Progress batman
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==Schedule== | ==Schedule== | ||
| + | <!-- #[[media:2월첫째주.xls|2월첫째주 ]] --> | ||
| - | #[[ | + | |
| + | ==ADDA algorithm== | ||
| + | ===How to get residue correlation matrix=== | ||
| + | <pre> | ||
| + | # set working directory | ||
| + | setwd("/Users/igchoi/Downloads/") | ||
| + | # read blast table | ||
| + | tmp = read.table("sample.tab",sep="\t") | ||
| + | # dimension | ||
| + | dim(tmp) | ||
| + | # extract gi number | ||
| + | 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]) }) | ||
| + | # | ||
| + | tmp[1:5,] | ||
| + | # V7-V8 V9-V10 (aligned?) | ||
| + | # query = 590 residues | ||
| + | count.que = matrix(0,nrow(tmp),590) | ||
| + | for(i in 1:nrow(tmp)) { | ||
| + | res = unlist(tmp[i,7:8]) | ||
| + | print(res) | ||
| + | count.que[i,c(res[1]:res[2])] = 1 | ||
| + | } | ||
| + | image(t(count.que),col=c("white","blue")) | ||
| + | # count number of neighbors occurring both position i and j | ||
| + | corr = matrix(0,590,590) | ||
| + | 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(corr) | ||
| + | image(corr,col=topo.colors(10)) | ||
| + | </pre> | ||
Revision as of 12:55, 14 February 2011
Contents |
2011
- Reference: PDF download
Error fetching PMID 12706730:
- Error fetching PMID 12706730:
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")
# dimension
dim(tmp)
# extract gi number
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]) })
#
tmp[1:5,]
# V7-V8 V9-V10 (aligned?)
# query = 590 residues
count.que = matrix(0,nrow(tmp),590)
for(i in 1:nrow(tmp)) {
res = unlist(tmp[i,7:8])
print(res)
count.que[i,c(res[1]:res[2])] = 1
}
image(t(count.que),col=c("white","blue"))
# count number of neighbors occurring both position i and j
corr = matrix(0,590,590)
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(corr)
image(corr,col=topo.colors(10))
