R/Plot_pca_loading.R

In 13479776/statTarget: Statistical Analysis of Molecular Profiles

Plot_pca_loading <- function(pcx, pcy, scaling) {
    loadi = paste(getwd(), "/PCA_Data_", scaling, "/PCA_LoadingsMatrix.csv", sep = "")
    Loading <- read.csv(loadi, sep = ",", header = TRUE)
    Loading.x <- Loading[, 2:ncol(Loading)]
    rownames(Loading.x) <- Loading[, 1]
    ppppp = paste(getwd(), "/PCA_Data_", scaling, "/PCA_P", sep = "")
    Pvar <- read.csv(ppppp, sep = ",", header = TRUE)
    Pvar.x <- Pvar[, 2:ncol(Pvar)]
    rownames(Pvar.x) <- Pvar[, 1]
    cum = Pvar[pcx, 2] + Pvar[pcy, 2]
    pca <- paste("Loadings PC", pcx, " (", Pvar[pcx, 2], ") %")
    pcb <- paste("Loadings PC", pcy, " (", Pvar[pcy, 2], ")%")
    lim.load = c()
    Max.pc1 = 1.1 * (max(Loading.x[, pcx]))
    Min.pc1 = 1.1 * (min(Loading.x[, pcx]))
    Mpc1 = c(Min.pc1 * 2, Max.pc1 * 2)
    Max.pc2 = 1.1 * (max(Loading.x[, pcy]))
    Min.pc2 = 1.1 * (min(Loading.x[, pcy]))
    Mpc2 = c(Min.pc2 * 2, Max.pc2 * 2)
    # dev.new()
    E = paste(getwd(), "/PCA_Data_", scaling, "/LoadingPlot_PC", pcx, "vsPC", pcy, ".pdf", sep = "")
    pdf(file = E)
    graphics::plot(Loading.x[, pcx], Loading.x[, pcy], xlab = pca, ylab = pcb, xlim = c(Min.pc1, Max.pc1), 
        ylim = c(Min.pc2, Max.pc2), main = paste("PCA Loading Plot (", scaling, ")", sep = ""), sub = paste("Cumulative Proportion of Variance Explained = ", 
            cum, "%", sep = ""))
    axis(1, at = Mpc1, pos = c(0, 0), labels = FALSE, col = "grey", lwd = 0.7)
    axis(2, at = Mpc2, pos = c(0, 0), labels = FALSE, col = "grey", lwd = 0.7)
    text(Loading.x[, pcx], Loading.x[, pcy], labels = rownames(Loading.x), cex = 0.6, pos = 1)
    dev.off()
}