Ns126:Calendar/NOTES/2015-4-21: Difference between revisions
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*corresponding table is as the following. Pan-Cancer Methylation 450K dataset were collected from TCGA Project to identify optional padloc regions. | *corresponding table is as the following. Pan-Cancer Methylation 450K dataset were collected from TCGA Project to identify optional padloc regions. | ||
{| | {| {{table}} | ||
| Symbol||Case||Control||Cancer ||肿瘤名称 | | align="center" style="background:#f0f0f0;"|'''Symbol''' | ||
| align="center" style="background:#f0f0f0;"|'''Case''' | |||
| align="center" style="background:#f0f0f0;"|'''Control''' | |||
| align="center" style="background:#f0f0f0;"|'''Cancer''' | |||
| align="center" style="background:#f0f0f0;"|'''肿瘤名称''' | |||
|- | |- | ||
| KIRC||160||160||Kidney renal clear cell carcinoma||肾透明细胞癌 | | KIRC||160||160||Kidney renal clear cell carcinoma||肾透明细胞癌 | ||
Line 48: | Line 52: | ||
|- | |- | ||
| LUAD||26||26||Lung adenocarcinoma||肺鳞癌 | | LUAD||26||26||Lung adenocarcinoma||肺鳞癌 | ||
|- | |||
| | |||
|} | |} | ||
Line 119: | Line 125: | ||
[[File:GSI2.png]] | [[File:GSI2.png]] | ||
s(j): average methylation in individual group j | *s(j): average methylation in individual group j | ||
S(max): average methylation in the group with highest methylation level | *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. | |||
{| class="wikitable" style="text-align: right; color: red;" | |||
| 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. | |||
{| class="wikitable" style="text-align: right; color: red;" | |||
| ||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: | |||
<nowiki> | |||
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)</nowiki> | |||
*The results were showed as the following table. Top 5 important biomarkers for each cancer of the specific tissue and status were list. | |||
{| class="wikitable" style="text-align: right; color: red;" | |||
| 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 | |||
|- | |||
|} | |||
<nowiki> | |||
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) </nowiki> | |||
* top 75 important variables selected from random forest algorithm | |||
*VIM: variable importance | |||
*GSI: group specific index | |||
{| class="wikitable" style="text-align: right; color: red;" | |||
| 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||LncRNA|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||LncRNA|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||LncRNA|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||LncRNA|NA | |||
|- | |||
| 12||108678186||108678306||cg07653800||0.7859823353||0.4191516247||LncRNA|NA | |||
|- | |||
| 12||125819537||125819657||cg03428945||0.6217564925||0.4321944141||TMEM132B | |||
|- | |||
| 13||114917237||114917357||cg20856545||0.8345343125||0.4269503759||LncRNA|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||LncRNA|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||LncRNA|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||LncRNA|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||LncRNA|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||LncRNA|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||LncRNA|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||LncRNA|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||LncRNA|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||LncRNA|NA | |||
|- | |||
| 5||149865369||149865489||cg13933692||0.6100229219||0.2041172294||LncRNA|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 | |||
|- | |||
|} |
Latest revision as of 01:00, 18 February 2016
- 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 |