MDPlot-methods {EDASeq} | R Documentation

## Methods for Function `MDPlot` in Package EDASeq

### Description

`MDPlot` produces a mean-difference smooth scatterplot of two lanes in an experiment.

### Usage

    MDPlot(x,y,...)

### Arguments

`x` | Either a numeric matrix or a `SeqExpressionSet` object containing the gene expression.
`y` | A numeric vecor specifying the lanes to be compared.
`...` | See `par`

### Details

The mean-difference (MD) plot is a useful plot to visualize difference in two lanes of an experiment. From a MDPlot one can see if normalization is needed and if a linear scaling is sufficient or nonlinear normalization is more effective.

The MDPlot also plots a lowess fit (in red) underlying a possible trend in the bias related to the mean expression.

### Methods

`signature(x = "matrix", y = "numeric")`
`signature(x = "SeqExpressionSet", y = "numeric")`

### Examples

    library(yeastRNASeq)
    data(geneLevelData)
    data(yeastGC)

    sub <- intersect(rownames(geneLevelData), names(yeastGC))

    mat <- as.matrix(geneLevelData[sub,])

    data <- newSeqExpressionSet(mat,
                phenoData=AnnotatedDataFrame(
                          data.frame(conditions=factor(c("mut", "mut", "wt", "wt")),
                                     row.names=colnames(geneLevelData))),
                featureData=AnnotatedDataFrame(data.frame(gc=yeastGC[sub])))

    MDPlot(data,c(1,3))

[Package EDASeq version 2.18.0 Index]