MDPlot-methods: Methods for Function 'MDPlot' in Package 'EDASeq'

In EDASeq: Exploratory Data Analysis and Normalization for RNA-Seq

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))

EDASeq documentation built on Nov. 8, 2020, 8:29 p.m.