maigesPack (version 1.36.0)

plot: 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

"plot"(x, bkgSub="subtract", z=NULL, legend.func=NULL, ylab="W", ...)
"plot"(x, z=NULL, legend.func=NULL, ylab="W", ...)
"plot"(x, z=NULL, legend.func=NULL, ylab="W", ...)
"plot"(x, adjP="none", idx=1, ...)
"plot"(x, adjP="none", idx=1, ...)
"plot"(x, idx=1, ...)
"plot"(x=NULL, cutPval=0.05, cutCor=NULL,
name=NULL, ...)
"plot"(x=NULL, cutPval=0.05, names=NULL, ...)
"plot"(x, type=c("S", "C")[2], keepEmpty=FALSE, ...)
"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.

See Also

mt.rawp2adjp, backgroundcorrect, maPlot in the package marray, plot in the base package.

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))