plot {maigesPack} | R Documentation |
Generic function plot
to display scatter plots
or other types of graphical representation for objects defined in this
package.
## 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], ...)
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 |
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.
Gustavo H. Esteves <gesteves@vision.ime.usp.br>
mt.rawp2adjp
,
backgroundcorrect
, maPlot
in
the package marray, plot
in the base package.
## 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))