- Now, I do know how to insert figures, but I don't know how to insert tables into MediaWiki. Therefore, I write a small code to transfer routine table to wikitable.
- I must say: mediawiki is really a excellent tool to manage the lab and monitor the progress or task to help us complete the project quickly.
perl table2wikitable.pl input.table.txt
#!/usr/bin/perl
#table2wikitable.pl
use strict;
use Cwd;
chdir getcwd;
my $input=@ARGV[0];
open F,$input;
print "{| class=\"wikitable\" style=\"text-align: right; color: red;\"\n";
while(<F>){
chomp;
my @line=split /\t/;
my $tmp=join("||",@line);
print "| $tmp\n|-\n";
}
print"|}\n";
- corresponding table is as the following. Pan-Cancer Methylation 450K dataset were collected from TCGA Project to identify optional padloc regions.
Symbol |
Case |
Control |
Cancer |
肿瘤名称
|
KIRC |
160 |
160 |
Kidney renal clear cell carcinoma |
肾透明细胞癌
|
BRCA |
92 |
92 |
Breast invasive carcinoma |
浸润性乳腺癌
|
THCA |
56 |
56 |
Thyroid carcinoma |
甲状腺癌
|
HNSC |
50 |
50 |
Head and Neck squamous cell carcinoma |
头颈部鳞状细胞癌
|
PRAD |
49 |
49 |
Prostate adenocarcinoma |
前列腺癌
|
LIHC |
49 |
49 |
Liver hepatocellular carcinoma |
肝癌
|
KIRP |
45 |
45 |
Kidney renal papillary cell carcinoma |
肾乳头状细胞癌
|
LUSC |
41 |
41 |
Lung squamous cell carcinoma |
肺腺癌
|
COAD |
39 |
39 |
Colon adenocarcinoma |
结肠癌
|
UCEC |
30 |
30 |
Uterine Corpus Endometrial carcinoma |
子宫内膜癌
|
LUAD |
26 |
26 |
Lung adenocarcinoma |
肺鳞癌
|
- In order to publish MONOD paper to a good paper quickly. 1) fast 2) large data
- collect methylation 450 data of cancer tissues and normals from TCGA Project
- sample size
- clinical information: gender, age
tar xvf 08177614-f305-4fbf-84ca-fd2fbfe26755.tar #
tar xvf 548dd1cf-84a1-4d96-86f0-b47d6300daa6.tar #
tar xvf 9cd95b1c-782c-478d-9ec9-de7c9c441cc4.tar #
tar xvf c5da6cd3-0266-4ebb-bd11-713ffe9b5ef9.tar #
- focus on 3 cancers which we have preliminary data (lung, colon, pancreatic)
library("stringr")
for(cancer in c("COAD","LUAD","LUSC","PAAD")){
dir<-paste("/home/sguo/monod/data/",tolower(cancer),"/DNA_Methylation/JHU_USC__HumanMethylation450/Level_3",sep="")
setwd(dir)
pattern=paste("jhu-usc.edu_",cancer,".*",sep="")
print (pattern)
file=list.files(pattern=pattern)
idv<-unique(as.array(str_extract(file,"TCGA-[0-9|a-z|A-Z]*-[0-9|a-z|A-Z]*")))
pairidv<-c()
for (i in 1:length(idv)){
t1<-paste(idv[i],"-01",sep="")
t2<-paste(idv[i],"-11",sep="")
if(all(any(grepl(t1,file)),any(grepl(t2,file)))){
pairidv<-c(pairidv,t1,t2)
}
}
l1<-length(pairidv)
l2<-length(file)
id1<-lapply(lapply(strsplit(file,"[.]"),function(x) x[6]),function(x) substr(x,1,15))
id2<-lapply(lapply(strsplit(file,"[.]"),function(x) x[6]),function(x) substr(x,14,15))
sam<-lapply(lapply(strsplit(file,"[.]"),function(x) x[6]),function(x) substr(x,1,15))
tab<-table(unlist(lapply(lapply(strsplit(file,"[.]"),function(x) x[6]),function(x) substr(x,14,15))))
c1<-tab[which(names(tab)=="01")]
c2<-tab[which(names(tab)=="11")]
c3<-length(pairidv)
print(c((c1),(c2),Pair=c3))
}
- The sample size for the genome-wide DNA methyaltion dataset for 3 cancers in TCGA Project are as the following:
|
Cancer |
Normal |
Paired |
Total
|
COAD |
312 |
38 |
76 |
350
|
LUAD |
473 |
32 |
58 |
505
|
LUSC |
370 |
42 |
80 |
412
|
PAAD |
184 |
10 |
20 |
194
|
Sum |
1339 |
122 |
234 |
1461
|
- totally, 1339 cancer tissues and 122 normal tissues were used to refine the padloc regions.
- firstly, we should check the methylation status of the CpG sites/regions in the normal tissues.
1) hypermethylation in cancer/patient plasma
2) hypomethylation in normal/health plasma
3) large methylation difference (delta beta)
4) high group specificity index (GSI)
5) high ratio release to plasma
File:GSI2.png
- s(j): average methylation in individual group j
- S(max): average methylation in the group with highest methylation level
# group specificity index,GSI
setwd("../pan")
load("PanPairMethData.RData")
pheno=data$pheno
xmean <- rowsum(data[,2:ncol(data)], pheno)/table(pheno)
gsi<-apply(xmean,2,function(x) (length(x)-sum(x)/max(x))/(length(x)-1))
rlt<-data.frame(pheno=names(table(pheno)),xmean[,match(names(sort(gsi,decreasing=T)[1:5]),colnames(xmean))])
write.table(rlt,file="pancancer.high.gsi.site.txt",sep="\t",quote=F,col.names=NA,row.names=T)
pdf("gsi.distribution.pdf")
hist(gsi,main="Histogram of Group Specific Index")
dev.off()
num90<-sum(gsi>0.85)
xmean90<-xmean[,match(names(sort(gsi,decreasing=T)[1:num90]),colnames(xmean))]
xx1<-table(data$pheno)[as.numeric(names(table(apply(xmean90,2,function(x) which.max(x)))))]
xx2<-table(apply(xmean90,2,function(x) which.max(x)))
rlt2<-data.frame(sample_size=xx1,xx2)
write.table(rlt2,file="target.status.specifi.txt",sep="\t",quote=F,col.names=NA,row.names=T)
- run the code and you can find the strongest group specific index CpGs with TCGA Pancancer dataset.
Sample_size |
cg16579555 |
cg26240185 |
cg12587766 |
cg15375239 |
cg10157975
|
BRCA-01 |
0.01 |
0.014 |
0.021 |
0.012 |
0.011
|
BRCA-11 |
0.01 |
0.011 |
0.02 |
0.011 |
0.011
|
COAD-01 |
0.011 |
0.012 |
0.605 |
0.011 |
0.353
|
COAD-11 |
0.011 |
0.015 |
0.033 |
0.012 |
0.022
|
HNSC-01 |
0.011 |
0.012 |
0.021 |
0.013 |
0.031
|
HNSC-11 |
0.011 |
0.011 |
0.022 |
0.012 |
0.012
|
KIRC-01 |
0.009 |
0.01 |
0.016 |
0.019 |
0.011
|
KIRC-11 |
0.009 |
0.009 |
0.016 |
0.01 |
0.009
|
KIRP-01 |
0.01 |
0.026 |
0.021 |
0.016 |
0.01
|
KIRP-11 |
0.01 |
0.011 |
0.022 |
0.012 |
0.011
|
LIHC-01 |
0.396 |
0.498 |
0.042 |
0.468 |
0.028
|
LIHC-11 |
0.057 |
0.099 |
0.024 |
0.133 |
0.015
|
LUAD-01 |
0.01 |
0.014 |
0.022 |
0.012 |
0.012
|
LUAD-11 |
0.011 |
0.013 |
0.024 |
0.013 |
0.013
|
LUSC-01 |
0.009 |
0.01 |
0.017 |
0.01 |
0.009
|
LUSC-11 |
0.008 |
0.01 |
0.018 |
0.011 |
0.01
|
PRAD-01 |
0.011 |
0.011 |
0.02 |
0.012 |
0.011
|
PRAD-11 |
0.01 |
0.012 |
0.019 |
0.012 |
0.011
|
THCA-01 |
0.011 |
0.013 |
0.028 |
0.013 |
0.013
|
THCA-11 |
0.011 |
0.013 |
0.024 |
0.012 |
0.013
|
UCEC-01 |
0.013 |
0.014 |
0.02 |
0.012 |
0.026
|
UCEC-11 |
0.011 |
0.015 |
0.022 |
0.012 |
0.014
|
- clearly, we can find cancer specific hypermethylated genes with previous method. Then, how about the distribution of the GSI.
File:Gsi.distribution.jpeg
- 745 CpGs or regions whose GSI>0.9. these CpGs/regions showed high tissue/status specific.
- 3181 CpGs or regions whose GSI>0.85. these CpGs/regions showed high tissue/status specific.
- The question is that whether they can be found in patients plasma? (Dr.Zhang might have some information)
- 3181 tissue and status specific hypermethylation CpG sites were showed as the following table.
|
Sample_size |
Var1 |
Freq
|
BRCA-01 |
92 |
1 |
33
|
BRCA-11 |
92 |
2 |
1
|
COAD-01 |
39 |
3 |
1695
|
COAD-11 |
39 |
4 |
16
|
HNSC-01 |
50 |
5 |
143
|
KIRC-01 |
160 |
7 |
1
|
KIRC-11 |
160 |
8 |
1
|
KIRP-01 |
45 |
9 |
20
|
KIRP-11 |
45 |
10 |
1
|
LIHC-01 |
49 |
11 |
768
|
LIHC-11 |
49 |
12 |
40
|
LUAD-01 |
26 |
13 |
1
|
LUSC-01 |
41 |
15 |
8
|
PRAD-01 |
49 |
17 |
271
|
PRAD-11 |
49 |
18 |
1
|
THCA-01 |
56 |
19 |
4
|
THCA-11 |
56 |
20 |
7
|
UCEC-01 |
30 |
21 |
168
|
UCEC-11 |
30 |
22 |
2
|
- Here, I choose top 5 regions to represent such specific tissue and it's specific status (cancer or normal), code is as the following:
gsi2<-gsi[order(gsi,decreasing=T)]
rlt3<-c()
for(i in seq(1,22,by=2)){
z<-0
j<-0
while(j<length(gsi2) & z<5){
j=j+1
max<-which.max(xmean[,match(names(gsi2[j]),colnames(xmean))])
if(max==i){
z<-z+1
rlt3<-rbind(rlt3,(c(names(gsi2[j]),names(table(pheno))[i])))
}
}
}
rlt3<-data.frame(rlt3)
inf<-read.table("/home/sguo/monod/data/paad/DNA_Methylation/JHU_USC__HumanMethylation450/Level_3/jhu-usc.edu_PAAD.HumanMethylation450.9.lvl-3.TCGA-S4-A8RM-01A-11D-A378-05.txt",head=F,skip=2,sep="\t")
cpg<-rlt3[,1]
cancer<-substr(rlt3[,2],1,4)
gene<-inf[match(rlt3[,1],inf[,1]),3]
chr<-inf[match(rlt3[,1],inf[,1]),4]
start<-inf[match(rlt3[,1],inf[,1]),5]-60
end<-inf[match(rlt3[,1],inf[,1]),5]+60
GSI<-gsi[match(rlt3[,1],names(gsi))]
rlt4<-data.frame(cpg,cancer,GSI,gene,chr,start,end)
write.table(rlt4,file="target.status.specifi.site.txt",sep="\t",quote=F,col.names=NA,row.names=T)
- The results were showed as the following table. Top 5 important biomarkers for each cancer of the specific tissue and status were list.
cpg |
cancer |
GSI |
gene |
chr |
start |
end
|
cg18565473 |
BRCA |
0.932682778 |
ETS1 |
11 |
128392042 |
128392162
|
cg23884187 |
BRCA |
0.9157933441 |
C20orf95 |
20 |
37275009 |
37275129
|
cg14052221 |
BRCA |
0.912760996 |
PSAT1 |
9 |
80911998 |
80912118
|
cg24797187 |
BRCA |
0.9120597976 |
AFF3 |
2 |
100175708 |
100175828
|
cg18943693 |
BRCA |
0.9116756297 |
|
1 |
155043501 |
155043621
|
cg12587766 |
COAD |
0.9628664076 |
LIFR |
5 |
38556375 |
38556495
|
cg10157975 |
COAD |
0.9590682334 |
ZNF304 |
19 |
57862382 |
57862502
|
cg23977631 |
COAD |
0.9547387465 |
LONRF2 |
2 |
100938739 |
100938859
|
cg04117229 |
COAD |
0.9529809636 |
SPG20 |
13 |
36920753 |
36920873
|
cg09854653 |
COAD |
0.9527461786 |
QKI |
6 |
163834843 |
163834963
|
cg03988778 |
HNSC |
0.9412487691 |
SVIP |
11 |
22850831 |
22850951
|
cg08211306 |
HNSC |
0.9364418236 |
ENPP4 |
6 |
46097724 |
46097844
|
cg26968387 |
HNSC |
0.9325520916 |
ZNF420 |
19 |
37569208 |
37569328
|
cg03280624 |
HNSC |
0.9251408321 |
ZNF583 |
19 |
56915595 |
56915715
|
cg00471966 |
HNSC |
0.9237859158 |
ZNF420 |
19 |
37569290 |
37569410
|
cg26228351 |
KIRC |
0.8668214436 |
KIF21B |
1 |
200992596 |
200992716
|
cg00593900 |
KIRC |
0.8404290268 |
ANGPTL6 |
19 |
10206686 |
10206806
|
cg11697226 |
KIRC |
0.8393271691 |
TNFRSF11A |
18 |
59992325 |
59992445
|
cg09865339 |
KIRC |
0.83639651 |
GPC2;STAG3 |
7 |
99774875 |
99774995
|
cg02632185 |
KIRC |
0.8242621029 |
MAST4 |
5 |
66299726 |
66299846
|
cg15598442 |
KIRP |
0.9265204439 |
|
1 |
25175003 |
25175123
|
cg26622232 |
KIRP |
0.9074060913 |
OXR1 |
8 |
107669727 |
107669847
|
cg16326979 |
KIRP |
0.8986303106 |
OXR1 |
8 |
107670101 |
107670221
|
cg17031478 |
KIRP |
0.8970812881 |
HOXC4;HOXC5 |
12 |
54427113 |
54427233
|
cg17136799 |
KIRP |
0.8884003413 |
OXR1 |
8 |
107669723 |
107669843
|
cg16579555 |
LIHC |
0.9680462923 |
RNF135 |
17 |
29298292 |
29298412
|
cg26240185 |
LIHC |
0.966346469 |
FAR1 |
11 |
13690097 |
13690217
|
cg15375239 |
LIHC |
0.961490202 |
SPINT2 |
19 |
38755227 |
38755347
|
cg15969216 |
LIHC |
0.9580311599 |
TSC22D1 |
13 |
45150202 |
45150322
|
cg03326059 |
LIHC |
0.9580238498 |
FAR1 |
11 |
13690100 |
13690220
|
cg13215643 |
LUAD |
0.8553268659 |
DACT1 |
14 |
59104765 |
59104885
|
cg07017994 |
LUAD |
0.8425613476 |
EPHB6 |
7 |
142552854 |
142552974
|
cg12487147 |
LUAD |
0.8391970226 |
HSD17B8 |
6 |
33172382 |
33172502
|
cg26615830 |
LUAD |
0.8371456733 |
MSX1 |
4 |
4861270 |
4861390
|
cg21929771 |
LUAD |
0.8207003816 |
PTPRU |
1 |
29586520 |
29586640
|
cg07240673 |
LUSC |
0.8990393087 |
CLUAP1 |
16 |
3550848 |
3550968
|
cg18772127 |
LUSC |
0.8862042556 |
CMTM7 |
3 |
32443436 |
32443556
|
cg08562243 |
LUSC |
0.8748813166 |
CLUAP1 |
16 |
3551109 |
3551229
|
cg02566698 |
LUSC |
0.8726750003 |
CLUAP1 |
16 |
3550872 |
3550992
|
cg02379764 |
LUSC |
0.8697737274 |
CLUAP1 |
16 |
3550968 |
3551088
|
cg07635623 |
PRAD |
0.9556665683 |
SERPINB1 |
6 |
2841815 |
2841935
|
cg14283569 |
PRAD |
0.9475569263 |
|
19 |
51416153 |
51416273
|
cg05098590 |
PRAD |
0.9434078407 |
ADD3 |
10 |
111767319 |
111767439
|
cg10938374 |
PRAD |
0.942601773 |
IER3 |
6 |
30711998 |
30712118
|
cg16232979 |
PRAD |
0.9424310073 |
TPM4 |
19 |
16187571 |
16187691
|
cg27115721 |
THCA |
0.8819506898 |
FAM49A |
2 |
16790278 |
16790398
|
cg04358131 |
THCA |
0.8775891556 |
MAFK |
7 |
1572192 |
1572312
|
cg10540754 |
THCA |
0.8609459396 |
FAM49A |
2 |
16790310 |
16790430
|
cg22749810 |
THCA |
0.8552029028 |
RNF213 |
17 |
78237329 |
78237449
|
cg12822074 |
THCA |
0.8497825469 |
RTN4RL2 |
11 |
57243805 |
57243925
|
cg03221247 |
UCEC |
0.9555805745 |
LYPLAL1 |
1 |
219347398 |
219347518
|
cg15494117 |
UCEC |
0.9514218403 |
TERC |
3 |
169482835 |
169482955
|
cg15599946 |
UCEC |
0.9474897559 |
TERC |
3 |
169482839 |
169482959
|
cg18985581 |
UCEC |
0.946277345 |
|
14 |
105512153 |
105512273
|
cg02665570 |
UCEC |
0.9458580151 |
LYPLAL1 |
1 |
219347280 |
219347400
|
rm(list=ls())
setwd("/home/sguo/Dropbox/Project/PanCancer")
load("rf.proximity.6631.1500tree.RData")
rlt3<-data.frame(rownames(RF$importance)[match(sort(RF$importance,decreasing=T)[1:300],RF$importance)],sort(RF$importance,decreasing=T)[1:300])
inf<-read.table("/home/sguo/monod/data/paad/DNA_Methylation/JHU_USC__HumanMethylation450/Level_3/jhu-usc.edu_PAAD.HumanMethylation450.9.lvl-3.TCGA-S4-A8RM-01A-11D-A378-05.txt",head=F,skip=2,sep="\t")
gene<-inf[match(rlt3[,1],inf[,1]),3]
chr<-inf[match(rlt3[,1],inf[,1]),4]
start<-inf[match(rlt3[,1],inf[,1]),5]-60
end<-inf[match(rlt3[,1],inf[,1]),5]+60
GSI<-gsi[match(rlt3[,1],names(gsi))]
cpg<-rlt3[,1]
VIM<-rlt3[,2]
rlt4<-data.frame(cpg,VIM,GSI,gene,chr,start,end)
write.table(rlt4,file="top300.txt",sep="\t",quote=F,col.names=NA,row.names=T)
- top 75 important variables selected from random forest algorithm
- VIM: variable importance
- GSI: group specific index
chr |
start |
end |
cpg |
VIM |
GSI |
gene
|
1 |
9600055 |
9600175 |
cg03901886 |
0.7527770839 |
0.90858776 |
SLC25A33
|
1 |
25943056 |
25943176 |
cg06080267 |
0.9964247548 |
0.396840622 |
MAN1C1
|
1 |
45241764 |
45241884 |
cg07550267 |
0.6139353757 |
0.7744774163 |
SNORD46;RPS8
|
1 |
154435888 |
154436008 |
cg25135018 |
1.1579398716 |
0.2542848418 |
IL6R
|
1 |
205782179 |
205782299 |
cg25022560 |
0.689666593 |
0.7540172664 |
SLC41A1
|
10 |
29948368 |
29948488 |
cg13324103 |
1.2195568782 |
0.6584742177 |
SVIL
|
10 |
65442229 |
65442349 |
cg04148163 |
0.6111681566 |
0.3307470871 |
NA
|
10 |
94450956 |
94451076 |
cg24787755 |
0.8350950605 |
0.3569153802 |
HHEX
|
10 |
129350862 |
129350982 |
cg19321696 |
0.6204525113 |
0.4549838076 |
NPS
|
10 |
130385927 |
130386047 |
ch.10.2810236F |
2.943185017 |
0.3580734812 |
NA
|
11 |
3877416 |
3877536 |
cg06349174 |
1.25625477 |
0.6498979205 |
STIM1
|
11 |
77122804 |
77122924 |
cg26996201 |
1.1437467953 |
0.8204421456 |
PAK1
|
11 |
95889394 |
95889514 |
cg15521790 |
0.822874101 |
0.3726921136 |
MAML2
|
11 |
114252352 |
114252472 |
ch.11.2339138R |
4.2619507571 |
0.7229315151 |
NA
|
11 |
126152905 |
126153025 |
cg10291648 |
0.7004139255 |
0.5213674972 |
TIRAP
|
12 |
863545 |
863665 |
cg05615230 |
0.9420615719 |
0.7006003974 |
WNK1
|
12 |
3226385 |
3226505 |
cg11958128 |
1.9753368227 |
0.2515861697 |
TSPAN9
|
12 |
6665270 |
6665390 |
cg08875705 |
0.8908324386 |
0.5077694894 |
IFFO1
|
12 |
54090194 |
54090314 |
cg05120944 |
0.9328347886 |
0.7118414871 |
NA
|
12 |
108678186 |
108678306 |
cg07653800 |
0.7859823353 |
0.4191516247 |
NA
|
12 |
125819537 |
125819657 |
cg03428945 |
0.6217564925 |
0.4321944141 |
TMEM132B
|
13 |
114917237 |
114917357 |
cg20856545 |
0.8345343125 |
0.4269503759 |
NA
|
14 |
34415696 |
34415816 |
cg24170040 |
0.6532317962 |
0.2338509865 |
EGLN3
|
14 |
93651109 |
93651229 |
cg05199950 |
0.6905234361 |
0.2130837095 |
MOAP1;C14orf109
|
15 |
59225753 |
59225873 |
cg19083363 |
0.8685475284 |
0.4071556532 |
SLTM
|
15 |
64422201 |
64422321 |
ch.15.934240F |
0.8219896207 |
0.7244697713 |
SNX1
|
16 |
1582521 |
1582641 |
cg17706173 |
0.7835224688 |
0.3236855023 |
IFT140
|
16 |
57147142 |
57147262 |
cg09100343 |
0.6293840758 |
0.2163426531 |
CPNE2
|
16 |
58231499 |
58231619 |
cg16570890 |
0.6791063582 |
0.2229238535 |
CSNK2A2
|
16 |
87541416 |
87541536 |
cg06723337 |
0.8006480781 |
0.3309506764 |
NA
|
16 |
89169798 |
89169918 |
cg05145233 |
0.60590424 |
0.2007694734 |
ACSF3
|
17 |
2304348 |
2304468 |
cg22188945 |
0.7132355571 |
0.3858541342 |
MNT
|
17 |
7482634 |
7482754 |
cg15095917 |
0.7065151168 |
0.5785444268 |
CD68
|
17 |
18854655 |
18854775 |
cg22159015 |
0.6353459241 |
0.2109526527 |
SLC5A10
|
17 |
38347543 |
38347663 |
cg20668644 |
0.6072404858 |
0.8419954529 |
RAPGEFL1
|
17 |
38478747 |
38478867 |
cg08580254 |
0.6341166925 |
0.6245268317 |
RARA
|
17 |
72745934 |
72746054 |
cg05245070 |
0.8878316511 |
0.5927578173 |
SLC9A3R1
|
18 |
19321646 |
19321766 |
cg25291907 |
0.7459762222 |
0.3789889437 |
MIB1
|
18 |
46447677 |
46447797 |
cg07661480 |
0.9216006768 |
0.2258655059 |
SMAD7
|
19 |
663033 |
663153 |
cg27297221 |
0.6151658465 |
0.4707300482 |
RNF126
|
19 |
2085226 |
2085346 |
cg02326386 |
1.6891350909 |
0.1928435344 |
MOBKL2A
|
19 |
3557478 |
3557598 |
cg18458026 |
0.7531306854 |
0.2154988356 |
C19orf28
|
19 |
15218653 |
15218773 |
cg04981492 |
0.7315780166 |
0.5005283445 |
SYDE1
|
19 |
15695239 |
15695359 |
cg27067425 |
1.2118209248 |
0.8905884347 |
NA
|
19 |
16682801 |
16682921 |
cg13630845 |
1.0457068888 |
0.4190268993 |
SLC35E1
|
19 |
19174771 |
19174891 |
cg25768103 |
0.9692324947 |
0.2463079679 |
SLC25A42
|
19 |
35454947 |
35455067 |
cg13412003 |
0.7024035872 |
0.1931523898 |
ZNF792
|
19 |
35633609 |
35633729 |
cg21105227 |
0.8229343388 |
0.8553915885 |
FXYD7;FXYD1
|
19 |
53098110 |
53098230 |
ch.19.57789982R |
3.4066718963 |
0.7783309751 |
NA
|
19 |
56186482 |
56186602 |
cg10802291 |
0.7218154695 |
0.2437737683 |
EPN1
|
19 |
58715617 |
58715737 |
cg19416570 |
0.6295396794 |
0.4363244639 |
ZNF274
|
2 |
8825847 |
8825967 |
cg06659057 |
1.117764663 |
0.3526208675 |
NA
|
2 |
9471171 |
9471291 |
cg06549275 |
0.6122434498 |
0.2397999845 |
ASAP2
|
2 |
182451477 |
182451597 |
cg22274745 |
0.973310137 |
0.713374049 |
CERKL
|
2 |
192109930 |
192110050 |
cg17167076 |
0.7451925256 |
0.6177831567 |
MYO1B
|
2 |
235512706 |
235512826 |
ch.2.235177505R |
2.5506292498 |
0.4026840223 |
NA
|
2 |
239112163 |
239112283 |
cg16101252 |
0.8543170331 |
0.3273364481 |
ILKAP
|
20 |
3451562 |
3451682 |
cg05928023 |
0.6481265693 |
0.574503526 |
ATRN
|
20 |
22995739 |
22995859 |
ch.20.22943799F |
4.6613590534 |
0.8478966268 |
NA
|
20 |
35381178 |
35381298 |
ch.20.741059R |
3.6099667053 |
0.7629989937 |
DSN1
|
3 |
16343137 |
16343257 |
cg16456087 |
0.6789975939 |
0.3034575124 |
OXNAD1
|
3 |
58456916 |
58457036 |
cg13575139 |
1.0156728993 |
0.6186794832 |
NA
|
3 |
72433777 |
72433897 |
cg10805254 |
1.1820370084 |
0.2502749589 |
RYBP
|
3 |
134371225 |
134371345 |
cg00310463 |
1.0863684156 |
0.49695302 |
KY
|
4 |
2813664 |
2813784 |
cg05336051 |
1.1598864999 |
0.3604126249 |
SH3BP2
|
4 |
56301571 |
56301691 |
cg05826175 |
2.1727957265 |
0.1823073923 |
CLOCK
|
4 |
124571325 |
124571445 |
cg18257996 |
0.6672094353 |
0.4719120447 |
NA
|
4 |
140661805 |
140661925 |
cg23206160 |
0.6237924752 |
0.6949826053 |
MAML3
|
5 |
472947 |
473067 |
cg17284070 |
0.6598847845 |
0.8367555434 |
LOC25845
|
5 |
7619923 |
7620043 |
cg26129110 |
0.6621186528 |
0.3695590235 |
ADCY2
|
5 |
10479670 |
10479790 |
cg01053766 |
0.6736994287 |
0.3516795265 |
NA
|
5 |
149865369 |
149865489 |
cg13933692 |
0.6100229219 |
0.2041172294 |
NA
|
5 |
179125770 |
179125890 |
cg09173344 |
0.8351629738 |
0.1826979689 |
CANX
|
6 |
2766132 |
2766252 |
cg02754722 |
1.0906682368 |
0.4421082644 |
WRNIP1
|
6 |
13428528 |
13428648 |
cg03606646 |
0.6368303445 |
0.3597661558 |
GFOD1
|