normR {sights} | R Documentation

## Normalization by R score method

### Description

Apply Robust Regression model separately to each plate

### Usage

    normR(dataMatrix, plateRows, plateCols, dataRows = NULL, dataCols = NULL)

### Arguments

`dataMatrix` | Data frame or numeric matrix. Columns are plates, and rows are plate wells.
`plateRows, plateCols` | Number of rows/columns in plate.
`dataRows, dataCols` | Optional integer vector. Indicate which row/column numbers from the dataMatrix should be normalized. If NULL then all rows/columns from the dataMatrix are used.

### Details

R score normalization uses the robust regression method described by Wu et al (2008). Parameters are estimated through the `rlm` function. Data is pre-normalized by median normalization prior to applying the regression algorithm. R scores are the residuals produced by the model and rescaled by dividing with the standard deviation estimate from the regression function.

### Value

Numeric matrix of normalized data in the same format as dataMatrix

### Note

For information on how to arrange your dataset for dataMatrix, please see (`ex_dataMatrix`)

### References

Wu et al. (2008). Quantitative Assessment of Hit Detection and Confirmation in Single and Duplicate High-Throughput Screenings. Journal of Biomolecular Screening, 13(2), 159-167.

### See Also

Other normalization methods: `normLoess`, `normMedFil`, `normRobZ`, `normSPAWN`, `normZ`

### Examples

    ## load dataset
    data(ex_dataMatrix)

    ## apply R score
    ex_normMatrix <- normR(dataMatrix = ex_dataMatrix, dataCols = 5:10,
    plateRows = 8, plateCols = 10)

[Package sights version 1.18.0 Index]