Progress batman

From CSBLwiki

Revision as of 07:47, 14 April 2011 (view source) Batman (Talk | contribs) (→How to parse domains from the correlation matrix) ← Older edit		Revision as of 15:38, 14 April 2011 (view source) Igchoi (Talk | contribs) (→ADDA algorithm) Newer edit →
Line 68:		Line 68:
	===How to parse domains from the correlation matrix===		===How to parse domains from the correlation matrix===
		+	:Function for Splitting Domains
		+	<pre>
		+	#
		+	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])
		+	</pre>
		+
		+	:old
	<pre style col="blue">		<pre style col="blue">
	chi = NULL		chi = NULL
Line 79:		Line 103:
	chi = c(chi,x)		chi = c(chi,x)
	p1 = which(chi==min(chi))+9		p1 = which(chi==min(chi))+9
-	# chi[i-9,1:2] = c(A,x)
-	}
-	chi1= NULL
-	for (i in 10:p1){
-	A = i
-	c1 = sum(corr[1:i,1:i])
-	a = sum(corr[1:i ,(i+1):(ncol(count.que))])
-	c2 = sum(corr[(i+1):(ncol(count.que)),(i+1):(ncol(count.que))])
-	x =  (((c1*c2)-(a*a))^2)/(((c1+a)^2)*((c2+a)^2))
-	# print(paste(A,c1,a,c2 ,x))
-	chi1 = c(chi1,x)
-	p2 = which(chi1==min(chi1))+9
-	# chi[i-9,1:2] = c(A,x)
-	}
-	chi2=NULL
-	for (i in p1:(ncol(count.que)-10)){
-	A = i
-	c1 = sum(corr[1:i,1:i])
-	a = sum(corr[1:i ,(i+1):(ncol(count.que))])
-	c2 = sum(corr[(i+1):(ncol(count.que)),(i+1):(ncol(count.que))])
-	x =  (((c1*c2)-(a*a))^2)/(((c1+a)^2)*((c2+a)^2))
-	# print(paste(A,c1,a,c2 ,x))
-	chi2 = c(chi2,x)
-	p3 = which(chi2==min(chi2))+p1
	# chi[i-9,1:2] = c(A,x)		# chi[i-9,1:2] = c(A,x)
	}		}
	</pre>		</pre>

---

Revision as of 15:38, 14 April 2011

Contents 1 2011 2 Schedule 3 ADDA algorithm 3.1 How to get residue correlation matrix 3.2 How to parse domains from the correlation matrix

2011

• Reference: PDF download

1. Error fetching PMID 12706730: [ADDA]

Schedule

ADDA algorithm

How to get residue correlation matrix

• sample data: sample.tab

# 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

chi = NULL
for (i in 10:(ncol(count.que)-10)){
        A = i
       c1 = sum(corr[1:i,1:i])
        a = sum(corr[1:i ,(i+1):(ncol(count.que))])
       c2 = sum(corr[(i+1):(ncol(count.que)),(i+1):(ncol(count.que))])
       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==min(chi))+9
      # chi[i-9,1:2] = c(A,x)
}