DiffGenes: Find differentially expressed genes

In MosaiClusteR: An Umbrella Framework for Multi-Source and Multi-Omics Clustering

Find differentially expressed genes

Description

The DiffGenes function looks for differentially expressed genes for each cluster of a clustering result by comparing the objects of a cluster against all other objects. The limma method is applied to find the differentially expressed genes.

Usage

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

Arguments

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. 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.

Details

This function relies on the suggested Bioconductor package 'limma'.

Value

The result is a list with an element per method. Each element is again a list with for each cluster the found differentially expressed genes.

Examples

## Not run: 
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_F,MCF7_T)
names=c('FP','TP')

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

## End(Not run)

MosaiClusteR documentation built on Oct. 25, 2026, 1:08 a.m.