R.value<-round(cortest$estimate[[1]],3) P.value<-format(cortest$p.value,scientific=T,digits=3) ptext<-paste0("R = ",R.value,", P = ",P.value) dat_text <- data.frame(microRNA.A.Expression=mean(tmp.data$microRNA.A.Expression), microRNA.B.Expression=max(tmp.data$microRNA.B.Expression)*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=microRNA.A.Expression, y=microRNA.B.Expression, 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('Expression level',geneA))+ ylab(paste0('Expression level',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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