Progress batman

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[[배형섭]]
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{| align="right" cellpadding=15
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| __TOC__
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|}
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<!-- :[[배형섭|go to 배형섭 페이지]] -->
==2011==
==2011==
*Reference: [[media:JMB_adda.pdf|PDF download]]
*Reference: [[media:JMB_adda.pdf|PDF download]]
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ADDA pmid=12706730
ADDA pmid=12706730
</biblio>
</biblio>
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==Schedule==
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<!-- #[[media:2월첫째주.xls|2월첫째주 ]] -->
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==ADDA algorithm==
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===How to get residue correlation matrix===
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*sample data: [[media:sample.tab.txt|sample.tab]]
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<pre style col="blue">
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# set working directory
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setwd("/Users/igchoi/Downloads/")
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# read blast table
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tmp = read.table("sample.tab",sep="\t")
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# check dimension of table
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dim(tmp)
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# change simpler GI number (skip this if you don't understand)
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tmp[,1] = sapply(as.vector(tmp[,1]), function(x) {
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  y = strsplit(x,"\\|"); return(unlist(y)[2]) })
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tmp[,2] = sapply(as.vector(tmp[,2]), function(x) {
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  y = strsplit(x,"\\|"); return(unlist(y)[2]) })
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# check the result
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tmp[1:5,]
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# aligned region: V7 V8, V9 V10
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# query length = 590 residues
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count.que = matrix(0,nrow(tmp),590)  # <- build empty 'aligned' matrix
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for(i in 1:nrow(tmp)) {
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  res = unlist(tmp[i,7:8])
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  print(res)
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  count.que[i,c(res[1]:res[2])] = 1
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}
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# check aligned region (blue colored regions are aligned with other proteins - central region)
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image(t(count.que),col=c("white","blue"))
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# count number of neighbors occurring both position i and j (using 'aligned' matrix)
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corr = matrix(0,590,590)  # <- set empty correlation matrix (590 x 590)
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for (i in 1:ncol(count.que)) {
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  for (j in i:ncol(count.que)) {
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    print(paste(i,j))
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    chk = count.que[,i]+count.que[,j]
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    cnt = length(which(chk==2))
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    corr[i,j] = cnt
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  }
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}
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# range of number of neighbors (among 52 hits)
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range(corr)
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# check highly correlated region in the correlation matrix
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image(corr,col=topo.colors(10))
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</pre>
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===How to parse domains from the correlation matrix===
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:Function for Splitting Domains
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<pre>
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#
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splitPos <- function(x,m=start,n=end) { # x = corr.matrix
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  chi = NULL; corr = x
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  for (i in m:(n-1)) {
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    c1 = sum(corr[m:i,m:i])
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    a = sum(corr[m:i,(i+1):n])
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    c2 = sum(corr[(i+1):n,(i+1):n])
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    chi2 = (((c1*c2)-(a*a))^2)/((c1+a)^2*(c2+a)^2)
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#    print(paste(i,c1,a,c2,chi2))
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    chi = c(chi,chi2)
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  }
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  pos = which(chi==max(chi))
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  return(c(m,pos,pos+1,n))
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}
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tmp = splitPos(corr,1,590)  # split function returns a vector (start,splitPos,splitPos+1,end)
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tmp1 = splitPos(corr,tmp[1],tmp[2])
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tmp2 = splitPos(corr,tmp[3],tmp[4])
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</pre>
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:Old
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<pre style col="blue">
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A<- 1
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B<-590
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chi = NULL
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point =NULL
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for (i in A:B-3){
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      c1 = sum(corr[(A+1):(i+2),(A+1):(i+2)])
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        a = sum(corr[(A+1):(i+2), (i+2):(B-1)])
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      c2 = sum(corr[(i+2):(B-1),(i+2):(B-1)])
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        x = (((c1*c2)-(a*a))^2)/(((c1+a)^2)*((c2+a)^2))
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      # print(paste(A,c1,a,c2 ,x))
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      chi = c(chi,x)
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      p1 = which(chi==max(chi))
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      B<- p1
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      point = c(point,p1)
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      # chi[i-9,1:2] = c(A,x)
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}
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</pre>

Latest revision as of 08:05, 22 April 2011

Contents

2011

Error fetching PMID 12706730:
  1. Error fetching PMID 12706730: [ADDA]

Schedule

ADDA algorithm

How to get residue correlation matrix

# set working directory
setwd("/Users/igchoi/Downloads/")

# read blast table
tmp = read.table("sample.tab",sep="\t")

# check dimension of table
dim(tmp)

# change simpler GI number (skip this if you don't understand)
tmp[,1] = sapply(as.vector(tmp[,1]), function(x) {
  y = strsplit(x,"\\|"); return(unlist(y)[2]) })
tmp[,2] = sapply(as.vector(tmp[,2]), function(x) {
  y = strsplit(x,"\\|"); return(unlist(y)[2]) })

# check the result
tmp[1:5,]

# aligned region: V7 V8, V9 V10
# query length = 590 residues
count.que = matrix(0,nrow(tmp),590)  # <- build empty 'aligned' matrix
for(i in 1:nrow(tmp)) {
  res = unlist(tmp[i,7:8])
  print(res)
  count.que[i,c(res[1]:res[2])] = 1
}

# check aligned region (blue colored regions are aligned with other proteins - central region)
image(t(count.que),col=c("white","blue"))

# count number of neighbors occurring both position i and j (using 'aligned' matrix)
corr = matrix(0,590,590)  # <- set empty correlation matrix (590 x 590)

for (i in 1:ncol(count.que)) {
  for (j in i:ncol(count.que)) {
    print(paste(i,j))
    chk = count.que[,i]+count.que[,j]
    cnt = length(which(chk==2))
    corr[i,j] = cnt
  }
}

# range of number of neighbors (among 52 hits)
range(corr)

# check highly correlated region in the correlation matrix
image(corr,col=topo.colors(10))

How to parse domains from the correlation matrix

Function for Splitting Domains 
#
splitPos <- function(x,m=start,n=end) { # x = corr.matrix
  chi = NULL; corr = x
  for (i in m:(n-1)) {
    c1 = sum(corr[m:i,m:i])
    a = sum(corr[m:i,(i+1):n])
    c2 = sum(corr[(i+1):n,(i+1):n])
    chi2 = (((c1*c2)-(a*a))^2)/((c1+a)^2*(c2+a)^2)
#    print(paste(i,c1,a,c2,chi2))
    chi = c(chi,chi2)
  }
  pos = which(chi==max(chi))
  return(c(m,pos,pos+1,n))
}

tmp = splitPos(corr,1,590)  # split function returns a vector (start,splitPos,splitPos+1,end)

tmp1 = splitPos(corr,tmp[1],tmp[2])
tmp2 = splitPos(corr,tmp[3],tmp[4])
Old 
A<- 1
B<-590 
chi = NULL
point =NULL
for (i in A:B-3){
       c1 = sum(corr[(A+1):(i+2),(A+1):(i+2)])
        a = sum(corr[(A+1):(i+2), (i+2):(B-1)])
       c2 = sum(corr[(i+2):(B-1),(i+2):(B-1)])
        x = (((c1*c2)-(a*a))^2)/(((c1+a)^2)*((c2+a)^2))
      # print(paste(A,c1,a,c2 ,x))
       chi = c(chi,x)
       p1 = which(chi==max(chi))
       B<- p1
       point = c(point,p1)
        
      # chi[i-9,1:2] = c(A,x)
}
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