MADvsMedianPlot {MmPalateMiRNA} | R Documentation

## Spread vs location of probe intensities

### Description

Plots of the spread (median absolute deviation) versus the location (median) of probe intensity levels.

### Usage

    MADvsMedianPlot(x, ...)

    ## S4 method for signature 'list'
    MADvsMedianPlot(
        x,
        channel=c("G", "R"),
        group=NULL,
        subset=NULL,
        ...)

### Arguments

`x` | A list containing `MAList` and/or `NChannelSet` objects
`channel` | The channel to use for calculating distances, one of either "G" (green or control channel) or "R" (red or experimental channel)
`group` | An optional character string specifying the name of a factor to create separate panel displays, which must be in `x$genes` (for `RGList` objects)
`subset` | An optional character vector specifying the which levels of `group` to use in creating separate panel displays
`...` | arguments to pass to `densityplot`

### Methods

`signature(x = "list")`

  The method for `list` objects is intended to work with lists of normalized data sets, as either `MAList` or `NChannelSet` objects. This method will produce separate panel displays for each normalized data set, additionally color-coded by the `group` argument if supplied.

### References

D. Sarkar, R. Parkin, S. Wyman, A. Bendoraite, C. Sather, J. Delrow, A. K. Godwin, C. Drescher, W. Huber, R. Gentleman, and M. Tewari. Quality assessment and data analysis for microRNA expression arrays. Nucleic Acids Res, 37(2):e17, 2009.

### See Also

`levelplot` for pairwise distance plots between arrays, `densityplot` for density plots of log2 intensity values, and `MAplot` for MA plots.

### Examples

    data(PalateData)
    reducedSet <- filterArray(PalateData, keep=c("MIR", "LET", "POSCON", "CALIB"),
                              frac=1.1, number=3, reps=4)
    ndata.none <- normalizeWithinArrays(reducedSet, method="none")
    ndata.median <- normalizeWithinArrays(reducedSet, method="median")
    ndata.loess <- normalizeWithinArrays(reducedSet, method="loess")
    ndata.quantile <- normalizeBetweenArrays(reducedSet, method="quantile")
    ndata.all <- list(ndata.none, ndata.median, ndata.loess,
                      ndata.quantile)
    res <- MADvsMedianPlot(ndata.all, channel="R", group="probe.type",
                     subset=c("MMU miRNAs", "Other miRNAs", "Control"))
    print(res)

[Package MmPalateMiRNA version 1.30.0 Index]