R: Methods for Function 'MDPlot' in Package 'EDASeq'
MDPlot-methods
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))
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(EDASeq)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
rbind, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: ShortRead
Loading required package: BiocParallel
Loading required package: Biostrings
Loading required package: S4Vectors
Loading required package: stats4
Attaching package: 'S4Vectors'
The following objects are masked from 'package:base':
colMeans, colSums, expand.grid, rowMeans, rowSums
Loading required package: IRanges
Loading required package: XVector
Loading required package: Rsamtools
Loading required package: GenomeInfoDb
Loading required package: GenomicRanges
Loading required package: GenomicAlignments
Loading required package: SummarizedExperiment
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/EDASeq/MDPlot-methods.Rd_%03d_medium.png", width=480, height=480)
> ### Name: MDPlot-methods
> ### Title: Methods for Function 'MDPlot' in Package 'EDASeq'
> ### Aliases: MDPlot MDPlot-methods MDPlot,matrix,numeric-method
> ### MDPlot,SeqExpressionSet,numeric-method
> ### Keywords: 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))
>
>
>
>
>
>
> dev.off()
null device
1
>