CNV and network analysis references, human diseases

PubMed ID: 24244640;
CNVannotator: a comprehensive annotation server for copy number variation in the human genome.

PMID:22729399 for PPI;
Proteome-wide prediction of protein-protein interactions from high-throughput data.

PMID:24067414 for gene network,
Gaussian graphical model for identifying significantly responsive regulatory networks from time course high-throughput data.

PMID:22360268 for network-based disease analysis; and
Network-based analysis of complex diseases.

PMID:14735121 for network biology
Network biology: understanding the cell's functional organization.

20150718:
http://www.nature.com/nature/journal/v464/n7289/abs/nature08979.html
Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls

http://www.nature.com/nature/journal/v464/n7289/abs/nature08516.html
Origins and functional impact of copy number variation in the human genome

CNV basics, human, African American, McElroy 2009

McElroy 2009 BMC Genetics

3.5 CNV per individual in African Americans
4.8 CNV per individual in white (a bit inflated due to the reference)
when the reference is a pool of 50 randomly chosen AA women were used as references.

Except two CNV (one on chr17 and one on chr15), all other CNVs vary within 10% between African American and Caucasians.

Yi Jiang defence, CNV paoper

CNV annotator on HSPB1 and ATP2A1

Use gene-specific search on HSPB1

CNV web site

http://utclab.ddns.net/cnv_research/ucsc_db.xhtml

CNV papers to read

PubMed ID: 24244640; 
CNVannotator: a comprehensive annotation server for copy number variation in the human genome.

PMID:22729399 for PPI; 
Proteome-wide prediction of protein-protein interactions from high-throughput data.

PMID:24067414 for gene network, 
Gaussian graphical model for identifying significantly responsive regulatory networks from time course high-throughput data.

PMID:22360268 for network-based disease analysis; and 
Network-based analysis of complex diseases.

PMID:14735121 for network biology
Network biology: understanding the cell's functional organization.

disconnected clusters from MCL

Our MCL clusters at high I-values give genes in clusters with disconnected nodes. This is a problem has been discussed before:

http://larsjuhljensen.wordpress.com/2010/03/01/analysis-markov-clustering-and-the-case-of-the-unnatural-clusters/

According to the post, this is not an "error", but a possible though "unexpected" result. Looks like "--force-connected=y" option might solve this problem. I will redo the clustering and then check.

two step ranking

q< 0.2 and p<0.1 (top candidates should often be better than 0.2), chose only consistent clusters.

then sort from low to high by q1*q2

CYP2E1 needs discussion

description of I values needs changes.

methods needs update.

focus on consistent 3 clusters HSPB1, ATP2A1, CYP2E1. 

   1. delete crx
2. add CYP2E1
3. prepare pic for clusters
4. change methodology
5. clustering descriptive statistics. 

robust.fdr on p-values

rm(list=ls())
source("robust-fdr.R")

setwd("~... ... CNV/pvalues,qvalues/RTFET")
infiles = list.files(, pattern="txt")
  
for( i in 1:length(infiles)){ 
  tb = read.table(infiles[i], sep="\t", header=F)
  
  tmp1 = tb[tb$V4<1.0, ];
  if( length(tmp1[,1]) >2  ) {
    hist(tmp1$V2)
    ret = robust.fdr(tmp1$V4, sides=1, discrete=T )
    tmp1$q1 = ret$q
    tb$q1 = tmp1$q1[match(tb$V1, tmp1$V1)]
  } else {
    tb$q1 = NA;
  }
  summary(tb)
  tb[tb$q1<0.15 & (!is.na(tb$q1)), ]
  
  outfile = paste("output_robustFDR/", infiles[i], ".csv", sep='')
  write.csv(tb, outfile,, row.names=F, quote=F)
}

network hSNP, CNV note

CNV genes show no SNP with frequency from Bio-Q and Ensemble.

http://bioq.saclab.net/query/submit.php?db=bioq_dbsnp_human_138

cluster 175, 214, health diparity

 

Todo: sequence comparison of HSPB1 and CRY proteins