SharedGenesPathsFeat: Intersection of genes and pathways over multiple methods
In IntClust: Integration of Multiple Data Sets with Clustering Techniques

Description Usage Arguments Value Examples

It is interesting to investigate exactly which and how many differently expressed genes, pathways and characteristics are shared by the clusters over the different methods. The function SharedGenesPathsFeat will provide this information. Given the outputs of the DiffGenes, the Geneset.intersect function and/or CharacteristicFeatures, it investigates how many genes, pathways and/or characteristics are expressed by each cluster per method, how many of these are shared over the methods and which ones are shared including their respective p-values of each method and a mean p-value. This is very handy to look into the shared genes and pathways of clusters that share many objects but also of those that only share only a few. Further, the result also includes the number of objects per cluster per method and how many of these are shared over the methods. The input can also be focused for a specific selection of objects or a specific cluster.

1 2	SharedGenesPathsFeat(DataLimma = NULL, DataMLP = NULL, DataFeat = NULL, names = NULL, Selection = FALSE)

`DataLimma`	Optional. The output of a `DiffGenes` function. Default is NULL.
`DataMLP`	Optional. The output of `Geneset.intersect` function. Default is NULL.
`DataFeat`	Optional. The output of `CharacteristicFeatures` function. Default is NULL.
`names`	Optional. Names of the methods or "Selection" if it only considers a selection of objects. Default is NULL.
`Selection`	Logical. Do the results concern only a selection of objects or a specific cluster? If yes, then Selection should be put to TRUE. Otherwise all objects and clusters are considered.Default is FALSE.

The result of the SharedGenesPathsFeat function is a list with two elements. The first element Table is a table indicating how many genes, pathways and/or characteristics were found to be differentially expressed and how many of these are shared. The table also contains the number of objects shared between the clusters of the different methods. The second element Which is another list with a component per cluster. Each component consists of four vectors: SharedComps indicating which objects were shared across the methods, SharedGenes represents the shared genes, SharedPaths shows the shared pathways and SharedFeat the shared features.

## Not run: 
data(fingerprintMat)
data(targetMat)
data(geneMat)
data(GeneInfo)

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_Paths_FandT=PathwaysIter(List=L, geneExpr=geneMat, nrclusters=7, method=
c("limma", "MLP"), geneInfo=GeneInfo, geneSetSource="GOBP", topP=NULL,
topG=NULL, GENESET=NULL, sign=0.05,niter=2,fusionsLog=TRUE, 
weightclust=TRUE, names =names)

MCF7_Paths_intersection=Geneset.intersect(MCF7_Paths_FandT,0.05,names=names,
seperatetables=FALSE,separatepvals=FALSE)

MCF7_DiffGenes_FandT10=DiffGenes(list(MCF7_F,MCF7_T),Selection=NULL,geneExpr=geneMat,
nrclusters=7,method="limma",sign=0.05,top=10,fusionsLog=TRUE,weightclust=TRUE,names=NULL)

MCF7_Char=CharacteristicFeatures(list(MCF7_F,MCF7_T),Selection=NULL,binData=
list(fingerprintMat,targetMat),datanames=c("FP","TP"),nrclusters=7,top=NULL,
sign=0.05,fusionsLog=TRUE,weightclust=TRUE,names=c("FP","TP")) 

MCF7_Shared=SharedGenesPathsFeat(DataLimma=MCF7_DiffGenes_FandT10,
DataMLP=MCF7_Paths_intersection,DataFeat=MCF7_Char)

str(MCF7_Shared)

## End(Not run)