normR: Normalization by R score method
In sights: Statistics and dIagnostic Graphs for HTS

Description Usage Arguments Details Value Note References See Also Examples

Apply Robust Regression model separately to each plate

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

`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.

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.

Numeric matrix of normalized data in the same format as dataMatrix

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

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.

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

## load dataset
data(ex_dataMatrix)

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