IntClust (version 0.1.0)

ProfilePlot: Plotting gene profiles

Description

In ProfilePlot, the gene profiles of the significant genes for a specific cluster are shown on 1 plot. Therefore, each gene is normalized by subtracting its the mean.

Usage

ProfilePlot(Genes, Comps, geneExpr = NULL, raw = FALSE, orderLab = NULL,
  colorLab = NULL, nrclusters = NULL, cols = NULL, addLegend = TRUE,
  margins = c(8.1, 4.1, 1.1, 6.5), extra = 5, plottype = "new",
  location = NULL)

Arguments

Genes

The genes to be plotted.

Comps

The objects to be plotted or to be separated from the other objects.

geneExpr

The gene expression matrix or ExpressionSet of the objects.

raw

Logical. Should raw p-values be plotted? Default is FALSE.

orderLab

Optional. If the objects are to set in a specific order of a specific method. Default is NULL.

colorLab

The clustering result that determines the color of the labels of the objects in the plot. Default is NULL.

nrclusters

Optional. The number of clusters to cut the dendrogram in.

cols

Optional. The color to use for the objects in Clusters for each method.

addLegend

Optional. Whether a legend of the colors should be added to the plot.

margins

Optional. Margins to be used for the plot. Default is margins=c(8.1,4.1,1.1,6.5).

extra

The space between the plot and the legend. Default is 5.

plottype

Should be one of "pdf","new" or "sweave". If "pdf", a location should be provided in "location" and the figure is saved there. If "new" a new graphic device is opened and if "sweave", the figure is made compatible to appear in a sweave or knitr document, i.e. no new device is opened and the plot appears in the current device or document. Default is "new".

location

If plottype is "pdf", a location should be provided in "location" and the figure is saved there. Default is NULL.

Value

A plot which contains multiple gene profiles. A distinction is made between the values for the objects in Comps and the others.

Examples

# NOT RUN {
# }
# NOT RUN {
data(fingerprintMat)
data(targetMat)
data(geneMat)

MCF7_F = Cluster(fingerprintMat,type="data",distmeasure="tanimoto",normalize=FALSE,
	method=NULL,clust="agnes",linkage="flexible",gap=FALSE,maxK=55,StopRange=FALSE)
MCF7_T = Cluster(targetMat,type="data",distmeasure="tanimoto",normalize=FALSE,
	method=NULL,clust="agnes",linkage="flexible",gap=FALSE,maxK=55,StopRange=FALSE)

L=list(MCF7_F,MCF7_T)
names=c('FP','TP')

MCF7_FT_DE = DiffGenes(List=L,geneExpr=geneMat,nrclusters=7,method="limma",sign=0.05,topG=10,
fusionsLog=TRUE,weightclust=TRUE)

Comps=SharedComps(list(MCF7_FT_DE$`Method 1`$"Cluster 1",MCF7_FT_DE$`Method 2`$"Cluster 1"))[[1]]

MCF7_SharedGenes=FindGenes(dataLimma=MCF7_FT_DE,names=c("FP","TP"))

Genes=names(MCF7_SharedGenes[[1]])[-c(2,4,5)]

colscl=ColorPalette(colors=c("red","green","purple","brown","blue","orange"),ncols=9)

ProfilePlot(Genes=Genes,Comps=Comps,geneExpr=geneMat,raw=FALSE,orderLab=MCF7_F,
colorLab=NULL,nrclusters=7,cols=colscl,addLegend=TRUE,margins=c(16.1,6.1,1.1,13.5),
extra=4,plottype="sweave",location=NULL)
# }
# NOT RUN {
# }