- 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.
pheno |
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.
|
pheno |
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), as the following: