TwoGroupPlots: Plots of differential metabolites
In metabolomics: Analysis of Metabolomics Data

Description Usage Arguments Value Author(s) References Examples

Produces plots for visualising differential metabolites.

1 2	TwoGroupPlots(datamat, tstats, foldchanges, pvalues, padjmethod = "BH", fcutoff = log(2), pcutoff = 0.05, cexval = 0.7)

`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.

A list containing:

`IncreasedMets`	Names of increased metabolites.
`DecreasedMets`	Names of decreased metabolites.
`DifferentialMets`	Names of all differential metabolites.

Alysha M De Livera, Jairus B Bowne

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.

    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)