1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
|
\name{MDPlot-methods}
\docType{methods}
\alias{MDPlot}
\alias{MDPlot-methods}
\alias{MDPlot,matrix,numeric-method}
\alias{MDPlot,SeqExpressionSet,numeric-method}
\title{ Methods for Function \code{MDPlot} in Package \pkg{EDASeq} }
\description{
\code{MDPlot} produces a mean-difference smooth scatterplot of two lanes in an experiment.
}
\usage{
MDPlot(x,y,...)
}
\arguments{
\item{x}{Either a numeric matrix or a \code{\linkS4class{SeqExpressionSet}} object containing the gene expression.
}
\item{y}{A numeric vecor specifying the lanes to be compared.}
\item{...}{See \code{\link{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.
}
\section{Methods}{
\describe{
\item{\code{signature(x = "matrix", y = "numeric")}}{
}
\item{\code{signature(x = "SeqExpressionSet", y = "numeric")}}{
}
}}
\keyword{methods}
\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))
}
|
