Analyse - Methylation Distribution Result | CGGA

Analyse result - methylation data (distribution)

Dataset:

methyl_159

Gene:

Plot
Plot (Beta)
Data
R code

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CGGA.ID	PRS.type	Histology	Grade	Gender	Age	OS	Censor	Methylation
Normal1	NA	Normal	NA	NA	NA	NA	NA	0.286847925714286
Normal2	NA	Normal	NA	NA	NA	NA	NA	0.256770682857143
Normal3	NA	Normal	NA	NA	NA	NA	NA	0.256851842857143
Normal4	NA	Normal	NA	NA	NA	NA	NA	0.276822085714286
Normal5	NA	Normal	NA	NA	NA	NA	NA	0.243518414285714
Normal6	NA	Normal	NA	NA	NA	NA	NA	0.388891985714286
Normal7	NA	Normal	NA	NA	NA	NA	NA	0.27539052
Normal8	NA	Normal	NA	NA	NA	NA	NA	0.25461532
CGGA_469	Primary	A	WHO II	Male	30	4464	0	0.432557414285714
CGGA_285	Primary	A	WHO II	Male	30	1576	0	0.568646342857143
CGGA_590	Primary	A	WHO II	Male	36	4319	0	0.3529628
CGGA_447	Primary	A	WHO II	Female	20	4508	0	0.465958657142857
CGGA_433	Primary	A	WHO II	Male	30	NA	NA	0.413204657142857
CGGA_321	Primary	A	WHO II	Male	42	NA	NA	0.447178185714286
CGGA_258	Primary	A	WHO II	Female	61	663	1	0.452370957142857
CGGA_522	Primary	A	WHO II	Female	24	3630	0	0.548014214285714
CGGA_317	Primary	A	WHO II	Female	42	4681	0	0.513368842857143
CGGA_601	Primary	A	WHO II	Male	36	1133	1	0.578607985714286
CGGA_461	Primary	A	WHO II	Male	36	1266	1	0.532287842857143
CGGA_357	Primary	A	WHO II	Female	37	2518	1	0.448437942857143
CGGA_542	Primary	A	WHO II	Male	24	NA	NA	0.480748857142857
CGGA_544	Primary	A	WHO II	Female	44	4371	0	0.398902785714286
CGGA_626	Primary	A	WHO II	Male	20	4273	0	0.360180914285714
CGGA_673	Primary	AA	WHO III	Male	54	NA	NA	0.407903057142857
CGGA_688	Primary	A	WHO II	Male	19	4196	0	0.447221828571429
CGGA_692	Primary	AOA	WHO III	Male	19	NA	NA	0.346422585714286
CGGA_505	Primary	A	WHO II	Male	36	2994	1	0.713609942857143
CGGA_379	Primary	A	WHO II	Female	39	1077	1	0.540050901428571
CGGA_648	Primary	A	WHO II	Female	33	3647	1	0.683460071428571
CGGA_396	Primary	A	WHO II	Female	49	4576	0	0.564546314285714
CGGA_552	Primary	A	WHO II	Female	36	4362	0	0.722863771428571
CGGA_595	Primary	A	WHO II	Female	51	NA	NA	0.853910757142857
CGGA_583	Primary	A	WHO II	Female	36	3107	1	0.646221747142857
CGGA_365	Primary	A	WHO II	Female	32	3593	0	0.44830197
CGGA_459	Primary	A	WHO II	Male	44	3613	0	0.535015328571429
CGGA_434	Primary	A	WHO II	Male	35	4513	0	0.511189742857143
CGGA_399	Primary	A	WHO II	Male	36	2663	1	0.4916475
CGGA_281	Primary	A	WHO II	Female	36	1236	0	0.389678128571429
CGGA_407	Primary	A	WHO II	Male	33	4555	0	0.385355757142857
CGGA_708	Primary	A	WHO II	Female	38	4167	0	0.368964771428571
CGGA_711	Primary	A	WHO II	Male	49	1798	0	0.375274257142857
CGGA_712	Primary	A	WHO II	Male	25	NA	NA	0.358911328571429
CGGA_718	Primary	A	WHO II	Male	17	4159	0	0.383611957142857
CGGA_736	Primary	A	WHO II	Male	38	1164	1	0.213376568571429
CGGA_743	Primary	A	WHO II	Male	30	851	1	0.24740995
CGGA_746	Primary	A	WHO II	Male	45	332	1	0.381958628571429
CGGA_753	Primary	A	WHO II	Male	24	3217	1	0.17025655
CGGA_767	Primary	AA	WHO III	Male	36	1310	1	0.206519144285714
CGGA_770	Primary	A	WHO II	Male	30	1057	0	0.206350757142857
CGGA_776	Primary	A	WHO II	Female	48	4091	0	0.367202028571429
CGGA_781	Recurrent	rA	WHO II	Male	35	4088	0	0.3469363
CGGA_792	Primary	A	WHO II	Male	32	816	1	0.380294842857143
CGGA_253	Primary	A	WHO II	Female	37	5159	0	0.373632042857143
CGGA_571	Primary	A	WHO II	Male	38	NA	NA	0.707407742857143
CGGA_592	Primary	A	WHO II	Male	47	4315	0	0.478142824285714
CGGA_663	Primary	A	WHO II	Female	29	4210	0	0.525921642857143
CGGA_316	Primary	A	WHO II	Female	30	4682	0	0.536904405714286
CGGA_402	Primary	AA	WHO III	Male	53	4562	0	0.706228
CGGA_548	Primary	A	WHO II	Male	39	NA	NA	0.845710042857143
CGGA_354	Primary	AA	WHO III	Male	62	180	1	0.482871042857143
CGGA_334	Primary	AA	WHO III	Female	35	1450	1	0.476629014285714
CGGA_247	Primary	AA	WHO III	Male	60	609	1	0.4173412
CGGA_486	Primary	AA	WHO III	Male	23	424	1	0.2864613
CGGA_353	Primary	AA	WHO III	Female	60	516	1	0.340915171428571
CGGA_476	Primary	A	WHO II	Female	37	936	1	0.342279185714286
CGGA_405	Primary	AA	WHO III	Male	59	497	1	0.4037894
CGGA_412	Primary	AA	WHO III	Female	42	358	1	0.342917314285714
CGGA_410	Primary	AA	WHO III	Female	70	1099	1	0.296153345714286
CGGA_249	Primary	AA	WHO III	Male	41	312	1	0.23897994
CGGA_331	Primary	AA	WHO III	Female	27	1638	1	0.5209325
CGGA_202	Primary	AA	WHO III	Male	41	NA	NA	0.353707857142857
CGGA_558	Primary	AO	WHO III	Male	34	188	0	0.424265171428571
CGGA_277	Primary	AO	WHO III	Male	40	4739	0	0.546000685714286
CGGA_364	Primary	AO	WHO III	Male	32	666	1	0.586716557142857
CGGA_508	Primary	AO	WHO III	Female	17	285	1	0.368161005714286
CGGA_260	Primary	AO	WHO III	Female	49	567	1	0.511381414285714
CGGA_414	Primary	AO	WHO III	Male	28	4541	0	0.605984171428571
CGGA_489	Primary	AO	WHO III	Male	31	3804	0	0.604550214285714
CGGA_481	Primary	AO	WHO III	Female	61	1507	1	0.667777148571429
CGGA_403	Primary	AO	WHO III	Male	43	925	0	0.5636143
CGGA_598	Primary	AO	WHO III	Female	61	3677	1	0.628975642857143
CGGA_490	Primary	AO	WHO III	Female	37	3804	0	0.562927814285714
CGGA_474	Primary	AO	WHO III	Male	NA	2029	0	0.5019975
CGGA_314	Recurrent	rAO	WHO III	Male	39	2568	1	0.517077742857143
CGGA_329	Primary	AA	WHO III	Male	42	419	1	0.504875757142857
CGGA_352	Primary	AA	WHO III	Male	18	4639	0	0.348946671428571
CGGA_438	Primary	AOA	WHO III	Male	53	686	1	0.384964828571429
CGGA_259	Recurrent	rAA	WHO III	Female	26	649	0	0.510325142857143
CGGA_279	Recurrent	rAA	WHO III	Female	49	1314	1	0.5360168
CGGA_336	Recurrent	rAO	WHO III	Female	57	2549	1	0.485491145714286
CGGA_487	Recurrent	rGBM	WHO IV	Male	46	156	1	0.301070174285714
CGGA_391	Primary	AOA	WHO III	Female	29	2010	1	0.623100737142857
CGGA_232	Recurrent	rAA	WHO III	Male	28	415	1	0.446600281428571
CGGA_231	Primary	AA	WHO III	Male	60	510	1	0.472798485714286
CGGA_492	Recurrent	rAA	WHO III	Male	27	652	0	0.272857791428571
CGGA_406	Recurrent	rAA	WHO III	Female	24	90	1	0.484659457142857
CGGA_393	Primary	AOA	WHO III	Male	65	2190	1	0.478069285714286
CGGA_471	Primary	AA	WHO III	Male	45	1373	1	0.304232204285714
CGGA_562	Primary	AA	WHO III	Female	62	290	1	0.645537114285714
CGGA_498	Primary	AOA	WHO III	Male	37	356	1	0.416248642857143
CGGA_596	Primary	AA	WHO III	Male	60	1618	1	0.533445757142857
CGGA_351	Primary	AA	WHO III	Female	35	742	1	0.5434799
CGGA_513	Primary	AA	WHO III	Female	44	1374	1	0.3778141
CGGA_387	Primary	O	WHO II	Female	51	539	1	0.374863385714286
CGGA_579	Primary	O	WHO II	Female	42	2125	1	0.447897714285714
CGGA_323	Primary	O	WHO II	Female	48	4338	0	0.337593428571429
CGGA_484	Primary	O	WHO II	Female	32	4442	0	0.338382614285714
CGGA_485	Primary	O	WHO II	Male	31	2437	0	0.419676828571429
CGGA_639	Primary	O	WHO II	Male	47	4256	0	0.551951657142857
CGGA_672	Primary	O	WHO II	Male	22	4215	0	0.471276928571429
CGGA_633	Primary	O	WHO II	Female	56	1812	1	0.602494071428571
CGGA_589	Primary	O	WHO II	Male	33	4320	0	0.482190761428571
CGGA_446	Primary	O	WHO II	Female	35	4508	0	0.533200694285714
CGGA_659	Primary	O	WHO II	Female	50	730	0	0.533831557142857
CGGA_275	Primary	O	WHO II	Female	43	4722	0	0.381937385714286
CGGA_543	Primary	O	WHO II	Male	43	4371	0	0.634582091428571
CGGA_41	Primary	AA	WHO III	Male	53	94	1	0.314301485714286
CGGA_81	Recurrent	rGBM	WHO IV	Male	39	551	1	0.536579428571429
CGGA_462	Secondary	sGBM	WHO IV	Male	38	361	1	0.440436942857143
CGGA_545	Recurrent	rGBM	WHO IV	Female	38	555	1	0.567383557142857
CGGA_9	Recurrent	rGBM	WHO IV	Male	43	408	1	0.514562142857143
CGGA_518	Secondary	sGBM	WHO IV	Male	27	212	1	0.435407885714286
CGGA_104	Secondary	sGBM	WHO IV	Male	27	440	0	0.455331428571429
CGGA_220	Secondary	sGBM	WHO IV	Male	34	318	1	0.494639634285714
CGGA_822	Secondary	sGBM	WHO IV	Male	46	2237	1	0.207044041428571
CGGA_D59	Secondary	sGBM	WHO IV	Female	30	NA	NA	0.210658531428571
CGGA_11	Primary	GBM	WHO IV	Female	57	155	1	0.304372405714286
CGGA_88	Primary	GBM	WHO IV	Male	20	169	1	0.349758771428571
CGGA_156	Primary	GBM	WHO IV	Male	51	179	1	0.225777078571429
CGGA_161	Primary	GBM	WHO IV	Female	35	329	1	0.44901757
CGGA_182	Primary	GBM	WHO IV	Female	47	128	1	0.256273487142857
CGGA_203	Primary	GBM	WHO IV	Male	40	188	1	0.220745308571429
CGGA_214	Primary	GBM	WHO IV	Female	48	193	1	0.17585512
CGGA_305	Primary	GBM	WHO IV	Female	49	34	1	0.231743064285714
CGGA_346	Primary	GBM	WHO IV	Male	45	104	1	0.213649591428571
CGGA_401	Primary	GBM	WHO IV	Female	24	168	1	0.229309733857143
CGGA_409	Primary	GBM	WHO IV	Male	63	98	1	0.193233558571429
CGGA_525	Primary	GBM	WHO IV	Male	61	138	1	0.187685151428571
CGGA_13	Primary	GBM	WHO IV	Male	59	267	1	0.283677952857143
CGGA_311	Primary	GBM	WHO IV	Male	40	230	1	0.454337471428571
CGGA_366	Primary	GBM	WHO IV	Male	54	255	1	0.3745776
CGGA_380	Primary	GBM	WHO IV	Male	52	165	1	0.499768647142857
CGGA_419	Primary	GBM	WHO IV	Male	56	198	1	0.21240049
CGGA_444	Primary	GBM	WHO IV	Female	70	225	1	0.191361964285714
CGGA_557	Primary	GBM	WHO IV	Female	38	257	1	0.281230594285714
CGGA_593	Primary	GBM	WHO IV	Male	37	242	1	0.333012658571429
CGGA_1	Primary	GBM	WHO IV	Female	43	605	1	0.4471908
CGGA_31	Primary	GBM	WHO IV	Female	9	5127	0	0.28355625
CGGA_58	Primary	GBM	WHO IV	Male	60	570	1	0.575474871428571
CGGA_126	Primary	GBM	WHO IV	Female	50	1177	1	0.404893487142857
CGGA_130	Primary	GBM	WHO IV	Male	45	1262	1	0.668805142857143
CGGA_139	Primary	GBM	WHO IV	Male	59	694	1	0.487882671428571
CGGA_168	Primary	GBM	WHO IV	Male	17	3086	0	0.422650972857143
CGGA_178	Primary	GBM	WHO IV	Male	38	972	1	0.753309585714286
CGGA_225	Primary	GBM	WHO IV	Male	32	1741	1	0.352257618428571
CGGA_255	Primary	GBM	WHO IV	Female	42	591	1	0.263562227142857
CGGA_308	Primary	GBM	WHO IV	Female	55	823	1	0.308237145714286
CGGA_439	Primary	GBM	WHO IV	Female	33	3813	1	0.500048112857143
CGGA_547	Primary	GBM	WHO IV	Male	29	1337	1	0.386565952857143

data.file<-"data.txt"
gene<-"ALDH1A3"

## 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)&
           !is.na(dat$Grade)&
           !is.na(dat$Gender)&
           !is.na(dat$Age),]
#head(mat)
#mat$Methylation<-log1p(mat$Methylation)

### 1.Histology
types<-c('Normal','O','A','rA','AO','AA','rAO','AOA','rAOA','sAOA','GBM','rGBM','sGBM')
ptext<-paste0("Anova, p=",compare_means(Methylation~Histology,mat,method = "anova")[[3]])
dat_text <- data.frame(Histology=types[6],
                       Methylation=max(mat$Methylation)+0.5,
                       label = ptext,
                       Grade = NA,
                       fill_color=NA,
                       CGGA.ID=NA,
                       Age=NA,
                       Gender=NA
                       )
mat$fill_color<-mat$Histology

set.seed(1)
plot1<-ggplot(mat,aes(fill=fill_color,
                      y=Methylation,
                      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 methylation 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(Methylation~Grade,mat,method = "anova")[[3]])
dat_text <- data.frame(Grade="WHO III",
                       Methylation=max(mat$Methylation)+0.5,
                       label = ptext,
                       Histology = NA,
                       fill_color=NA,
                       CGGA.ID=NA,
                       Age=NA,
                       Gender=NA
                       )
mat$fill_color<-mat$Grade

set.seed(1)
plot2<-ggplot(mat,aes(fill=fill_color,
                      y=Methylation,
                      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 methylation 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(Methylation~Gender,mat,method = "t.test")[[5]])
dat_text <- data.frame(Gender = 1.5,
                       Methylation = c(max(mat$Methylation)+0.5),
                       label = ptext,
                       Grade = NA,
                       fill_color=NA,
                       Histology=NA,
                       CGGA.ID=NA,
                       Age=NA
                       #Gender=NA,
                       )
mat$fill_color<-mat$Gender

set.seed(1)
plot3<-ggplot(mat,aes(fill=fill_color,
                      y=Methylation,
                      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 methylation 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(Methylation~Gender,mat[mat$Grade=="WHO II",],method = "t.test")[[5]])
ptext2<-paste0("T-test, p=",compare_means(Methylation~Gender,mat[mat$Grade=="WHO III",],method = "t.test")[[5]])
ptext3<-paste0("T-test, p=",compare_means(Methylation~Gender,mat[mat$Grade=="WHO IV",],method = "t.test")[[5]])
dat_text <- data.frame(Gender=rep(1.5,3),
                       Methylation=rep(max(mat$Methylation)+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
)
mat$fill_color<-mat$Gender

set.seed(1)
plot4<-ggplot(mat,aes(fill=fill_color,
                      y=Methylation,
                      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 methylation 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$Age>=med.Age,paste0('>=',med.Age),paste0('<',med.Age))
ptext<-paste0("T-test, p=",compare_means(Methylation~Age.status,mat,method = "t.test")[[5]])
dat_text <- data.frame(Age.status = 1.5,
                       Methylation = c(max(mat$Methylation)+0.5),
                       label = ptext,
                       Grade = NA,
                       fill_color=NA,
                       Histology=NA,
                       CGGA.ID=NA,
                       Age=NA,
                       Gender=NA
                       )
mat$fill_color<-mat$Age.status

set.seed(1)
plot5<-ggplot(mat,aes(fill=fill_color,
                       y=Methylation,
                       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 methylation 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(Methylation~Age.status,mat[mat$Grade=="WHO II",],method = "t.test")[[5]])
ptext2<-paste0("T-test, p=",compare_means(Methylation~Age.status,mat[mat$Grade=="WHO III",],method = "t.test")[[5]])
ptext3<-paste0("T-test, p=",compare_means(Methylation~Age.status,mat[mat$Grade=="WHO IV",],method = "t.test")[[5]])
dat_text <- data.frame(Age.status=rep(1.5,3),
                       Methylation=rep(max(mat$Methylation)+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)
fill_color<-mat$Age.status

set.seed(1)
plot6<-ggplot(mat,aes(fill=fill_color,
                       y=Methylation,
                       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 methylation 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))

Analyse result - methylation data (distribution)

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