Methods for Function plotPCA in Package EDASeq

Description

plotPCA produces a Principal Component Analysis (PCA) plot of the counts in object

Usage

## S4 method for signature 'matrix'
plotPCA(object, k=2, labels=TRUE, isLog=FALSE, ...)
## S4 method for signature 'SeqExpressionSet'
plotPCA(object, k=2, labels=TRUE, ...)

Arguments

Details

The Principal Component Analysis (PCA) plot is a useful diagnostic plot to highlight differences in the distribution of replicate samples, by projecting the samples into a lower dimensional space.

If there is strong differential expression between two classes, one expects the samples to cluster by class in the first few Principal Components (PCs) (usually 2 or 3 components are enough). This plot also highlights possible batch effects and/or outlying samples.

Methods

signature(x = "matrix")

signature(x = "SeqExpressionSet")

Examples

library(yeastRNASeq)
data(geneLevelData)

mat <- as.matrix(geneLevelData)

data <- newSeqExpressionSet(mat,
                            phenoData=AnnotatedDataFrame(
                                      data.frame(conditions=factor(c("mut", "mut", "wt", "wt")),
                                                 row.names=colnames(geneLevelData))))

plotPCA(data, col=rep(1:2, each=2))

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/plotPCA-methods.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plotPCA-methods
> ### Title: Methods for Function 'plotPCA' in Package 'EDASeq'
> ### Aliases: plotPCA plotPCA-methods plotPCA,matrix-method
> ###   plotPCA,SeqExpressionSet-method
> ### Keywords: methods
> 
> ### ** Examples
> 
> library(yeastRNASeq)
> data(geneLevelData)
> 
> mat <- as.matrix(geneLevelData)
> 
> data <- newSeqExpressionSet(mat,
+                             phenoData=AnnotatedDataFrame(
+                                       data.frame(conditions=factor(c("mut", "mut", "wt", "wt")),
+                                                  row.names=colnames(geneLevelData))))
> 
> plotPCA(data, col=rep(1:2, each=2))
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>