Ns126:Calendar/NOTES/2015-4-21: Difference between revisions
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[[File:Gsi.distribution.jpeg]] | [[File:Gsi.distribution.jpeg]] | ||
*745 CpGs or regions whose GSI>0.9. 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) | *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;" | |||
| ||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 | |||
|} |
Revision as of 01:31, 22 April 2015
- 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()
- 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.
- 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 |