| clusterInteractions-methods {R3CPET} | R Documentation |
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.
## S4 method for signature 'ChromMaintainers'
clusterInteractions(object, method=c("clues","sota"), nbClus=20 )
object |
(Required) a non-empty |
method |
(optional)used to specify the method to use. if Another option is that user can first do an automatic clustering using |
nbClus |
(optional) The user-specified number of clusters. It is taken into consideration only if |
A ChromMaintainers object in which the clusRes is populated as a sota
or clues object.
Mohamed Nadhir Djekidel (nde12@mails.tsinghua.edu.cn)
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
ChromMaintainers, clues , sota, InferNetworks
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)