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
provide this information. Given the outputs of the
Geneset.intersect function and/or
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
Optional. The output of a
Optional. The output of
Optional. The output of
Optional. Names of the methods or "Selection" if it only considers a selection of objects. Default is NULL.
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.
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## 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)
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