PathwayAnalysis: Pathway Analysis
In IntClust: Integration of Multiple Data Sets with Clustering Techniques

Description Usage Arguments Value Examples

The PathwayAnalysis function combines the functions PathwaysIter and Geneset.intersect such that only one function should be called.

PathwayAnalysis(List, Selection = NULL, geneExpr = NULL,
  nrclusters = NULL, method = c("limma", "MLP"), geneInfo = NULL,
  geneSetSource = "GOBP", topP = NULL, topG = NULL, GENESET = NULL,
  sign = 0.05, niter = 10, fusionsLog = TRUE, weightclust = TRUE,
  names = NULL, seperatetables = FALSE, separatepvals = FALSE)

`List`	A list of clustering outputs or output of the`DiffGenes` function. The first element of the list will be used as the reference in `ReorderToReference`. The output of `ChooseFeatures` is also accepted.
`Selection`	If pathway analysis should be conducted for a specific selection of objects, this selection can be provided here. Selection can be of the type "character" (names of the objects) or "numeric" (the number of specific cluster). Default is NULL.
`geneExpr`	The gene expression matrix of the objects. The rows should correspond with the genes.
`nrclusters`	The number of clusters to cut the dendrogram in. Default is NULL.
`method`	The method to applied to look for differentially expressed genes and related pathways. For now, only the limma method is available for gene analysis and the MLP method for pathway analysis. Default is c("limma","MLP").
`geneInfo`	A data frame with at least the columns ENTREZID and SYMBOL. This is necessary to connect the symbolic names of the genes with their EntrezID in the correct order. The order of the gene is here not in the order of the rownames of the gene expression matrix but in the order of their significance. Default is NULL.
`geneSetSource`	The source for the getGeneSets function, defaults to "GOBP".
`topP`	Overrules sign. The number of pathways to display for each cluster. If not specified, only the significant genes are shown. Default is NULL.
`topG`	Overrules sign. The number of top genes to be returned in the result. If not specified, only the significant genes are shown. Default is NULL.
`GENESET`	Optional. Can provide own candidate gene sets. Default is NULL.
`sign`	The significance level to be handled. Default is 0.05.
`niter`	The number of times to perform pathway analysis. Default is 10.
`fusionsLog`	Logical. To be handed to `ReorderToReference`: indicator for the fusion of clusters. Default is TRUE
`weightclust`	Logical. To be handed to `ReorderToReference`: to be used for the outputs of CEC, WeightedClust or WeightedSimClust. If TRUE, only the result of the Clust element is considered. Default is TRUE.
`names`	Optional. Names of the methods. Default is NULL.
`seperatetables`	Logical. If TRUE, a separate element is created per cluster. containing the pathways for each iteration. Default is FALSE.
`separatepvals`	Logical. If TRUE, the p-values of the each iteration of each pathway in the intersection is given. If FALSE, only the mean p-value is provided. Default is FALSE.

The output is a list with an element per method. For each method, it is portrayed per cluster which pathways belong to the intersection over all iterations and their corresponding mean p-values.

## 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_PathsFandT=PathwayAnalysis(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,seperatetables=FALSE,separatepvals=FALSE)

## End(Not run)