statRVM: RVM Test
In sights: Statistics and dIagnostic Graphs for HTS

Description Usage Arguments Details Value References See Also Examples

Apply one-sample RVM t-test separately to each plate

1 2	statRVM(normMatrix, repIndex, normRows = NULL, normCols = NULL, testSide = "two.sided")

`normMatrix`	Data frame or numeric matrix of normalized data. Columns are plates, and rows are plate wells.
`repIndex`	Integer vector indicating replicates in normMatrix. Which plates are replicates of each other? Provide the same number for plates belonging to a replicate group. Each index in the vector matches the corresponding column of normMatrix.
`normRows, normCols`	Optional integer vector. Indicate which row/column numbers from the normMatrix should be tested. If NULL then all rows/columns from the normMatrix are used.
`testSide`	Optional. Type of t-test: 'two.sided', 'less', or 'greater'. Default is 'two.sided'.

Random Variance Model one-sample t-test is applied to the normalized data. RVM assumes that the across replicate variances are distributed according to an inverse gamma distribution. This can be checked by using the plotIGFit function.

A matrix of parameters for each replicate group is returned:

`RVM T-statistic`	Value of the RVM 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 RVM test.

Malo et al. (2006). Statistical practice in high-throughput screening data analysis. Nature Biotechnology, 24(2), 167-175.

Wright & Simon (2003). A random variance model for detection of differential gene expression in small microarray experiments. Bioinformatics, 19(18), 2448-2455.

Other statistical methods: statFDR, 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')
## apply RVM test to normalized data matrix and get the p-values
ex_testMatrix <- statRVM(normMatrix = ex_normMatrix, repIndex = c(1,1,1,2,2,2))