inst/doc/ESEA.R

In ESEA: ESEA: Discovering the Dysregulated Pathways based on Edge Set Enrichment Analysis

### R code from vignette source 'ESEA.Rnw'

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### code chunk number 1: ESEA.Rnw:46-47
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library(ESEA)


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### code chunk number 2: ESEA.Rnw:58-64
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#obtain the data for background set of edges.
edgesbackgrand<-GetEdgesBackgrandData()
edgesbackgrand[1:10,]

#obtain the edge sets of pathways.
pathwayEdge.db<-GetPathwayEdgeData()


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### code chunk number 3: ESEA.Rnw:77-94
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#get example data
dataset<-GetExampleData("dataset")
class.labels<-GetExampleData("class.labels")
controlcharactor<-GetExampleData("controlcharactor")

#get the data for background set of edges
edgesbackgrand<-GetEdgesBackgrandData()

#Calculate the differential correlation score for edges
EdgeCorScore<-calEdgeCorScore(dataset, class.labels, controlcharactor, edgesbackgrand)

#print the top ten results to screen
EdgeCorScore[1:10]

#Each element is the differential correlation score of an edge and whose name
# correspond to the edge in the background set of edges.


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### code chunk number 4: ESEA.Rnw:104-151
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#get example data
dataset<-GetExampleData("dataset")
class.labels<-GetExampleData("class.labels")
controlcharactor<-GetExampleData("controlcharactor")

#get the data for background set of edges
edgesbackgrand<-GetEdgesBackgrandData()

#get the edge sets of pathways
pathwayEdge.db<-GetPathwayEdgeData()

#calculate the differential correlation score for edges
EdgeCorScore<-calEdgeCorScore(dataset, class.labels, controlcharactor, edgesbackgrand)

#identify dysregulated pathways by using the function ESEA.Main
Results<-ESEA.Main(
EdgeCorScore,
pathwayEdge.db,
weighted.score.type = 1, 
pathway = "kegg", 
gs.size.threshold.min = 15, 
gs.size.threshold.max = 1000,
reshuffling.type = "edge.labels",
nperm =10, 
p.val.threshold=-1,
FDR.threshold = 0.05, 
topgs =1
)

#print the summary results of pathways to screen
Results[[1]][[1]][1:5,]

#print the detail results of pathways to screen
Results[[2]][[1]][1:5,]

#write the summary results of pathways to tab delimited file.
write.table(Results[[1]][[1]], file = "kegg-SUMMARY RESULTS Gain-of-correlation.txt",
 quote=F, row.names=F, sep = "\t")
write.table(Results[[1]][[2]], file = "kegg-SUMMARY RESULTS Loss-of-correlation.txt",
 quote=F, row.names=F, sep = "\t")

#write the detail results of genes for each pathway with FDR.threshold< 0.05 to tab delimited file.
for(i in 1:length(Results[[2]])){
PathwayList<-Results[[2]][[i]]
filename <- paste(names(Results[[2]][i]),".txt", sep="", collapse="")
write.table(PathwayList, file = filename, quote=F, row.names=F, sep = "\t")
}


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### code chunk number 5: ESEA.Rnw:160-171
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#get example data
dataset<-GetExampleData("dataset")
class.labels<-GetExampleData("class.labels")
controlcharactor<-GetExampleData("controlcharactor")

#get the data for background set of edges
edgesbackgrand<-GetEdgesBackgrandData()

#calculate the differential correlation score for edges
EdgeCorScore<-calEdgeCorScore(dataset, class.labels, controlcharactor, edgesbackgrand)



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### code chunk number 6: GlobEdgeCorProfile
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#plot global edge correlation profile
PlotGlobEdgeCorProfile(EdgeCorScore)


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### code chunk number 7: ESEA.Rnw:191-212
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#get example data
dataset<-GetExampleData("dataset")
class.labels<-GetExampleData("class.labels")
controlcharactor<-GetExampleData("controlcharactor")

#get the data for background set of edges
edgesbackgrand<-GetEdgesBackgrandData()

#get the edge sets of pathways
pathwayEdge.db<-GetPathwayEdgeData()

#calculate the differential correlation score for edges
EdgeCorScore<-calEdgeCorScore(dataset, class.labels, controlcharactor,edgesbackgrand)

#identify dysregulated pathways by using the function ESEA.Main
#Results<-ESEA.Main(EdgeCorScore,pathwayEdge.db)
Results<-GetExampleData("PathwayResult")

#obtain the detail results of genes for a significant pathway
PathwayResult<-Results[[2]][1]



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### code chunk number 8: RunEdgeCorScore
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#Plot running edge enrichment score for the pathway result
PlotRunEnrichment(EdgeCorScore,PathwayResult,weighted.score.type = 1)


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### code chunk number 9: ESEA.Rnw:233-254
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#get example data
dataset<-GetExampleData("dataset")
class.labels<-GetExampleData("class.labels")
controlcharactor<-GetExampleData("controlcharactor")

#get the data for background set of edges
edgesbackgrand<-GetEdgesBackgrandData()

#get the edge sets of pathways
pathwayEdge.db<-GetPathwayEdgeData()

#calculate the differential correlation score for edges
EdgeCorScore<-calEdgeCorScore(dataset, class.labels, controlcharactor,edgesbackgrand)

#identify dysregulated pathways by using the function ESEA.Main
#Results<-ESEA.Main(EdgeCorScore,pathwayEdge.db)
Results<-GetExampleData("PathwayResult")

#obtain the detail results of genes for a significant pathway
PathwayNetwork<-Results[[2]][[1]]



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### code chunk number 10: PathwayNetwork
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#Plot the pathway-result network diagram, the edges which contribute to the ES are labeled with red.
PlotPathwayGraph(PathwayNetwork,layout=layout.random)


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### code chunk number 11: ESEA.Rnw:275-298
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#get example data
dataset<-GetExampleData("dataset")
class.labels<-GetExampleData("class.labels")
controlcharactor<-GetExampleData("controlcharactor")

#get the data for background set of edges
edgesbackgrand<-GetEdgesBackgrandData()

#get the edge sets of pathways
pathwayEdge.db<-GetPathwayEdgeData()

#calculate the differential correlation score for edges
EdgeCorScore<-calEdgeCorScore(dataset, class.labels, controlcharactor,edgesbackgrand)

#identify dysregulated pathways by using the function ESEA.Main
#Results<-ESEA.Main(EdgeCorScore,pathwayEdge.db)
Results<-GetExampleData("PathwayResult")

#obtain the detail results of genes for a significant pathway
PathwayNetwork<-Results[[2]][[1]]

#save the pathway-result network to a file which can be input to the Cytoscape software. 
SavePathway2File(PathwayNetwork,layout=layout.circle,file="Graph")


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### code chunk number 12: sessionInfo
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sessionInfo()