ostocsy: OSC Statistical TOtal Correlation SpectroscopY

In muma: Metabolomics Univariate and Multivariate Analysis

Description

Performs the stocsy analysis on the matrix deflated through the 'oplsda' function

Usage

ostocsy(threshold = TRUE, pos.threshold, neg.threshold)

Arguments

threshold	logical, indicating whether positive and negative threshold must be specified. By default is 'TRUE'.
pos.threshold	The positive threshold for plotting positive correlations.
neg.threshold	The negative threshold for plotting negative correlations.

Details

OSTOCSY correlation matrix is graphically visualized, with correlations ranging from -1 to 1 and color coded for negative correlations (blue), positive correlations (red) and no correlation (white). If 'threshold' is 'TRUE' an additional plot is generated with correlation values >= and =< to the specified positive and negative thresholds, respectively. All plots visualized are written in the directory 'OSTOCSY', together with the deflated (OSC-filtered) matrix.

Author(s)

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

References

Blaise, B.J. et al. Orthogonal filtered recoupled-STOCSY to extract metabolic networkd associated with minor perturbations from NMR spectroscopy. (2011) J Proteome Res. 10(9):4342-8.

Examples

## The function is currently defined as
function (threshold = TRUE, pos.threshold, neg.threshold) 
{
    pwd.n = paste(getwd(), "/OPLS-DA/X_deflated.csv", sep = "")
    x <- read.csv(pwd.n, sep = ",", header = TRUE)
    x.x <- x[, 2:ncol(x)]
    rownames(x.x) <- x[, 1]
    x.t <- x.x
    mycor = cor(x.t, method = c("pearson"))
    library(gplots)
    col = colorpanel(50, "blue", "white", "red")
    image(mycor, axes = FALSE, col = col, main = "OSTOCSY")
    axis(side = 1, labels = colnames(mycor), at = seq(0, 1, length = length(colnames(mycor))), 
        las = 2, cex.axis = 0.4)
    axis(side = 2, labels = colnames(mycor), at = seq(0, 1, length = length(colnames(mycor))), 
        las = 2, cex.axis = 0.4)
    dirout = paste(getwd(), "/OPLS-DA/OSTOCSY/", sep = "")
    dir.create(dirout)
    o = paste(dirout, "OSTOCSY.pdf", sep = "")
    dev.copy2pdf(file = o)
    o.cor = paste(dirout, "CorrelationMatrix.csv", sep = "")
    write.csv(mycor, file = o.cor)
    if (threshold) {
        dev.new()
        image(mycor, axes = FALSE, zlim = c(pos.threshold, 1), 
            col = "red", main = paste("OSTOCSY <", neg.threshold, 
                " & >", pos.threshold, sep = ""))
        image(mycor, axes = FALSE, zlim = c(-1, neg.threshold), 
            col = "navy", add = TRUE)
        axis(side = 1, labels = colnames(mycor), at = seq(0, 
            1, length = length(colnames(mycor))), las = 2, cex.axis = 0.4)
        axis(side = 2, labels = colnames(mycor), at = seq(0, 
            1, length = length(colnames(mycor))), las = 2, cex.axis = 0.4)
        out = paste(dirout, "OSTOCSY_", pos.threshold, "_", neg.threshold, 
            ".pdf", sep = "")
        dev.copy2pdf(file = out)
    }
  }

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