R/plotFunction_v1.4.R

In pwrEWAS: A user-friendly tool for comprehensive power estimation for epigenome wide association studies (EWAS)

#' @title Plot function to create a power plot
#'
#' @description pwrEWAS_powerPlot create a figure with power (with 95-percentile interval (2.5% & 97.5%)) as a function sample size for different effect sizes
#' 
#' @param data "powerArray" attribute within the pwrEWAS object create by pwrEWAS.
#' @param sd FALSE if targetDelta was specified in pwrEWAS, and TRUE if deltaSD was specified in pwrEWAS.
#'  
#' @return pwrEWAS_powerPlot return a figure displaying power as a function sample size for different effect sizes
#' 
#' @export
#' 
#' @examples
#' outDelta <- pwrEWAS(minTotSampleSize = 10,
#'     maxTotSampleSize = 20,
#'     SampleSizeSteps = 10,
#'     NcntPer = 0.5,
#'     targetDelta = c(0.2, 0.5),
#'     J = 1000,
#'     targetDmCpGs = 10,
#'     tissueType = "Adult (PBMC)",
#'     detectionLimit = 0.01,
#'     DMmethod = "limma",
#'     FDRcritVal = 0.05,
#'     core = 2,
#'     sims = 30)
#' pwrEWAS_powerPlot(data = outDelta$powerArray, sd = FALSE)
#' outSD <- pwrEWAS(minTotSampleSize = 10,
#'     maxTotSampleSize = 20,
#'     SampleSizeSteps = 10,
#'     NcntPer = 0.5,
#'     deltaSD = c(0.02, 0.03),
#'     J = 1000,
#'     targetDmCpGs = 10,
#'     tissueType = "Adult (PBMC)",
#'     detectionLimit = 0.01,
#'     DMmethod = "limma",
#'     FDRcritVal = 0.05,
#'     core = 2,
#'     sims = 30)
#' pwrEWAS_powerPlot(data = outSD$powerArray, sd = TRUE)
pwrEWAS_powerPlot <- function(data, sd = FALSE){
    sampleSizes <- as.numeric(dimnames(data)[[2]])
    deltas <- dimnames(data)[[3]]
    x <- NULL
    y <- NULL
    
    df <- data.frame(x = sampleSizes, y = colMeans(matrix(data[,,1])))
    p <- ggplot2::ggplot(df, ggplot2::aes(x = x, y = y)) + 
        ggplot2::ggtitle("Mean power curve with 95-percentile interval (2.5% & 97.5%)") + 
        ggplot2::labs(x = "Sample size") + 
        ggplot2::labs(y = "Power") +
        ggplot2::geom_hline(yintercept = 0.8, linetype = 3, size = 1.5)
    dftemp <- NULL
    
    if(length(deltas) == 1){
        scatter <- 0
    } else if(length(sampleSizes) > 1 & length(deltas) > 1) {
        scatter <- seq(-0.03 * (sampleSizes[2]-sampleSizes[1]), 
            0.03 * (sampleSizes[2]-sampleSizes[1]), 
            0.06/(length(deltas)-1) * (sampleSizes[2]-sampleSizes[1]))
    } else scatter <- seq(-0.03 , 0.03, 0.06/(length(deltas)-1))
    
    
    
    
    for(j in seq_len(dim(data)[3])){
        U <- NULL
        L <- NULL
        if(methods::is(data[,,j], "matrix")){
            dataSlice <- data[,,j]
        } else  dataSlice <- matrix(data[,,j])
        
        for(i in seq_len(dim(dataSlice)[2])){
            L[i] <- stats::quantile(dataSlice[,i], 0.025, na.rm = TRUE)
            U[i] <- stats::quantile(dataSlice[,i], 0.975, na.rm = TRUE)
        }
        
        dftemp[[j]] <- data.frame(x = sampleSizes, y = colMeans(dataSlice) , 
            scatter = sampleSizes + scatter[j],
            L = L, 
            U = U, 
            deltas = rep(as.character(deltas[j]), length(sampleSizes)))
        
        p <- p +
            ggplot2::geom_errorbar(data = dftemp[[j]], 
                ggplot2::aes(x = scatter, 
                    ymax = U, 
                    ymin = L, 
                    colour=deltas),
                width=ifelse(length(sampleSizes)>1,
                    diff(range(sampleSizes))/(length(sampleSizes)*4),0.9), 
                linetype=1, size=1) +
            ggplot2::geom_line(data = dftemp[[j]], 
                ggplot2::aes(x = x, y = y, colour=deltas), size=1.2) +
            ggplot2::geom_point(data = dftemp[[j]], 
                ggplot2::aes(x = x, y = y), size = 1) +
            ggplot2::scale_x_continuous(breaks = sampleSizes) +
            ggplot2::scale_y_continuous(minor_breaks = seq(0 , 1, 0.1), 
                breaks = seq(0, 1, 0.2), limits = c(0,1)) +
            ggplot2::theme(axis.text=ggplot2::element_text(size=18),
                axis.title=ggplot2::element_text(size=22)) +
            ggplot2::theme(axis.title.x = ggplot2::element_text(margin = 
                    ggplot2::margin(t = 20, r = 0, b = 0, l = 0)))+ 
            ggplot2::theme(axis.title.y = ggplot2::element_text(margin = 
                    ggplot2::margin(t = 0, r = 20, b = 0, l = 0)))+ 
            ggplot2::theme(legend.text=ggplot2::element_text(size=17),
                legend.title=ggplot2::element_text(size=20))
        
        if(sd){
            p <- p + ggplot2::scale_colour_discrete(name  = 
                    expression(paste("sd(",Delta[beta],")",sep = "")))
        } else p <- p + ggplot2::scale_colour_discrete(name  = 
                expression(Delta[beta]))
        
        
    }
    print(p)
}


gg_color_hue <- function(n) {
    hues <- seq(15, 375, length = n + 1)
    grDevices::hcl(h = hues, l = 65, c = 100)[seq_len(n)]
}

#' @title Density plot for simulated differences in mean methylation
#'
#' @description pwrEWAS_deltaDensity create a density plot of the simulated differences in mean methylation for different effect sizes
#' 
#' @param data "deltaArray" attribute within the pwrEWAS object create by pwrEWAS
#' @param detectionLimit Detection limit specified in pwrEWAS.
#' @param sd FALSE if targetDelta was specified in pwrEWAS, and TRUE if deltaSD was specified in pwrEWAS.
#'
#' @return pwrEWAS_deltaDensity return a figure displaying densities of simulated differences in mean methylation different effect sizes
#'
#' @export
#' 
#' @examples
#' outDelta <- pwrEWAS(minTotSampleSize = 10,
#'     maxTotSampleSize = 20,
#'     SampleSizeSteps = 10,
#'     NcntPer = 0.5,
#'     targetDelta = c(0.2, 0.5),
#'     J = 1000,
#'     targetDmCpGs = 10,
#'     tissueType = "Adult (PBMC)",
#'     detectionLimit = 0.01,
#'     DMmethod = "limma",
#'     FDRcritVal = 0.05,
#'     core = 2,
#'     sims = 30)
#' pwrEWAS_deltaDensity(data = outDelta$deltaArray, detectionLimit = 0.01, sd = FALSE)
#' outSD <- pwrEWAS(minTotSampleSize = 10,
#'     maxTotSampleSize = 20,
#'     SampleSizeSteps = 10,
#'     NcntPer = 0.5,
#'     deltaSD = c(0.02, 0.03),
#'     J = 1000,
#'     targetDmCpGs = 10,
#'     tissueType = "Adult (PBMC)",
#'     detectionLimit = 0.01,
#'     DMmethod = "limma",
#'     FDRcritVal = 0.05,
#'     core = 2,
#'     sims = 30)
#' pwrEWAS_deltaDensity(data = outSD$deltaArray, detectionLimit = 0.01, sd = TRUE)
pwrEWAS_deltaDensity <- function(data, detectionLimit = 0.01, sd = FALSE){
    maxDensY <- 0
    maxDensX <- 0
    for(d in seq_len(length(data))){
        dens <- stats::density(data[[d]][abs(data[[d]])>detectionLimit]) 
        maxDensY <- max(c(maxDensY, max(dens$y)))
        maxDensX <- max(c(maxDensX, max(abs(dens$x))))
    }
    plot(stats::density(data[[1]]), col = "white", ylim = c(0,maxDensY), 
        xlim = c(max(-1,-1.1*maxDensX), min(1,1.1*maxDensX)),
        main = "", xlab = expression(Delta[beta]), cex.axis = 1.5, cex.lab = 1.5)
    myLineWd <- 2.5
    for(d in seq_len(length(data))){
        graphics::lines(stats::density(data[[d]][abs(data[[d]])>detectionLimit], 
            from = detectionLimit), 
            col = gg_color_hue(length(data))[d], lwd = myLineWd)
        graphics::lines(stats::density(data[[d]][abs(data[[d]])>detectionLimit], 
            to = -detectionLimit), 
            col = gg_color_hue(length(data))[d], lwd = myLineWd)
    }
    graphics::abline(v = c(-detectionLimit, detectionLimit), lty = 3)
    if(sd){
        graphics::legend("topright", names(data), 
            col = gg_color_hue(length(data)), lty = 1, lwd = myLineWd, 
            title = expression(paste("sd(",Delta[beta],")",sep = "")), 
            cex = 1.5)
    } else graphics::legend("topright", names(data), 
        col = gg_color_hue(length(data)), lty = 1, lwd = myLineWd, 
        title = expression(Delta[beta]), cex = 1.5)
    
}