#' Calculates differential correlation statistics between a specified variable and all other variables within a different dataset.
#'
#' Creates a table of pairwise correlations statistics, with separation of two groups for comparison.
#' This can be used as an exploratory tool to investigate correlations between a specific variable of interest and all other variables within a separate dataset.
#'
#' @param variable character string indicating the variable of interest for differential correlation (within \code{data1})
#' @param data1 first dataframe containing variable of interest
#' @param data2 second dataframe containing variable to compare
#' @param sample_col character string defining the sample or identifying column in both datasets (common to both dataframes)
#' @param group character string defining the grouping variable for comparative differential correlations
#' @param ordered character string defining which column the table should be ordered by. Choose from \code{g1cor}, \code{g1p}, \code{g2cor}, \code{g2r}, \code{fisher} (default) and \code{BH}.
#'
#' @return a table (or dataframe) with Pearson correlation coefficients (r), associated p-values, fisher r-to-z statistic and BH p-value correlation for each correlation pair
#'
#' @author Emily Mears, \email{mears.emilyrose@gmail.com}, Matthew Grant, \email{mgra576@aucklanduni.ac.nz}
#' @author Ben Day, \email{benjamindayengineer@gmail.com}
#'
#' @examples
#'## Load example dataframes
#'df1 <- read.csv("example_data/excorr_df1.csv")
#'df2 <- read.csv("example_data/excorr_df2.csv")
#'
#'## Run function
#'multi.cor.var.compare(variable = "NTRpFI", df1, df2, sample_col = "sample", group = "sex")
#'multi.cor.var.compare(variable = "NTRpFI", df1, df2, sample_col = "sample", group = "sex", ordered = "fisher")
#'
#' @export
#'
multi.cor.var.compare <- function(variable, data1, data2, sample_col, group, ordered = "fisher"){
##--- First check data inputs
# Convert group columns to char
data1[, which(colnames(data1) == group)] = as.character(data1[, which(colnames(data1) == group)])
data2[, which(colnames(data2) == group)] = as.character(data2[, which(colnames(data2) == group)])
# Check for all-zero variables/cols
if (TRUE %in% lapply(colnames(data1), function(x) all(data1[, x] == 0)) == TRUE) {
data1 <- data1[, colSums(data1 != 0) > 0]
#a <- sapply(colnames(data1), function(x) all(data1[, x] == 0), simplify = FALSE)
#zvars <- names(a[a == TRUE])
warning(paste("Some columns contain only zeros and were removed before calculation."))
#warning(paste(zvars, "removed.", sep = " "))
}
if (TRUE %in% lapply(colnames(data2), function(x) all(data2[, x] == 0)) == TRUE) {
data2 <- data2[, colSums(data2 != 0) > 0]
#a <- sapply(colnames(data2), function(x) all(data2[, x] == 0), simplify = FALSE)
#zvars <- names(a[a == TRUE])
warning(paste("Some columns contain only zeros and were removed before calculation."))
#cat("Removed variables: ", zvars, "\n", sep="\t")
}
# Stop function if sample columns do not match
if (any(data1[, sample_col] != data2[, sample_col]) == TRUE)
stop("Sample column must be identical across datasets (and in the same order).")
# Check variable exists in data
if (!(variable %in% colnames(data1)))
stop("Variable not in dataset 1")
# Check if length of datasets is same
if(max(lengths(data1)) != max(lengths(data2))) {
stop("Datasets must contain same number of rows")
}
# Check if group column contains more than 2 groups
if(length(unique(as.character(data1[data1[,group] != as.character(data1[,group][1]),group]))) > 1) {
stop("More than 2 groups in data1 grouping variable")}
if(length(unique(as.character(data2[data2[,group] != as.character(data2[,group][1]),group]))) > 1) {
stop("More than 2 groups in data2 grouping variable")}
##----- Define functions to be used
# Fisher's Z-transformation
compcor <- function(n1, r1, n2, r2){
# ad hoc process
num1a <- which(r1 >= 0.99)
num2a <- which(r2 >= 0.99)
r1[num1a] <- 0.99
r2[num2a] <- 0.99
num1b <- which(r1 <= -0.99)
num2b <- which(r2 <= -0.99)
r1[num1b] <- -0.99
r2[num2b] <- -0.99
# z
z1 <- atanh(r1)
z2 <- atanh(r2)
# difference
dz <- (z1 - z2)/sqrt(1/(n1 - 3) + (1/(n2 - 3)))
# p-value
pv <- 2*( 1 - pnorm(abs(dz)) )
return(list(diff=dz, pval=pv))
}
# Find both group names
group_1_name = as.character(data2[,group][1])
group_2_name = as.character(data2[data2[,group] != as.character(data2[,group][1]),group][1])
# Check if group column contains more than 2 groups
if(length(unique(as.character(data2[data2[,group] != as.character(data2[,group][1]),group]))) > 1)
stop("More than 2 groups in grouping variable")
#-----------------
# Begin pairwise correlations
cond1_cor_pvals = list()
for(i in 1:ncol(data2[, lapply(data2, class) == "numeric"])){
cond1_cor_pvals[i] = cor.test(method = "pearson", x = data1[data1[,group] == as.character(data1[,which(colnames(data1) == group)])[1], variable],
y = data2[data2[,group] == as.character(data2[,which(colnames(data2) == group)])[1], lapply(data2, class) == "numeric"][,i])$p.value
}
cond2_cor_pvals = list()
for(i in 1:ncol(data2[, lapply(data2, class) == "numeric"])){
cond2_cor_pvals[i] = cor.test(method = "pearson", x = data1[data1[,group] != as.character(data1[,which(colnames(data1) == group)])[1], variable],
y = data2[data2[,group] != as.character(data2[,which(colnames(data2) == group)])[1], lapply(data2, class) == "numeric"][,i])$p.value
}
# Vectorise cor.test pvalue outputs
cond1_cor_pvals = unlist(cond1_cor_pvals)
cond2_cor_pvals = unlist(cond2_cor_pvals)
cond1_cor <- cor(x = data2[data2[,group] == as.character(data2[,which(colnames(data2) == group)])[1], lapply(data2, class) == "numeric"],
y = data1[data1[,group] == as.character(data1[,which(colnames(data1) == group)])[1], variable])
cond2_cor <- cor(x = data2[data2[,group] != as.character(data2[,which(colnames(data2) == group)])[1], lapply(data2, class) == "numeric"],
y = data1[data1[,group] != as.character(data1[,which(colnames(data1) == group)])[1], variable])
# Combine variable correlations and pvlaues for each group
var_cor <- cbind(cond1_cor, cond1_cor_pvals, cond2_cor, cond2_cor_pvals)
# Then append Fisher r to z as a 3rd column and order
fisher <- compcor(n1 = length(data1[data1[,group] == as.character(data1[,which(colnames(data1) == group)])[1], variable]),
r1 = var_cor[,1],
n2 = length(data1[data1[,group] != as.character(data1[,which(colnames(data1) == group)])[1], variable]),
r2 = var_cor[,3])$pval
# Append fisher transformation and pval adjustments for multiple testing
p_adjust <- p.adjust(fisher, method = "BH", n = length(fisher))
var_cor_fish <- cbind(var_cor, fisher, p_adjust)
# Rename columns
colnames(var_cor_fish) <- c(paste(variable, "~ Correlations in", group_1_name),
"Correlation p-value",
paste(variable, "~ Correlations in", group_2_name),
"Correlation p-value",
"Fisher R to Z",
"BH p-value adjustment")
# Round correlation coefficients
var_cor_fish[,1] <- round(var_cor_fish[,1], digits = 2)
var_cor_fish[,3] <- round(var_cor_fish[,3], digits = 2)
# Significant figures p-values
var_cor_fish[,2] <- signif(var_cor_fish[,2], digits = 4)
var_cor_fish[,4] <- signif(var_cor_fish[,4], digits = 4)
var_cor_fish[,5] <- signif(var_cor_fish[,5], digits = 4)
var_cor_fish[,6] <- signif(var_cor_fish[,6], digits = 4)
# Ordering options
if(ordered == "fisher"){
var_cor_fish <- var_cor_fish[order(var_cor_fish[,5]),]
}
else if(ordered == "g1cor"){
var_cor_fish <- var_cor_fish[order(var_cor_fish[,1]),]
}
else if(ordered == "g1p"){
var_cor_fish <- var_cor_fish[order(var_cor_fish[,2]),]
}
else if(ordered == "g2cor"){
var_cor_fish <- var_cor_fish[order(var_cor_fish[,3]),]
}
else if(ordered == "g2p"){
var_cor_fish = var_cor_fish[order(var_cor_fish[,4]),]
}
else if(ordered == "BH"){
var_cor_fish = var_cor_fish[order(var_cor_fish[,6]),]
}
else {
var_cor_fish <- var_cor_fish[order(var_cor_fish[,5]),]
warning("Undefined ordering column.")
}
# Return output
return(as.data.frame(var_cor_fish))
}
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