Plot.pca.score: Plot PCA Score Plot

In muma: Metabolomics Univariate and Multivariate Analysis

Description

This function is implemented in the unique function 'plot.pca'. It generated and visualizes PCA score plot color-coded according with the class definition in the second column of the file. This function cannot be used without having previously used the function 'explore.data'.

Usage

Plot.pca.score(pcx, pcy, scaling)

Arguments

pcx	an integer indicating the principal component to be plotted in x.
pcy	an integer indicating the principal component to be plotted in y
scaling	a character string indicating the name of the scaling previously specified in the function 'explore.data'

Details

For details see ?plot.pca.

Author(s)

Edoardo Gaude, Dimitrios Spiliotopoulos, Francesca Chignola, Silvia Mari, Andrea Spitaleri and Michela Ghitti

Examples

## The function is currently defined as
function (pcx, pcy, scaling) 
{
    score = paste(getwd(), "/PCA_Data_", scaling, "/PCA_ScoreMatrix.csv", 
        sep = "")
    ppppp = paste(getwd(), "/PCA_Data_", scaling, "/PCA_P", sep = "")
    Score <- read.csv(score, sep = ",", header = TRUE)
    Score.x <- Score[, 2:ncol(Score)]
    rownames(Score.x) <- Score[, 1]
    pwdK = paste(getwd(), "/Preprocessing_Data_", scaling, "/class.csv", 
        sep = "")
    k = read.csv(pwdK)
    Pvar <- read.csv(ppppp, sep = ",", header = TRUE)
    Pvar.x <- Pvar[, 2:ncol(Pvar)]
    rownames(Pvar.x) <- Pvar[, 1]
    pca <- paste("PC", pcx, " (", Pvar[pcx, 2], ") %")
    pcb <- paste("PC", pcy, " (", Pvar[pcy, 2], ")%")
    cum = Pvar[pcx, 2] + Pvar[pcy, 2]
    xlab = c(pca)
    ylab = c(pcb)
    lim = c()
    max.pc1 = 1.3 * (max(abs(Score.x[, pcx])))
    max.pc2 = 1.3 * (max(abs(Score.x[, pcy])))
    if (max.pc1 > max.pc2) {
        lim = c(-max.pc1, max.pc1)
    }
    else {
        lim = c(-max.pc2, max.pc2)
    }
    tutticolors = matrix(c(1, 2, 3, 4, 5, 6, 7, 8, "rosybrown4", 
        "green4", "navy", "purple2", "orange", "pink", "chocolate2", 
        "coral3", "khaki3", "thistle", "turquoise3", "palegreen1", 
        "moccasin", "olivedrab3", "azure4", "gold3", "deeppink"), 
        ncol = 1)
    col = c()
    for (i in 1:nrow(k)) {
        col = c(col, tutticolors[k[i, 2], ])
    }
    dev.new()
    plot(Score.x[, pcx], Score.x[, pcy], col = col, xlab = xlab, 
        ylab = ylab, xlim = lim, ylim = lim, pch = 19, sub = paste("Cumulative Proportion of Variance Explained = ", 
            cum, "%", sep = ""), main = paste("PCA Score Plot (", 
            scaling, ")", sep = ""))
    axis(1, at = lim * 2, pos = c(0, 0), labels = FALSE, col = "grey", 
        lwd = 0.7)
    axis(2, at = lim * 2, pos = c(0, 0), labels = FALSE, col = "grey", 
        lwd = 0.7)
    library(car)
    dataEllipse(Score.x[, pcx], Score.x[, pcy], levels = c(0.95), 
        add = TRUE, col = "black", lwd = 0.4, plot.points = FALSE, 
        center.cex = 0.2)
    text(Score.x[, pcx], Score.x[, pcy], col = col, cex = 0.5, 
        labels = rownames(Score.x), pos = 1)
    D <- paste(getwd(), "/PCA_Data_", scaling, "/ScorePlot_PC", 
        pcx, "vsPC", pcy, ".pdf", sep = "")
    dev.copy2pdf(file = D)
  }

muma documentation built on May 2, 2019, 9:45 a.m.