- 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