Ns126:Calendar/NOTES/2015-4-21
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- 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
- collect methylation 450 data of cancer tissues and normals from TCGA Project
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
- 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.
- 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 |