statRVM {sights} | R Documentation

## 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))

[Package sights version 1.12.0 Index]