statRVM: RVM Test

In eg-r/sights: Statistics and dIagnostic Graphs for HTS

RVM Test

Description

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

Usage

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

Arguments

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'.

Details

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.

Value

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.

References

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.

See Also

Other statistical methods: statFDR, statT

Examples

## 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))

eg-r/sights documentation built on Jan. 28, 2023, 12:17 a.m.