normMedFil: Normalization by median filter method
In sights: Statistics and dIagnostic Graphs for HTS

Description Usage Arguments Details Value Note References See Also Examples

Apply median filter normalization to data

1 2	normMedFil(dataMatrix, plateRows, plateCols, dataRows = NULL, dataCols = NULL, seqFilter = TRUE)

`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.
`seqFilter`	Optional logical. If TRUE apply initial row median filter then standard filter, else just apply standard filter.

Median Filter normalization uses a two-step median filter process where each well is adjusted by the median score of a neighbouring group of wells [Bushway et al (2011)]. The first median filter uses a neighbour set based on the Manhattan distance to each well. The second median filter uses a neighbour set based on the proximity along each row or column.

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)

Bushway et al.(2011). Optimization and application of median filter corrections to relieve diverse spatial patterns in microtiter plate data. Journal of Biomolecular Screening, 16(9), 1068-1080.

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

## load dataset
data(ex_dataMatrix)

## apply standard median filter method
ex_normMatrix <- normMedFil(dataMatrix = ex_dataMatrix, dataCols = 5:10,
plateRows = 8, plateCols = 10, seqFilter = FALSE)
## apply initial row median filter then standard filter
ex_normMatrix <- normMedFil(dataMatrix = ex_dataMatrix, dataCols = 5:10,
plateRows = 8, plateCols = 10, seqFilter = TRUE)