DiffGenes: Differential gene expressions for multiple results
In IntClust: Integration of Multiple Data Sets with Clustering Techniques

Description Usage Arguments Details Value References Examples

The function DiffGenes will, given the output of a certain method, look for genes that are differentially expressed for each cluster by applying the limma function to that cluster and compare it to all other clusters simultaneously. If a list of outputs of several methods is provided, DiffGenes will perform the limma function for each method.

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DiffGenes(List, Selection = NULL, geneExpr = NULL, nrclusters = NULL,
  method = "limma", sign = 0.05, topG = NULL, fusionsLog = TRUE,
  weightclust = TRUE, names = NULL)

`List`	A list of the clustering outputs to be compared. The first element of the list will be used as the reference in `ReorderToReference`.
`Selection`	If differential gene expression should be investigated 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 or ExpressionSet of the objects. The rows should correspond with the genes.
`nrclusters`	Optional. The number of clusters to cut the dendrogram in. The number of clusters should not be specified if the interest lies only in a specific selection of objects which is known by name. Otherwise, it is required. Default is NULL.
`method`	The method to applied to look for DE genes. For now, only the limma method is available. Default is "limma".
`sign`	The significance level to be handled. Default is 0.05.
`topG`	Overrules sign. The number of top genes to be shown. Default is NULL.
`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.

The function rearranges the clusters of the methods to a reference method such that a comparison is made easier. Given a list of methods, it calls upon ReorderToReference to rearrange the number of clusters according to the first element of the list which will be used as the reference.

The returned value is a list with an element per method. Each element contains a list per cluster with the following elements:

`objects`	A list with the elements LeadCpds (the objects of interest) and OrderedCpds (all objects in the order of the clustering result)
`Genes`	A list with the elements TopDE (a table with information on the top genes) and AllDE (a table with information on all genes)

SMYTH, G. K. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology. 3(1).

data(fingerprintMat)
data(targetMat)
data(geneMat)

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_T ,MCF7_F)

MCF7_FT_DE = DiffGenes(List=L,geneExpr=geneMat,nrclusters=7,method="limma",
sign=0.05,topG=10,fusionsLog=TRUE,weightclust=TRUE)