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#' Cross covarianse feature vector
#' @description The PSSM-CC variable measures the correlation of two different properties between two residues
#'separated by a distance of lg along the sequence.
#' @param pssm_name name of PSSM Matrix file
#' @param g shortest protein length in dataset minus one
#' @import utils
#' @return feature vector of length 3800
#' @references
#' Dong, Q., Zhou, S. and Guan, J. (2009) A new taxonomy-based protein fold recognition approach
#' based on autocross-covariance transformation, Bioinformatics, 25, 2655-2662.
#' @export
#' @examples
#' X<-pssm_cc(system.file("extdata","C7GQS7.txt.pssm",package="PSSMCOOL"))
pssm_cc <- function(pssm_name,g=10){ #g smaler than shortest protein length in database
x<-read.delim(pssm_name,skip = 2,sep = "",header = FALSE)
x<-x[-1,-c(1,23:44)]
d<-which(x=="Lambda")
if(length(d)!=0){
x<-x[-c(d:dim(x)[1]),]
}
x<-x[,-1]
colnames(x)<-NULL
rownames(x)<-NULL
x<-as.matrix(x)
mode(x)<-"integer"
s<-x
L<-dim(s)[1]
sbar<-apply(s,2,mean)
names(sbar)<-NULL
sbar<-round(sbar,digits = 4)
CC<-matrix(0,nrow = g,ncol = 380)
for(pg in 0:(g-1)){
lg <- L - pg -1
for(pj_1 in 0:19){
sum_j_1 <- 0
for (i in 0:(L-1)) {
sum_j_1 <- sum_j_1 + s[i+1,pj_1+1]
}
sum_j_1 <- sum_j_1 / L
for(pj_2 in 0:19){
if(pj_2 != pj_1){
sum_j_2 <- 0
for(i in 0:(L-1)){
sum_j_2 <- sum_j_2 + s[i+1,pj_2+1]
}
sum_j_2 <- sum_j_2 / L
pssm_acjg = 0
for(i in 0:(lg-1)){
pssm_acjg = pssm_acjg + (s[i+1,pj_1+1]-sum_j_1) * (s[i+pg+1,pj_2+1]-sum_j_2)
}
pssm_acjg = pssm_acjg / lg
if(pj_1 < pj_2){
CC[pg,19*pj_1+(pj_2 -1)] = pssm_acjg
} else {
CC[pg,19*pj_1+(pj_2)] = pssm_acjg
}
}
}
}
}
vec<-c()
for(i in 1:g){
vec<-c(vec,CC[i,])
}
vec<-round(vec,digits = 4)
return(vec)
}
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