normMedFil {sights} | R Documentation

## Normalization by median filter method

### Description

Apply median filter normalization to data

### Usage

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

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

### Details

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.

### 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

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.

### See Also

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

### Examples

    ## 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)

[Package sights version 1.6.0 Index]