Methods for Function biasPlot in Package EDASeq

Description

biasPlot produces a plot of the lowess regression of the counts on a covariate of interest, tipically the GC-content or the length of the genes.

Methods

signature(x = "matrix", y = "numeric")

It plots a line representing the regression of every column of the matrix x on the numeric covariate y. One can pass the usual graphical parameters as additional arguments (see par).

signature(x = "SeqExpressionSet", y = "character")

It plots a line representing the regression of every lane in x on the covariate specified by y. y must be one of the column of the featureData slot of the x object. One can pass the usual graphical parameters as additional arguments (see par). The parameter color_code (optional) must be a number specifying the column of phenoData to be used for color-coding. By default it is color-coded according to the first column of phenoData. If legend=TRUE and col is not specified a legend with the information stored in phenoData is added.

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])))

biasPlot(data,"gc",ylim=c(0,5),log=TRUE)

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/biasPlot-methods.Rd_%03d_medium.png", width=480, height=480)
> ### Name: biasPlot-methods
> ### Title: Methods for Function 'biasPlot' in Package 'EDASeq'
> ### Aliases: biasPlot biasPlot-methods biasPlot,matrix,numeric-method
> ###   biasPlot,SeqExpressionSet,character-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])))
> 
> biasPlot(data,"gc",ylim=c(0,5),log=TRUE)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>