plsda: Partial Least Square Discriminant Analysis
In muma: Metabolomics Univariate and Multivariate Analysis

Description Usage Arguments Details Author(s) Examples

Perform PLS-DA according to the class subgroups.

1	plsda(scaling)

scaling

a character string indicating the name of the scaling previously specified in the function 'explore.data'

The number of components to be calculated are defined as the number of classes - 1. A plot reporting pairwise representation of the components is graphically visualized and written in the directory 'PLS-DA', together with the PLS score and loading matrices.

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

## The function is currently defined as
function (scaling) 
{
    pwd.x = paste(getwd(), "/Preprocessing_Data_", scaling, "/ProcessedTable.csv", 
        sep = "")
    x = read.csv(pwd.x, header = TRUE)
    x.x = x[, 2:ncol(x)]
    rownames(x.x) = x[, 1]
    pwdK = paste(getwd(), "/Preprocessing_Data_", scaling, "/class.csv", 
        sep = "")
    k = read.csv(pwdK, header = TRUE)
    k.x = matrix(k[, -1], ncol = 1)
    x.n = cbind(k.x, x.x)
    sorted = x.n[order(x.n[, 1]), ]
    g = c()
    for (i in 1:nrow(sorted)) {
        if (any(g == sorted[i, 1])) {
            g = g
        }
        else {
            g = matrix(c(g, sorted[i, 1]), ncol = 1)
        }
    }
    dimB = nrow(g) * nrow(sorted)
    B = matrix(rep(NA, dimB), ncol = nrow(g))
    for (i in 1:nrow(sorted)) {
        for (j in 1:nrow(g)) {
            if (sorted[i, 1] == j) {
                B[i, j] = 1
            }
            else {
                B[i, j] = 0
            }
        }
    }
    library(pls)
    sorted.x = sorted[, -1]
    sorted.un = matrix(unlist(sorted.x), ncol = ncol(sorted.x))
    P = plsr(B ~ sorted.un, ncomp = nrow(g) - 1, method = c("kernelpls"), 
        validation = "CV")
    rownames(P$scores) = rownames(sorted.x)
    rownames(P$loadings) = colnames(sorted.x)
    dirout = paste(getwd(), "/PLS-DA_", scaling, "/", sep = "")
    dir.create(dirout)
    out.score = paste(dirout, "PLSDA_Scores_", scaling, ".csv", 
        sep = "")
    write.csv(P$scores, out.score)
    out.load = paste(dirout, "PLSDA_Loadings_", scaling, ".csv", 
        sep = "")
    write.csv(P$loadings, out.load)
    k = matrix(sorted[, 1], ncol = 1)
    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, ], ])
    }
    if (ncol(P$scores) == 1) {
        xlab = "Samples"
        ylab = "Score values Component 1"
        plot(P$scores[, 1], col = col, pch = 19, xlab = c(xlab), 
            ylab = c(ylab), main = paste("PLS-DA Score Plot (", 
                scaling, ")", sep = ""))
        lim1 = nrow(P$scores) * 2
        axis(1, at = c(-lim1, lim1), col = "grey", pos = c(0, 
            0), labels = FALSE, lwd = 1)
        text(P$scores[, 1], col = col, cex = 0.5, pos = 1, labels = rownames(P$scores))
        pwdout = paste(dirout, "ScorePlot_PLSDA_1Component_", 
            scaling, ".pdf", sep = "")
        dev.copy2pdf(file = pwdout)
        Max.pc2 = 1.1 * (max(P$loadings[, 1]))
        Min.pc2 = 1.1 * (min(P$loadings[, 1]))
        Mpc2 = c(Min.pc2, Max.pc2)
        dev.new()
        plot(P$loadings[, 1], ylim = Mpc2, main = paste("PLS-DA Loading Plot (", 
            scaling, ")", sep = ""), xlab = "Variables", ylab = "W*c values (Component1)")
        text(P$loadings[, 1], cex = 0.7, pos = 1, labels = rownames(P$loadings))
        pwdout1 = paste(dirout, "W*cPlot_PLSDA_1Component_", 
            scaling, ".pdf", sep = "")
        dev.copy2pdf(file = pwdout1)
    }
    else {
        pairs = c()
        if (ncol(P$scores) >= 10) {
            pairs = c(10)
        }
        else {
            pairs = c(ncol(P$scores))
        }
        pairs(P$scores[, 1:pairs], col = col)
        pairs = paste(dirout, "Pairs_PLSDA_", scaling, ".pdf", 
            sep = "")
        dev.copy2pdf(file = pairs)
    }
    p.v = matrix(P$Xvar, ncol = 1)
    p.v.csv = paste(dirout, "PLSDA_P_", scaling, ".csv", sep = "")
    write.csv(p.v, file = p.v.csv)
    p.vtot = matrix(P$Xtotvar, ncol = 1)
    p.vtot.csv = paste(dirout, "PLSDA_Ptot_", scaling, ".csv", 
        sep = "")
    write.csv(p.vtot, file = p.vtot.csv, row.names = FALSE)
  }