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Last data update: 2014.03.03

R: Method plot for objects defined in this package

plot	R Documentation

Method plot for objects defined in this package

Description

Generic function plot to display scatter plots or other types of graphical representation for objects defined in this package.

Usage

## S3 method for class 'maigesRaw'
plot(x, bkgSub="subtract", z=NULL, legend.func=NULL,
    ylab="W", ...)

## S3 method for class 'maiges'
plot(x, z=NULL, legend.func=NULL, ylab="W", ...)

## S3 method for class 'maigesANOVA'
plot(x, z=NULL, legend.func=NULL, ylab="W", ...)

## S3 method for class 'maigesDE'
plot(x, adjP="none", idx=1, ...)

## S3 method for class 'maigesDEcluster'
plot(x, adjP="none", idx=1, ...)

## S3 method for class 'maigesClass'
plot(x, idx=1, ...)

## S3 method for class 'maigesRelNetB'
plot(x=NULL, cutPval=0.05, cutCor=NULL,
name=NULL, ...)

## S3 method for class 'maigesRelNetM'
plot(x=NULL, cutPval=0.05, names=NULL, ...)

## S3 method for class 'maigesActMod'
plot(x, type=c("S", "C")[2], keepEmpty=FALSE, ...)

## S3 method for class 'maigesActNet'
plot(x, type=c("score", "p-value")[1], ...)

Arguments

`x`	an object of any class defined in this package, except `maigesPreRaw`.
`bkgSub`	string specifying the method for background subtraction. See function `backgroundcorrect` to find the available options.
`z`	accessor method for stratifying data, see `maPlot`.
`legend.func`	string specifying options to show legend in the figure.
`ylab`	character string specifying the label to y axis.
`adjP`	type of p-value adjustment, see function `mt.rawp2adjp` in package multtest.
`idx`	index of the test statistic to be plotted in case of objects of classes `maigesDE` and `maigesDEcluster` or the index of the clique to be plotted in case of object with class `maigesClass`.
`cutPval`	real number in [0,1] specifying a cutoff p-value to show significant results from relevance network analysis. For class `maigesRelNetB`, if this parameter is specified the argument `cutCor` isn't used.
`cutCor`	real number in [0,1], specifying a coefficient correlation value cutoff (in absolute value) to show only absolute correlation values greater than this value. Pay attention, to use this cutoff it is necessary to specify `cutPval` as NULL.
`name`	character string giving a name for sample type tested to be plotted as a name in the method for class `maigesRelNetB`.
`names`	similar to the previous one, but it is a vector of length 3.
`type`	string specifying the type of colour map to be plotted. For class `maigesActMod` it must be 'S' or 'C' for samples or biological conditions, respectively. For class `maigesActNet` it must be 'score' or 'p-value' for the statistics or p-values of the tests, respectively.
`keepEmpty`	logical, if true the results of all gene groups are displayed, else only the gene groups that present at least one significant result are displayed.
`...`	additional arguments for method `maPlot` or `plot`

Details

This method uses the function maPlot to display scatter plots ratio vs mean values for objects of class maiges, maigesRaw or maigesANOVA. For objects of class maigesDE or maigesDEcluster, this method display volcano plots. For objects of class maigesClass it do 2 or 3 dimensions scatter plots that facilitate the visualisation of good classifying cliques of genes For objects of class maigesRelNetM the method displays 3 circular graphs representing the correlation values for the two groups tested and the p-values of the tests. For class maigesRelNetB it displays only one circular graph showing the correlation values for the type tested. In objects of class maigesActMod and maigesActNet the method do the same job as image.

Pay attention that, even using the method maPlot from marray package, we plot W values against A values instead of MA plots.

Author(s)

Gustavo H. Esteves <gesteves@vision.ime.usp.br>

Examples

## Loading the dataset
data(gastro)

## Example with an object of class maigesRaw, without and with backgound
## subtraction, also we present a plot with normexp (from limma package)
## subtract algorithm.
plot(gastro.raw[,1], bkgSub="none")
plot(gastro.raw[,1], bkgSub="subtract")
plot(gastro.raw[,1], bkgSub="normexp")

## Example with an object of class maigesNorm.
plot(gastro.norm[,1])



## Example for objects of class maigesDE.

## Doing bootstrap from t statistic test fot 'Type' sample label, k=1000
## specifies one thousand bootstraps
gastro.ttest = deGenes2by2Ttest(gastro.summ, sLabelID="Type")

plot(gastro.ttest) ## Volcano plot


## Example for object of class maigesClass.

## Doing LDA classifier with 3 genes for the 6th gene group comparing
## the 2 categories from 'Type' sample label.
gastro.class = classifyLDA(gastro.summ, sLabelID="Type",
  gNameID="GeneName", nGenes=3, geneGrp=6)

plot(gastro.class) ## plot the 1st classifier
plot(gastro.class, idx=7) ## plot the 7th classifier


## Example for object of class maigesActNet

## Doing functional classification of gene groups for 'Tissue' sample label
gastro.mod = activeMod(gastro.summ, sLabelID="Tissue", cutExp=1,
  cutPhiper=0.05)

plot(gastro.mod, "S", margins=c(15,3)) ## Plot for individual samples
plot(gastro.mod, "C", margins=c(21,5)) ## Plot for unique biological conditions



## Example for object of class maigesRelNetB

## Constructing the relevance network (Butte's method) for sample
## 'Tissue' equal to 'Neso' for the 1st gene group
gastro.net = relNetworkB(gastro.summ, sLabelID="Tissue", 
  samples="Neso", geneGrp=1, type="Rpearson")

plot(gastro.net, cutPval=0.05)




## Example for object of class maigesRelNetM

## Constructing the relevance network for sample
## 'Tissue' comparing 'Neso' and 'Aeso' for the 1st gene group
gastro.net = relNetworkM(gastro.summ, sLabelID="Tissue", 
  samples = list(Neso="Neso", Aeso="Aeso"), geneGrp=11,
  type="Rpearson")

plot(gastro.net, cutPval=0.05)
plot(gastro.net, cutPval=0.01)



## Example for objects of class maigesActNet

## Doing functional classification of gene networks for sample Label
## given by 'Tissue'
gastro.net = activeNet(gastro.summ, sLabelID="Tissue")

plot(gastro.net, type="score", margins=c(21,5))
plot(gastro.net, type="p-value", margins=c(21,5))

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(maigesPack)
Loading required package: convert
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: limma

Attaching package: 'limma'

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

    plotMA

Loading required package: marray
Loading required package: graph
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/maigesPack/plot-methods.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plot
> ### Title: Method plot for objects defined in this package
> ### Aliases: plot.maigesRaw plot.maiges plot.maigesANOVA plot.maigesDE
> ###   plot.maigesDEcluster plot.maigesClass plot.maigesRelNetB
> ###   plot.maigesRelNetM plot.maigesActMod plot.maigesActNet plot
> ### Keywords: array
> 
> ### ** Examples
> 
> ## Loading the dataset
> data(gastro)
> 
> ## Example with an object of class maigesRaw, without and with backgound
> ## subtraction, also we present a plot with normexp (from limma package)
> ## subtract algorithm.
> plot(gastro.raw[,1], bkgSub="none")
> plot(gastro.raw[,1], bkgSub="subtract")
> plot(gastro.raw[,1], bkgSub="normexp")
Array 1 corrected
Array 1 corrected
> 
> ## Example with an object of class maigesNorm.
> plot(gastro.norm[,1])
> 
> 
> 
> ## Example for objects of class maigesDE.
> 
> ## Doing bootstrap from t statistic test fot 'Type' sample label, k=1000
> ## specifies one thousand bootstraps
> gastro.ttest = deGenes2by2Ttest(gastro.summ, sLabelID="Type")
> 
> plot(gastro.ttest) ## Volcano plot
> 
> 
> ## Example for object of class maigesClass.
> 
> ## Doing LDA classifier with 3 genes for the 6th gene group comparing
> ## the 2 categories from 'Type' sample label.
> gastro.class = classifyLDA(gastro.summ, sLabelID="Type",
+   gNameID="GeneName", nGenes=3, geneGrp=6)
> 
> plot(gastro.class) ## plot the 1st classifier
Loading required package: rgl
> plot(gastro.class, idx=7) ## plot the 7th classifier
> 
> 
> ## Example for object of class maigesActNet
> 
> ## Doing functional classification of gene groups for 'Tissue' sample label
> gastro.mod = activeMod(gastro.summ, sLabelID="Tissue", cutExp=1,
+   cutPhiper=0.05)
> 
> plot(gastro.mod, "S", margins=c(15,3)) ## Plot for individual samples
> plot(gastro.mod, "C", margins=c(21,5)) ## Plot for unique biological conditions
> 
> 
> 
> ## Example for object of class maigesRelNetB
> 
> ## Constructing the relevance network (Butte's method) for sample
> ## 'Tissue' equal to 'Neso' for the 1st gene group
> gastro.net = relNetworkB(gastro.summ, sLabelID="Tissue", 
+   samples="Neso", geneGrp=1, type="Rpearson")
> 
> plot(gastro.net, cutPval=0.05)
> 
> 
> 
> 
> ## Example for object of class maigesRelNetM
> 
> ## Constructing the relevance network for sample
> ## 'Tissue' comparing 'Neso' and 'Aeso' for the 1st gene group
> gastro.net = relNetworkM(gastro.summ, sLabelID="Tissue", 
+   samples = list(Neso="Neso", Aeso="Aeso"), geneGrp=11,
+   type="Rpearson")
> 
> plot(gastro.net, cutPval=0.05)
> plot(gastro.net, cutPval=0.01)
> 
> 
> 
> ## Example for objects of class maigesActNet
> 
> ## Doing functional classification of gene networks for sample Label
> ## given by 'Tissue'
> gastro.net = activeNet(gastro.summ, sLabelID="Tissue")
> 
> plot(gastro.net, type="score", margins=c(21,5))
> plot(gastro.net, type="p-value", margins=c(21,5))
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>