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Analyse result - microRNAseq data (distribution)
Dataset:
microRNA_array_198
microRNA:
hsa-miR-29c
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CGGA.ID PRS.type Histology Grade Gender Age OS Censor Expression CGGA_253 Primary A WHO II Female 37 5159 0 10622.41 CGGA_258 Primary A WHO II Female 61 663 1 6668.907 CGGA_281 Primary A WHO II Female 36 1236 0 1869.148 CGGA_285 Primary A WHO II Male 30 1576 0 6624.62 CGGA_316 Primary A WHO II Female 30 4682 0 6241.384 CGGA_317 Primary A WHO II Female 42 4681 0 5842.255 CGGA_321 Primary A WHO II Male 42 NA NA 4939.933 CGGA_357 Primary A WHO II Female 37 2518 1 11187.24 CGGA_365 Primary A WHO II Female 32 3593 0 7546.049 CGGA_379 Primary A WHO II Female 39 1077 1 3051.508 CGGA_396 Primary A WHO II Female 49 4576 0 9444.813 CGGA_399 Primary A WHO II Male 36 2663 1 7997.195 CGGA_402 Primary AA WHO III Male 53 4562 0 2751.624 CGGA_407 Primary A WHO II Male 33 4555 0 7546.049 CGGA_433 Primary A WHO II Male 30 NA NA 8947.796 CGGA_434 Primary A WHO II Male 35 4513 0 7677.65 CGGA_447 Primary A WHO II Female 20 4508 0 1341.847 CGGA_459 Primary A WHO II Male 44 3613 0 6857.684 CGGA_461 Primary A WHO II Male 36 1266 1 7677.65 CGGA_469 Primary A WHO II Male 30 4464 0 7927.285 CGGA_505 Primary A WHO II Male 36 2994 1 11404.41 CGGA_522 Primary A WHO II Female 24 3630 0 9912.727 CGGA_542 Primary A WHO II Male 24 NA NA 8703.474 CGGA_544 Primary A WHO II Female 44 4371 0 5513.285 CGGA_548 Primary A WHO II Male 39 NA NA 9444.813 CGGA_552 Primary A WHO II Female 36 4362 0 4038.693 CGGA_590 Primary A WHO II Male 36 4319 0 8341.135 CGGA_592 Primary A WHO II Male 47 4315 0 10109.78 CGGA_595 Primary A WHO II Female 51 NA NA 9279.122 CGGA_601 Primary A WHO II Male 36 1133 1 7109.204 CGGA_626 Primary A WHO II Male 20 4273 0 6908.778 CGGA_648 Primary A WHO II Female 33 3647 1 3410.727 CGGA_663 Primary A WHO II Female 29 4210 0 6624.62 CGGA_673 Primary AA WHO III Male 54 NA NA 8277.667 CGGA_688 Primary A WHO II Male 19 4196 0 8405.654 CGGA_692 Primary AOA WHO III Male 19 NA NA 9279.122 CGGA_708 Primary A WHO II Female 38 4167 0 8341.135 CGGA_711 Primary A WHO II Male 49 1798 0 6812.272 CGGA_712 Primary A WHO II Male 25 NA NA 6341.422 CGGA_718 Primary A WHO II Male 17 4159 0 8781.178 CGGA_736 Primary A WHO II Male 38 1164 1 7609.594 CGGA_743 Primary A WHO II Male 30 851 1 5755.375 CGGA_746 Primary A WHO II Male 45 332 1 7735.774 CGGA_753 Primary A WHO II Male 24 3217 1 6295.396 CGGA_767 Primary AA WHO III Male 36 1310 1 176.4437 CGGA_770 Primary A WHO II Male 30 1057 0 7864.118 CGGA_776 Primary A WHO II Female 48 4091 0 6150.667 CGGA_781 Recurrent rA WHO II Male 35 4088 0 8781.178 CGGA_792 Primary A WHO II Male 32 816 1 16800.89 CGGA_275 Primary O WHO II Female 43 4722 0 9193.826 CGGA_323 Primary O WHO II Female 48 4338 0 9029.006 CGGA_387 Primary O WHO II Female 51 539 1 9444.813 CGGA_446 Primary O WHO II Female 35 4508 0 4124.439 CGGA_484 Primary O WHO II Female 32 4442 0 10310.43 CGGA_485 Primary O WHO II Male 31 2437 0 11738.86 CGGA_543 Primary O WHO II Male 43 4371 0 8405.654 CGGA_579 Primary O WHO II Female 42 2125 1 5755.375 CGGA_589 Primary O WHO II Male 33 4320 0 4683.242 CGGA_633 Primary O WHO II Female 56 1812 1 6532.001 CGGA_639 Primary O WHO II Male 47 4256 0 8136.912 CGGA_659 Primary O WHO II Female 50 730 0 7802.637 CGGA_672 Primary O WHO II Male 22 4215 0 10011.48 CGGA_41 Primary AA WHO III Male 53 94 1 8341.135 CGGA_202 Primary AA WHO III Male 41 NA NA 12906.46 CGGA_247 Primary AA WHO III Male 60 609 1 13903.78 CGGA_249 Primary AA WHO III Male 41 312 1 8201.735 CGGA_331 Primary AA WHO III Female 27 1638 1 17530.25 CGGA_334 Primary AA WHO III Female 35 1450 1 20751.5 CGGA_353 Primary AA WHO III Female 60 516 1 19031.08 CGGA_354 Primary AA WHO III Male 62 180 1 18508.16 CGGA_405 Primary AA WHO III Male 59 497 1 11632.21 CGGA_410 Primary AA WHO III Female 70 1099 1 18508.16 CGGA_412 Primary AA WHO III Female 42 358 1 19340.83 CGGA_476 Primary A WHO II Female 37 936 1 14988.82 CGGA_486 Primary AA WHO III Male 23 424 1 12773.68 CGGA_260 Primary AO WHO III Female 49 567 1 15167.79 CGGA_277 Primary AO WHO III Male 40 4739 0 8703.474 CGGA_314 Recurrent rAO WHO III Male 39 2568 1 15757.22 CGGA_364 Primary AO WHO III Male 32 666 1 14423.27 CGGA_403 Primary AO WHO III Male 43 925 0 17280.88 CGGA_414 Primary AO WHO III Male 28 4541 0 8405.654 CGGA_474 Primary AO WHO III Male NA 2029 0 3537.251 CGGA_481 Primary AO WHO III Female 61 1507 1 14988.82 CGGA_489 Primary AO WHO III Male 31 3804 0 14799.65 CGGA_490 Primary AO WHO III Female 37 3804 0 14799.65 CGGA_508 Primary AO WHO III Female 17 285 1 16158.83 CGGA_558 Primary AO WHO III Male 34 188 0 13732.26 CGGA_598 Primary AO WHO III Female 61 3677 1 19031.08 CGGA_231 Primary AA WHO III Male 60 510 1 19697.88 CGGA_232 Recurrent rAA WHO III Male 28 415 1 10310.43 CGGA_259 Recurrent rAA WHO III Female 26 649 0 20019.25 CGGA_279 Recurrent rAA WHO III Female 49 1314 1 20019.25 CGGA_329 Primary AA WHO III Male 42 419 1 8476.147 CGGA_336 Recurrent rAO WHO III Female 57 2549 1 11404.41 CGGA_351 Primary AA WHO III Female 35 742 1 19697.88 CGGA_352 Primary AA WHO III Male 18 4639 0 21150.38 CGGA_391 Primary AOA WHO III Female 29 2010 1 17039.76 CGGA_393 Primary AOA WHO III Male 65 2190 1 16158.83 CGGA_406 Recurrent rAA WHO III Female 24 90 1 14061.22 CGGA_438 Primary AOA WHO III Male 53 686 1 12773.68 CGGA_471 Primary AA WHO III Male 45 1373 1 11868.88 CGGA_487 Recurrent rGBM WHO IV Male 46 156 1 18767.88 CGGA_492 Recurrent rAA WHO III Male 27 652 0 7677.65 CGGA_498 Primary AOA WHO III Male 37 356 1 10957.7 CGGA_513 Primary AA WHO III Female 44 1374 1 17039.76 CGGA_562 Primary AA WHO III Female 62 290 1 11738.86 CGGA_596 Primary AA WHO III Male 60 1618 1 18508.16 CGGA_1 Primary GBM WHO IV Female 43 605 1 16568.29 CGGA_11 Primary GBM WHO IV Female 57 155 1 11290.62 CGGA_13 Primary GBM WHO IV Male 59 267 1 15757.22 CGGA_24 Primary GBM WHO IV Male 48 387 1 15167.79 CGGA_58 Primary GBM WHO IV Male 60 570 1 15944.5 CGGA_88 Primary GBM WHO IV Male 20 169 1 8947.796 CGGA_100 Primary GBM WHO IV Male 47 335 1 13903.78 CGGA_102 Primary GBM WHO IV Male 17 214 1 11187.24 CGGA_124 Primary GBM WHO IV Male 53 414 1 18020.93 CGGA_126 Primary GBM WHO IV Female 50 1177 1 12353.38 CGGA_139 Primary GBM WHO IV Male 59 694 1 20019.25 CGGA_144 Primary GBM WHO IV Male 54 363 1 14988.82 CGGA_156 Primary GBM WHO IV Male 51 179 1 13044.76 CGGA_160 Primary OA WHO II Female 29 4982 0 16800.89 CGGA_161 Primary GBM WHO IV Female 35 329 1 18253.31 CGGA_168 Primary GBM WHO IV Male 17 3086 0 15944.5 CGGA_169 Primary GBM WHO IV Male 59 450 1 10745.25 CGGA_172 Primary GBM WHO IV Female 57 462 1 22090.16 CGGA_178 Primary GBM WHO IV Male 38 972 1 22090.16 CGGA_182 Primary GBM WHO IV Female 47 128 1 17280.88 CGGA_188 Primary GBM WHO IV Male 51 397 1 14988.82 CGGA_195 Primary GBM WHO IV Male 48 486 1 21557.71 CGGA_203 Primary GBM WHO IV Male 40 188 1 15540.49 CGGA_205 Primary GBM WHO IV Male 65 292 1 22090.16 CGGA_210 Primary GBM WHO IV Female 68 300 1 23528.36 CGGA_214 Primary GBM WHO IV Female 48 193 1 14988.82 CGGA_218 Primary GBM WHO IV Male 12 313 1 14061.22 CGGA_221 Primary GBM WHO IV Male 37 287 1 18508.16 CGGA_222 Primary GBM WHO IV Male 51 520 1 16158.83 CGGA_225 Primary GBM WHO IV Male 32 1741 1 12773.68 CGGA_238 Primary GBM WHO IV Male 53 275 1 15540.49 CGGA_240 Primary GBM WHO IV Male 54 386 1 14631.81 CGGA_242 Primary GBM WHO IV Male 62 382 1 15944.5 CGGA_255 Primary GBM WHO IV Female 42 591 1 20387.83 CGGA_264 Primary GBM WHO IV Male 54 383 1 16158.83 CGGA_287 Primary GBM WHO IV Female 39 571 1 13903.78 CGGA_292 Primary GBM WHO IV Male 37 812 1 16158.83 CGGA_306 Primary GBM WHO IV Male 59 901 1 15167.79 CGGA_308 Primary GBM WHO IV Female 55 823 1 15944.5 CGGA_311 Primary GBM WHO IV Male 40 230 1 16158.83 CGGA_324 Primary GBM WHO IV Male 52 336 1 13903.78 CGGA_335 Primary GBM WHO IV Male 59 385 1 14799.65 CGGA_342 Primary GBM WHO IV Female 49 500 1 13903.78 CGGA_345 Primary GBM WHO IV Female 43 1547 0 13566.81 CGGA_346 Primary GBM WHO IV Male 45 104 1 15757.22 CGGA_349 Primary GBM WHO IV Female 49 869 0 14061.22 CGGA_366 Primary GBM WHO IV Male 54 255 1 12486.94 CGGA_370 Primary GBM WHO IV Male 51 338 1 12773.68 CGGA_371 Primary GBM WHO IV Male 42 811 1 14423.27 CGGA_373 Primary GBM WHO IV Female 45 281 1 17530.25 CGGA_375 Primary GBM WHO IV Male 57 657 1 8547.053 CGGA_377 Primary GBM WHO IV Male 17 398 1 13402.89 CGGA_380 Primary GBM WHO IV Male 52 165 1 11868.88 CGGA_401 Primary GBM WHO IV Female 24 168 1 14245.22 CGGA_409 Primary GBM WHO IV Male 63 98 1 13044.76 CGGA_419 Primary GBM WHO IV Male 56 198 1 15167.79 CGGA_436 Primary GBM WHO IV Male 48 955 1 13566.81 CGGA_437 Primary GBM WHO IV Female 54 1025 1 15358.48 CGGA_439 Primary GBM WHO IV Female 33 3813 1 15757.22 CGGA_442 Primary GBM WHO IV Male 62 1555 1 16350.39 CGGA_444 Primary GBM WHO IV Female 70 225 1 19340.83 CGGA_451 Primary GBM WHO IV Male 61 795 1 13903.78 CGGA_454 Primary GBM WHO IV Male 37 412 1 14061.22 CGGA_464 Primary GBM WHO IV Female 39 403 1 17039.76 CGGA_504 Primary GBM WHO IV Female 22 563 1 16568.29 CGGA_512 Primary GBM WHO IV Male 33 4371 0 15757.22 CGGA_525 Primary GBM WHO IV Male 61 138 1 10310.43 CGGA_527 Primary GBM WHO IV Male 33 439 1 6580.427 CGGA_547 Primary GBM WHO IV Male 29 1337 1 9531.431 CGGA_549 Primary GBM WHO IV Female 34 413 1 16350.39 CGGA_557 Primary GBM WHO IV Female 38 257 1 13235.78 CGGA_570 Primary GBM WHO IV Female 60 3602 0 9444.813 CGGA_573 Primary GBM WHO IV Male 35 946 1 17039.76 CGGA_575 Primary GBM WHO IV Male 55 551 1 11187.24 CGGA_588 Primary GBM WHO IV Male 58 773 1 15167.79 CGGA_593 Primary GBM WHO IV Male 37 242 1 14061.22 CGGA_594 Primary GBM WHO IV Female 27 4318 0 17530.25 CGGA_597 Primary GBM WHO IV Female 31 733 1 15757.22 CGGA_604 Primary GBM WHO IV Male 46 381 1 14245.22 CGGA_606 Primary GBM WHO IV Male 62 325 1 14423.27 CGGA_609 Primary GBM WHO IV Female 44 512 0 13903.78 CGGA_612 Primary GBM WHO IV Male 47 2023 1 15358.48 CGGA_9 Recurrent rGBM WHO IV Male 43 408 1 18508.16 CGGA_81 Recurrent rGBM WHO IV Male 39 551 1 5471.8 CGGA_104 Secondary sGBM WHO IV Male 27 440 0 12230.8 CGGA_220 Secondary sGBM WHO IV Male 34 318 1 13044.76 CGGA_462 Secondary sGBM WHO IV Male 38 361 1 12353.38 CGGA_518 Secondary sGBM WHO IV Male 27 212 1 19340.83 CGGA_545 Recurrent rGBM WHO IV Female 38 555 1 14799.65 CGGA_822 Secondary sGBM WHO IV Male 46 2237 1 17039.76 CGGA_D59 Secondary sGBM WHO IV Female 30 NA NA 6295.396
data.file<-"data.txt" gene<-"hsa-miR-29c" ## load R package library(ggplot2) library(ggpubr) library(gridExtra) ## import data dat<-read.table(data.file, sep='\t', head=T) rownames(dat)<-dat$CGGA.ID #head(dat) mat<-dat[!is.na(dat$PRS.type)&(dat$PRS.type=="Primary"|dat$PRS.type=="Recurrent")& !is.na(dat$Histology)& !is.na(dat$Grade)& !is.na(dat$Gender)& !is.na(dat$Age),] #head(mat) #mat$Expression<-log1p(mat$Expression) ### 1.Histology types<-c('O','OA','A','rA','rOA','AO','AA','rAO','rAA','AOA', 'sAO','sAA','rAOA','sAOA','GBM','rGBM','sGBM','others') ptext<-paste0("Anova, p=",compare_means(Expression~Histology,mat,method = "anova")[[3]]) dat_text <- data.frame(Histology=types[6], Expression=max(mat$Expression)+0.5, label = ptext, Grade = NA, fill_color=NA, CGGA.ID=NA, Age=NA, Gender=NA, PRS.type=NA ) mat$fill_color<-mat$Histology set.seed(1) plot1<-ggplot(mat,aes(fill=fill_color, y=Expression, x=factor(Histology,levels=types) ))+ geom_boxplot(outlier.shape = NA, width = 0.5)+ guides(fill=FALSE)+ geom_jitter(width = 0.1, cex=1.5, alpha = 0.3,size = 1.5,fill="gray")+ xlab('Histology')+ylab(paste0('Gene expression of ',gene))+ geom_text(data=dat_text,aes(label = label))+ theme(text = element_text(size=10),plot.margin = unit(c(1,1,1,1), 'cm')) ### 2.Grade ptext<-paste0("Anova, p=",compare_means(Expression~Grade,mat,method = "anova")[[3]]) dat_text <- data.frame(Grade="WHO III", Expression=max(mat$Expression)+0.5, label = ptext, Histology = NA, fill_color=NA, CGGA.ID=NA, Age=NA, Gender=NA, PRS.type=NA ) mat$fill_color<-mat$Grade set.seed(1) plot2<-ggplot(mat,aes(fill=fill_color, y=Expression, x=Grade))+ guides(fill=FALSE)+ geom_boxplot(outlier.shape = NA, width = 0.5)+ geom_jitter(width = 0.1, cex=1.5, alpha = 0.3,size = 1.5,fill="gray")+ xlab('Grade')+ylab(paste0('Gene expression of ',gene))+ geom_text(data=dat_text,aes(label = label))+ theme(text = element_text(size=10),plot.margin = unit(c(1,1,1,1), 'cm')) ###3. Gender### ptext<-paste0("T-test, p=",compare_means(Expression~Gender,mat,method = "t.test")[[5]]) dat_text <- data.frame(Gender = 1.5, Expression = c(max(mat$Expression)+0.5), label = ptext, Grade = NA, fill_color=NA, Histology=NA, CGGA.ID=NA, Age=NA, #Gender=NA, PRS.type=NA ) mat$fill_color<-mat$Gender set.seed(1) plot3<-ggplot(mat,aes(fill=fill_color, y=Expression, x=Gender))+ geom_boxplot(outlier.shape = NA, width = 0.5)+ guides(fill=FALSE)+ geom_jitter(width = 0.1, cex=1.5, alpha = 0.3,size = 1.5,fill="gray")+ xlab("Gender")+ ylab(paste0('Gene expression of ',gene))+ geom_text(data=dat_text,aes(label = label))+ theme(text = element_text(size=10),plot.margin = unit(c(1,1,1,1), 'cm')) ###4. Gender.grade### ptext1<-paste0("T-test, p=",compare_means(Expression~Gender,mat[mat$Grade=="WHO II",],method = "t.test")[[5]]) ptext2<-paste0("T-test, p=",compare_means(Expression~Gender,mat[mat$Grade=="WHO III",],method = "t.test")[[5]]) ptext3<-paste0("T-test, p=",compare_means(Expression~Gender,mat[mat$Grade=="WHO IV",],method = "t.test")[[5]]) dat_text <- data.frame(Gender=rep(1.5,3), Expression=rep(max(mat$Expression)+0.5,3), label = c(ptext1, ptext2, ptext3), Grade = c("WHO II", "WHO III", "WHO IV"), fill_color=NA, Histology=NA, CGGA.ID=NA, Age=NA, #Gender=NA PRS.type=NA ) mat$fill_color<-mat$Gender set.seed(1) plot4<-ggplot(mat,aes(fill=fill_color, y=Expression, x=Gender))+ geom_boxplot(outlier.shape = NA, width = 0.5)+ guides(fill=FALSE)+ geom_jitter(width = 0.1, cex=1.5, alpha = 0.3,size = 1.5,fill="gray")+ xlab("Gender")+ ylab(paste0('Gene expression of ',gene))+ facet_grid(.~Grade)+ geom_text(data=dat_text,aes(label = label))+ theme(text = element_text(size=10),plot.margin = unit(c(1,1,1,1), 'cm')) ###5.Age.status### med.Age<-median(mat$Age) mat$Age.status<-ifelse(mat$Age>=med.Age,paste0('>=',med.Age),paste0('<',med.Age)) ptext<-paste0("T-test, p=",compare_means(Expression~Age.status,mat,method = "t.test")[[5]]) dat_text <- data.frame(Age.status = 1.5, Expression = c(max(mat$Expression)+0.5), label = ptext, Grade = NA, fill_color=NA, Histology=NA, CGGA.ID=NA, Age=NA, Gender=NA, PRS.type=NA ) mat$fill_color<-mat$Age.status set.seed(1) plot5<-ggplot(mat,aes(fill=fill_color, y=Expression, x=Age.status))+ geom_boxplot(outlier.shape = NA, width = 0.5)+ guides(fill=FALSE)+ geom_jitter(width = 0.1, cex=1.5, alpha = 0.3,size = 1.5,fill="gray")+ xlab("Age status")+ ylab(paste0('Gene expression of ',gene))+ geom_text(data=dat_text,aes(label = label))+ theme(text = element_text(size=10),plot.margin = unit(c(1,1,1,1), 'cm')) ###6. Age.status.grade### ptext1<-paste0("T-test, p=",compare_means(Expression~Age.status,mat[mat$Grade=="WHO II",],method = "t.test")[[5]]) ptext2<-paste0("T-test, p=",compare_means(Expression~Age.status,mat[mat$Grade=="WHO III",],method = "t.test")[[5]]) ptext3<-paste0("T-test, p=",compare_means(Expression~Age.status,mat[mat$Grade=="WHO IV",],method = "t.test")[[5]]) dat_text <- data.frame(Age.status=rep(1.5,3), Expression=rep(max(mat$Expression)+0.5,3), label = c(ptext1, ptext2, ptext3), Grade = c("WHO II", "WHO III", "WHO IV"), fill_color=NA, Histology=NA, CGGA.ID=NA, Age=NA, Gender=NA, PRS.type=NA) fill_color<-mat$Age.status set.seed(1) plot6<-ggplot(mat,aes(fill=fill_color, y=Expression, x=Age.status))+ geom_boxplot(outlier.shape = NA, width = 0.5)+ guides(fill=FALSE)+ geom_jitter(width = 0.1, cex=1.5, alpha = 0.3,size = 1.5,fill="gray")+ xlab("Age status")+ ylab(paste0('Gene expression of ',gene))+ facet_grid(.~Grade)+ geom_text(data=dat_text,aes(label = label))+ theme(text = element_text(size=10),plot.margin = unit(c(1,1,1,1), 'cm')) ## output pdf grid.arrange(arrangeGrob(plot1,ncol=1), arrangeGrob(plot2,ncol=1), arrangeGrob(plot3,plot4,ncol=2), arrangeGrob(plot5,plot6,ncol=2), ncol=1, heights=rep(1,4))
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