Plot attribute density and boxplot for each bias source quartile.

Description

biasExploration plots density and box-plot of the analyzed attribute for eaach bias source' quartiles. It helps the identification of some bias due to high source values, for example, high gc content. This graphics could plot together using the ggplot2 geom_violin method.

Usage

biasExploration(object, source = c("length", "gc", "pool"), dens = FALSE)

## S4 method for signature 'TargetExperiment'
biasExploration(object, source = c("length",
    "gc", "pool"), dens = FALSE)

Arguments

Value

ggplot2 graphics.

Note

see full example in TargetExperiment-class

Author(s)

Gabriela A. Merino gmerino@bdmg.com.ar, Cristobal Fresno cfresno@bdmg.com.ar and Elmer A. Fernandez efernandez@bdmg.com.ar

See Also

plot, plotFeatPerform

Examples

## Loading the TargetExperiment object
data(ampliPanel, package="TarSeqQC")

# Attribute boxplot and density plot exploration
g<-biasExploration(ampliPanel,source="gc", dens=TRUE)
# x11(type="cairo")
if(interactive()){
g
}

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(TarSeqQC)
Loading required package: GenomicRanges
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

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: GenomeInfoDb
Loading required package: Rsamtools
Loading required package: Biostrings
Loading required package: XVector
Loading required package: ggplot2
Loading required package: plyr

Attaching package: 'plyr'

The following object is masked from 'package:XVector':

    compact

The following object is masked from 'package:IRanges':

    desc

The following object is masked from 'package:S4Vectors':

    rename

Loading required package: openxlsx
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/TarSeqQC/TargetExperiment-biasExploration.Rd_%03d_medium.png", width=480, height=480)
> ### Name: biasExploration
> ### Title: Plot attribute density and boxplot for each bias source
> ###   quartile.
> ### Aliases: biasExploration biasExploration,TargetExperiment-method
> ###   biasExploration-methods
> 
> ### ** Examples
> 
> ## Loading the TargetExperiment object
> data(ampliPanel, package="TarSeqQC")
> 
> # Attribute boxplot and density plot exploration
> g<-biasExploration(ampliPanel,source="gc", dens=TRUE)
> # x11(type="cairo")
> #if(interactive()){
> g
> #}
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>