R/dCor.R

In andymckenzie/DGCA: Differential Gene Correlation Analysis

#' @title Differential correlation between two conditions.
#' @description Z-transforms correlation coefficients (Pearson or Spearman) and then calculates the difference in z-scores between the two conditions divided by the square root of the standard errors (which is inversely proportion to the sample sizes used to calculate the correlations).
#' @param rho1 Numeric vector of correlation coefficients in condition 1.
#' @param rho2 Numeric vector of correlation coefficients in condition 2.
#' @param n1 Numeric vector of the number of samples used in the correlation calculations in condition 1.
#' @param n2 Numeric vector of the number of samples used in the correlation calculations in condition 2.
#' @param corrType The correlation type of the analysis, limited to "pearson" or "spearman".
#' @references Tests For Rank Correlation Coefficients, I. http://biomet.oxfordjournals.org/content/44/3-4/470.full.pdf+html
#' @return Numeric vector with scaled difference in z-scores of correlations between the two conditions.
#' @examples
#' rho1 = runif(100, -1, 1); rho2 = runif(100, -1, 1)
#' n1 = rep(100, 100); n2 = rep(110, 100)
#' dcorrs_res = dCorrs(rho1, n1, rho2, n2)
#' @export
dCorrs <- function(rho1, n1, rho2, n2, corrType = "pearson"){

	if(!(all.equal(length(rho1), length(n1), length(rho2), length(n2)))) stop("All of the input vectors must be the same length.")

	zr1 = atanh(rho1)
	zr2 = atanh(rho2)

	if(corrType == "pearson"){
		diff12 = (zr2 - zr1)/sqrt((1/(n1 - 3)) + (1/(n2 - 3)))
	}

	if(corrType == "spearman"){
		#http://biomet.oxfordjournals.org/content/44/3-4/470.full.pdf+html
		diff12 = (zr2 - zr1)/sqrt((1.06/(n1 - 3)) + (1.06/(n2 - 3)))
	}

	return(diff12)

}