ProfilePlot: Plotting gene profiles

In MosaiClusteR: An Umbrella Framework for Multi-Source and Multi-Omics Clustering

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: 
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)

## End(Not run)

MosaiClusteR documentation built on Oct. 25, 2026, 1:08 a.m.