- 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
|