statFDR: FDR control
In sights: Statistics and dIagnostic Graphs for HTS

Description Usage Arguments Details Value Note References See Also Examples

Apply Storey's FDR control to p-values

1	statFDR(testMatrix, ctrlMethod = "smoother", ...)

`testMatrix`	Data frame or numeric matrix consisting of output from `statT` or `statRVM` functions. P-value columns from this matrix are automatically selected for FDR calculation. Columns are samples, and rows are plate wells.
`ctrlMethod`	Optional. Method to use either 'smoother' or 'bootstrap' to estimate null. Default is 'smoother'.
`...`	Optional. Additional parameters passed to `qvalue` function.

False Discovery Rate procedure is used to control the proportion of false positives in the results. This is an implementation of the positive false discovery (pFDR) procedure of the qvalue function.

A matrix of parameters for each replicate group is returned:

`T-statistic or RVM T-statistic`	Value of the t-statistic.
`Mean_Difference`	Difference between the calculated and the true mean.
`Standard_Error`	Standard error of the difference between means.
`Degrees_Of_Freedom`	Degrees of freedom for the t-statistic.
`P-value`	P-value for the t-test.
`q-value`	FDR q-value for the P-value.

Please install the package 'qvalue' from Bioconductor, if not already installed.

Storey (2002). A direct approach to false discovery rates. Journal of the Royal Statistical Society: Series B, 64, 479-498.

Other statistical methods: statRVM, statT

## load dataset
data(ex_dataMatrix)

## normalize data matrix using any method and store in new variable
ex_normMatrix <- normSights(dataMatrix = ex_dataMatrix, dataCols = 5:10,
normMethod = 'normZ')
## test normalized data matrix using either the RVM or T test and store in new variable
ex_testMatrix <- statT(normMatrix = ex_normMatrix, trueMean = 0,
repIndex = c(1,1,1,2,2,2))
## apply FDR control to test matrix with bootstrap control method
ex_ctrlMatrix <- statFDR(testMatrix = ex_testMatrix,
ctrlMethod = 'bootstrap')