PCA-plot: Principal Component Analysis plot

Description

A generic function which produces a PCA-plot.

Usage

plotPCA(object, ...)

Arguments

Value

Undefined. The function exists for its side effect, producing a plot.

See Also

• showMethods for displaying a summary of the methods defined for a given generic function.

• selectMethod for getting the definition of a specific method.

• plotPCA in the DESeq2 package for an example method that uses this generic.

• BiocGenerics for a summary of all the generics defined in the BiocGenerics package.

Examples

showMethods("plotPCA")

suppressWarnings(
  if(require("DESeq2"))
    example("plotPCA", package="DESeq2", local=TRUE)
)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(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

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/BiocGenerics/plotPCA.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plotPCA
> ### Title: PCA-plot: Principal Component Analysis plot
> ### Aliases: plotPCA plotPCA,ANY-method
> ### Keywords: methods
> 
> ### ** Examples
> 
> showMethods("plotPCA")
Function: plotPCA (package BiocGenerics)
<No methods>

> 
> suppressWarnings(
+   if(require("DESeq2"))
+     example("plotPCA", package="DESeq2", local=TRUE)
+ )
Loading required package: DESeq2
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: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


pltPCA> # using rlog transformed data:
pltPCA> dds <- makeExampleDESeqDataSet(betaSD=1)

pltPCA> rld <- rlog(dds)

pltPCA> plotPCA(rld)

pltPCA> # also possible to perform custom transformation:
pltPCA> dds <- estimateSizeFactors(dds)

pltPCA> # shifted log of normalized counts
pltPCA> se <- SummarizedExperiment(log2(counts(dds, normalized=TRUE) + 1),
pltPCA+                            colData=colData(dds))

pltPCA> # the call to DESeqTransform() is needed to
pltPCA> # trigger our plotPCA method.
pltPCA> plotPCA( DESeqTransform( se ) )
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>