R3CPET (version 1.4.2)

clusterInteractions-methods: Grouping DNA interactions by enrichment profile

Description

This method aims at clustering the DNA interactions according to their partnership probability to the inferred chromatin maintainer networks. Two kinds of clustering are supported supervised and un-supervised. In the first one the functionsota from the clValid package. In the second case the clues method from the clues package is used.

Usage

"clusterInteractions"(object, method=c("clues","sota"), nbClus=20 )

Arguments

object

(Required) a non-empty ChromMaintainers object

method

(optional)used to specify the method to use. if clues is specified, the number of clusters will be determined automatically. if method = "sota" , the user can specify the number of clusters by setting the parameter nbClus, by default it is set to 20. by default method = "clues". Another option is that user can first do an automatic clustering using "clues" and then if judges that more or less clusters are required he can recall the clusterInteractions method and specify the number the number of clusters.

nbClus

(optional) The user-specified number of clusters. It is taken into consideration only if method = sota.

Value

A ChromMaintainers object in which the clusRes is populated as a sota or clues object.

References

Herrero, J., Valencia, A, and Dopazo, J. (2005). A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics, 17, 126-136.

Wang, S., Qiu, W., and Zamar, R. H. (2007). CLUES: A non-parametric clustering method based on local shrinking. Computational Statistics & Data Analysis, Vol. 52, issue 1, pages 286-298

See Also

ChromMaintainers, clues , sota, InferNetworks

Examples

 data(RPKMS)

  ## get the different datasets path
  petFile <- file.path(system.file("example",package="R3CPET"),"HepG2_interactions.txt")  
  tfbsFile <- file.path(system.file("example",package="R3CPET"),"HepG2_TF.txt.gz")  
    
## Not run: 
#   x <- ChiapetExperimentData(pet = petFile, tfbs=  tfbsFile, IsBed = FALSE, ppiType="HPRD", filter= TRUE) 
#   ## build the diffrent indexes
#   x <- createIndexes(x)
#   x
# 
#   ## build networks connecting each interacting regions
#   nets<- buildNetworks(x)
# 
#   ## infer the networks
#   hlda<- InferNetworks(nets)
# 
#   #cluster
#   hlda<- clusterInteractions(hlda)
# 
#   #Display heatmap
#   plot3CPETRes(hlda,type="heatmap")
#   hlda
# ## End(Not run)