TwoGroup: Comparing two biological conditions in a metabolomics data...
In metabolomics: Analysis of Metabolomics Data

Description Usage Arguments Value Author(s) References Examples

This function computes fold changes, t statistics, p-values, and adjusted p-values for each metabolite given a series of replicates and two biological conditions.

1 2	TwoGroup(inputdata, alternative = "two.sided", paired = FALSE, padjmethod = "BH", saveoutput = FALSE, outputname = "results", ...)

`inputdata`	A log transformed data frame in the input data format. See `metabolomics` for details.
`alternative`	A character string specifying the alternative hypothesis for the t tests. This should be one of "`two.sided`", "`greater`" or "`less`". The default is set to "`two.sided`".
`paired`	A logical indicating whether the t-test should be paired or not. The default is set to "`FALSE`".
`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`".
`saveoutput`	A logical indicating whether the output should be saved. If TRUE the output will be saved as a .csv file.
`outputname`	The name of the output file if the output has to be saved.
`...`	Further arguments to be passed to or from methods.

The result is an object of class "results".

output

A matrix with t statistics, p-values, adjusted p-values, fold changes, and standard errors.

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 
    TwoGroup(treated.log, paired = TRUE,
        saveoutput = TRUE, outputname = "results")