metabolomics (version 0.1.4)

TwoGroupPlots: Plots of differential metabolites

Description

Produces plots for visualising differential metabolites.

Usage

TwoGroupPlots(datamat, tstats, foldchanges, pvalues,  padjmethod = "BH", fcutoff = log(2), pcutoff = 0.05, cexval = 0.7)

Arguments

datamat

A numerical data matrix with samples in rows and metabolites in columns

tstats

A vector of t statistics.

foldchanges

A vector of fold changes.

pvalues

A vector of corresponding p-values.

padjmethod

A character string specifying p-value adjustment method for multiple comparisons. Must be one of "bonferroni", "holm" (Holm 1979), "hochberg" (Hochberg 1988), "hommel" (Hommel 1988), "BH" (Benjamini and Hochberg 1995), "BY" (Benjamini and Yekutieli 2001), or "none". The default method is set to "BH".

fcutoff

A numeric indicating the fold change cut off. The default is set to 2.

pcutoff

A numeric indicating the adjusted p-value cut off. The default is set to 0.05.

cexval

The font size of the text labels.

Value

IncreasedMets

Names of increased metabolites.

DecreasedMets

Names of decreased metabolites.

DifferentialMets

Names of all differential metabolites.

References

Benjamini, Y., Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological) 57(1): 289-300. Benjamini, Y., Yekutieli, D. (2001) The Control of the False Discovery Rate in Multiple Testing under Dependency. The Annals of Statistics 29(4): 1165-1188. Hochberg, Y. (1988) A sharper Bonferroni procedure for multiple tests of significance. Biometrika 75(4): 800-802. Holm, S. (1979) A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6(2): 65-70. Hommel, G. (1988) A stagewise rejective multiple test procedure based on a modified Bonferroni test. Biometrika 75(2): 383-386.

Examples

    data(treated)
    treated.log <- LogTransform(treated)$output
    results <- TwoGroup(treated.log, paired = TRUE)$output
    TwoGroupPlots(treated.log[,-1], tstats = results[, 1], 
        foldchanges = results[, 4], pvalues = results[, 2], padjmethod = "BH",
        fcutoff = log(2), pcutoff = 0.05)