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#'A function to get the correlation coefficient between SHAPE trace change patterns.
#' @title getPatternCC
#' @aliases getPatternCC
#' @keywords pattern correlation-coefficient RNA
#' @usage getPatternCC(sample, base=sample[1,], margin=1, tol=0.1)
#' @param sample A numeric matrix containing values to be compared (e.g. a set of mutant SHAPE traces).
#' @param base An optional numeric vector containing the value to which the samples are to be compared (e.g. a wild type SHAPE trace). Default is the first trace in sample.
#' @param margin An optional number indicating if the samples are organized by rows or columns, where 1 indicates rows and 2 indicates columns. Default is 1.
#' @param tol An optional number indicating the tolerance for the change. Default is 0.1.
#' @export
#' @details The pattern for a single SHAPE reacitivity trace is the pattern of increase in reactivity or decrease in reactivity between nucleotides. If the change is less than the tolerance value, it is considered a none change. The pattern change value is the Pearson correlation coefficient between the base vector pattern and the pattern of each row (or column) in sample.
#' @return A numeric vector of pattern correlation coefficients.
#' @author Chanin Tolson
#' @seealso \code{\link{getFeatures}}
#' @examples #sample data
#' sample = matrix(sample(1:100), ncol=10)
#' #normalize
#' samp_norm = normalize(sample)
#' #reduce noise
#' samp_nreduce = reduceNoise(samp_norm, trim=1, high=4)
#' #get pattern correlation coefficient
#' pat = getPatternCC(samp_nreduce)
#'
getPatternCC = function(sample, base=sample[1,], margin=1, tol=0.1){
#set optional paramater margin
if(missing(margin)) {
margin = 1
} else {
if(!(margin %in% c(1,2))){
warning("Margin value not valid. Margin set to default.")
margin = 1
}
if(margin==2){
sample = t(sample)
}
}
#set optional paramater base
if(missing(base)) {
base = sample[1,]
} else {
base = base
}
#set optional paramater tol
if(missing(tol)){
tol = 0.1
} else {
if(tol < 0){
warning("Tol value not valid. Tol set to default.")
tol = 0.1
}
tol = tol
}
#values cannot be negative (raise the minimum value to at least 0)
if(sum(sample<0, na.rm=T)>0){
sample = sample-min(sample, na.rm=T)
base = base-min(sample, na.rm=T)
}
if(sum(base<0, na.rm=T)>0){
base = base-min(base, na.rm=T)
sample = sample-min(base, na.rm=T)
}
#function to get pattern change correlation
pattern = function(samp, base, tol){
#initialize
samp = as.numeric(samp)
base = as.numeric(base)
patterns = rep(0, length(samp)-1)
patternb = rep(0, length(base)-1)
#get samp pattern
pat = (c(samp, NA) - c(NA, samp))[2:(length(samp))]
patterns[pat>tol] = 1
patterns[pat<(-tol)] = -1
#get base pattern
pat = (c(base, NA) - c(NA, base))[2:(length(base))]
patternb[pat>tol] = 1
patternb[pat<(-tol)] = -1
return(cor(patterns, patternb, method="pearson", use="pairwise.complete.obs"))
}
#calculate pattern change
options(warn=-1)
pat = apply(sample, 1, pattern, base=base, tol=tol)
options(warn=0)
pat[is.na(pat)] = 1
sample[,is.na(base)]=NA
pat = pat
#return pattern change
return(pat)
}
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