IntClust (version 0.1.0)

BinFeaturesPlot_MultipleData: Visualization of characteristic binary features of multiple data sets

Description

A tool to visualize characteristic binary features of a set of objects in comparison with the remaining objects for multiple data sets. The result is a matrix with coloured cells. Columns represent objects and rows represent the specified features. A feature which is present is give a coloured cell while an absent feature is represented by a grey cell. The labels on the right indicate the names of the features while the labels on the bottom are the names of the objects.

Usage

BinFeaturesPlot_MultipleData(leadCpds, orderLab, features = list(),
  data = list(), validate = NULL, colorLab = NULL, nrclusters = NULL,
  cols = NULL, name = c("Data1", "Data2"), colors1 = c("gray90", "blue"),
  colors2 = c("gray90", "green"), margins = c(5.5, 3.5, 0.5, 5.5),
  cexB = 0.8, cexL = 0.8, cexR = 0.8, spaceNames = 0.2,
  plottype = "new", location = NULL)

Arguments

leadCpds

A character vector with the names of the objects in a first group, i.e., the group for which the specified features are characteristic. Default is NULL.

orderLab

A character vector with the order of the objects. Default is NULL.

features

A list with as elements character vectors with the names of the features to be visualized for each data set. Default is NULL.

data

A list with the different data sets. Default is NULL.

validate

Optional. A list with validation data sets. If a feature has a validation reference, these are added in a red colour. Default is NULL.

colorLab

Optional. A clustering object if the objects are to be coloured accoring to their clustering order. Default is NULL.

nrclusters

Optional. The number of clusters to divide the dendrogram of ColorLab. Default is NULL.

cols

Optional. A character vector with the colours of the different clusters. Default is NULL.

name

A character string with the names of the data sets. Default is c("Data1" ,"Data2") for two data sets.

colors1

A character vector with the colours to indicate the presence (first element) or the absence of the features for the objects in LeadCpds. Default is c('gray90','blue').

colors2

A character vector with the colours to indicate the presence (first element) or the absence of the features for the objects in the remaining objects. Default is c('gray90','green').

margins

A vector with the margings of the plot. Default is c(5.5,3.5,0.5,5.5).

cexB

The font size of the labels on the bottom: the object labels. Default is 0.80.

cexL

The font size of the labels on the left: the data labels. Default is 0.80.

cexR

The font size of the labels on the right: the feature labels. Default is 0.80.

spaceNames

A percentage of the height of the figure to be reserved for the names of the objects. Default is 0.20.

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

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

Examples

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

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

Comps=FindCluster(list(MCF7_F),nrclusters=10,select=c(1,8)) 

MCF7_Char=CharacteristicFeatures(List=NULL,Selection=Comps,binData=
list(fingerprintMat,targetMat),datanames=c("FP","TP"),nrclusters=NULL,
topC=NULL,sign=0.05,fusionsLog=TRUE,weightclust=TRUE,names=c("FP","TP"))

FeatFP=MCF7_Char$Selection$Characteristics$FP$TopFeat$Names[c(1:10)]
FeatTP=MCF7_Char$Selection$Characteristics$TP$TopFeat$Names[c(1:10)]

BinFeaturesPlot_MultipleData(leadCpds=Comps,orderLab=MCF7_Char$Selection$
objects$OrderedCpds,features=list(FeatFP,FeatTP),data=list(fingerprintMat,targetMat),
validate=NULL,colorLab=NULL,nrclusters=NULL,cols=NULL,name=c("FP","TP"),colors1=
c('gray90','blue'),colors2=c('gray90','green'),margins=c(5.5,3.5,0.5,5.5),cexB=0.80,
cexL=0.80,cexR=0.80,spaceNames=0.20,plottype="new",location=NULL)
# }