PathwaysIter: Iterations of the pathway analysis
In IntClust: Integrated Data Analysis via Clustering

Description Usage Arguments Value Author(s) Examples

The MLP method to perform pathway analysis is based on resampling of the data. Therefore it is recommended to perform the pathway analysis multiple times to observe how much the results are influenced by a different resample. The function PathwaysIter performs the pathway analysis as described in Pathways a specified number of times. The input can be one data set or a list as in Pathway.2 and Pathways.

PathwaysIter(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)

`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 compounds, this selection can be provided here. Selection can be of the type "character" (names of the compounds) or "numeric" (the number of specific cluster).
`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.
`method`	The method to applied to look for DE genes. For now, only the limma method is available.
`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.
`geneSetSource`	The source for the getGeneSets function ("GOBP", "GOMF","GOCC", "KEGG" or "REACTOME").
`topP`	Overrules sign. The number of pathways to display for each cluster. If not specified, only the significant genes are shown.
`topG`	Overrules sign. The number of top genes to be returned in the result. If not specified, only the significant genes are shown.
`GENESET`	Optional. Can provide own candidate gene sets.
`sign`	The significance level to be handled.
`niter`	The number of times to perform pathway analysis.
`fusionsLog`	To be handed to ReorderToReference.
`WeightClust`	To be handed to ReorderToReference.
`names`	Optional. Names of the methods.

This element is again a list with the following four elements:

`Compounds`	A list with the elements LeadCpds (the compounds of interest) and OrderedCpds (all compounds in the order of the clustering result)
`Characteristics`	The found (top) characteristics of the feauture data
`Genes`	A list with the elements TopDE (a table with information on the top genes) and AllDE (a table with information on all genes)
`Pathways`	A list with the element ranked.genesets.table which is a data frame containing the genesets, their p-values and their descriptions. The second element is nr.genesets and contains the used and total number of genesets.

Marijke Van Moerbeke

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

MCF7_F = Cluster(fingerprintMat,type="data",distmeasure="tanimoto",normalize=FALSE,
method=NULL,clust="agnes",linkage="ward",gap=FALSE,maxK=55,StopRange=FALSE)
MCF7_T = Cluster(targetMat,type="data",distmeasure="tanimoto",normalize=FALSE,
method=NULL,clust="agnes",linkage="ward",gap=FALSE,maxK=55,StopRange=FALSE)

L=list(MCF7_F,MCF7_T)
names=c('FP','TP')

MCF7_PathsFandT=PathwaysIter(L, GeneExpr = geneMat, nrclusters = 7, method = c("limma", 
"MLP"), GeneInfo = GeneInfo, geneSetSource = "GOBP", topP = NULL, 
topG = NULL, GENESET = GS, sign = 0.05,niter=2,fusionsLog = TRUE, WeightClust = TRUE, 
 names =names)

## End(Not run)