R3CPET (version 1.4.2)

plot3CPETRes-methods: Plotting clustering results

Description

This method enables the user the generate different types of plots to visualize the results.

Usage

"plot3CPETRes"(object, path="", W=14, H=7 ,  type=c("heatmap","clusters","curve","avgCurve","netSim", "networks"), byEdge=TRUE, layoutfct=layout.kamada.kawai, ...)

Arguments

object

(Required) a ChromMaintainers object that contains the results

path

(optional) path where to save the plots should be ".pdf". if not provided the plot will be displayed on the screen.

W

(optional) The width of the plot in the pdf file.by default it is 14 inch

H

(optional) The Height of the plot in the pdf file.by default it is 7 inch

type

type of the plot to generate. It can support the following values ((default: "heatmap")):

• "heatmap" : Generates a heatmap of the partnership of each DNA interaction to a chromatin maintainer network. Each column is a DNA interaction and each row a chromatin maintainer network.
• "clusters" : generates a pair-wise scatter plots of all clusters. Note: only supported if the sota method was applied.
• "curve" : for each cluster plots the enrichment profile of all the elements.
• "avgCurve" : draws the average curve of the enrichment profile for each clusters.
• "netSim" : plots a heatmap showing the percentage of common proteins or edges between the chromatin maintainer networks. Note: generally the similarity between networks is so small so the user can set the value in the diagonal to zero and then re-plot or plot the dissimilarity plot. if byEdge = TRUE a similarity based on the common edges is calculated otherwise by common nodes.
• "networks" : plots all the networks. if this option is chosen a pdf file named "AllGraphs.pdf" is generated in the current working unless the path parameter is explicitly determined. To get a finer control, the user can specify the type of layout to use, by default the layout.kamada.kawai is used. For additional layout functions you can check the igraph package. The reason we generate a pdf file because there is a lot of networks and it will not be convenient to display them in one plot, or generating multiple plots.

byEdge

(optional) if TRUE and type = "netSim" then the a heatmap showing the similarity between the chromatin maintainer networks by common edges. if FALSE the similarity is calculated based on the number of common nodes.

layoutfct

(optional) The graph layout algorithm to use. by default the layout.kamada.kawai is used. Additional functions are available in the igraph package.

...

options for future use.

Value

Different types of values are returned depending on the type of the plot selected.

"heatmap"

returns a list generated by the pheatmap method, however it is always empty.

"clusters","curve","avgCurve"

returns a list describing the number of plots per row and column.

"netSim"

returns a list that contains a ggplot2 object and the similarity matrix

"networks"

returns a list of ggplot2 objects, one per network.

See Also

cluster, igraph, sota

Examples

    ## 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: 
#     data(RPKMS)
#     x <- ChiapetExperimentData(pet = petFile, tfbs=  tfbsFile, IsBed = FALSE, ppiType="HPRD", filter= TRUE) 
#     ## build the different indexes
#     x <- createIndexes(x)
#     
#     ## build networks connecting each interacting regions
#     nets<- buildNetworks(x)
# 
#     ## infer the networks
#     hlda<- InferNetworks(nets)
# 
#     ## cluster results
#     hlda<- clusterInteractions(hlda)
# 
#     ## plot a heatmap
#     plot3CPETRes(hlda,type="heatmap")
# 
#     ## plot clusters pair-wise scatter plots
#     plot3CPETRes(hlda,type="clusters")
# 
#     ## enrichment plot for the elements in each network
#     plot3CPETRes(hlda,type="curve")
# 
#     ##  average enrichment plot for the elements in each network
#     plot3CPETRes(hlda,type="avgCurve")
# 
#     ## heatmap showing the similarity between the different network
#     plot3CPETRes(hlda,type="netSim")
# 
#     ## plot all the networks in the file "AllGraphs.pdf" 
#     nets_plot <- plot3CPETRes(hlda,type="networks")
# 
#     ## plot one of the networks
#     plot(nets_plot[[3]])
# 
# ## End(Not run)