IntClust (version 0.0.2)

PathwayAnalysis: Pathway Analysis

Description

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

Usage

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)

Arguments

List

A list of clustering outputs or output of theDiffGenes 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, defaults to "GOBP".

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.

seperatetables

Logical. If TRUE, a separate element is created per cluster. containing the pathways for each iteration.

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.

Value

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.

Examples

# 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=PathwaysAnalysis(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,seperatetables=FALSE,separatepvals=FALSE)
# }