PlotRunEnrichment: Plot running Edge enrichment score

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

Description

Plot running edge enrichment score for the pathway result

Usage

  PlotRunEnrichment(EdgeCorScore, PathwayResult, weighted.score.type = 1)

Arguments

EdgeCorScore	A numeric vector. Each element is the differential correlation score of an edge.
PathwayResult	A list of pathway result obtained from the ESEA.main function
weighted.score.type	A value. Edge enrichment correlation-based weighting: 0=no weight, 1=standard weigth, 2 = over-weigth. The default value is 1

Author(s)

Junwei Han <[e-mail]>, Xinrui Shi<[e-mail]> and Chunquan Li <[e-mail]>

Examples

## Not run: 

#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]

#Plot running edge enrichment score for the pathway result
PlotRunEnrichment(EdgeCorScore,PathwayResult,weighted.score.type = 1)


## End(Not run)

Example output

Loading required package: igraph

Attaching package: ‘igraph’

The following objects are masked from ‘package:stats’:

    decompose, spectrum

The following object is masked from ‘package:base’:

    union

Loading required package: XML
Loading required package: parmigene

ESEA documentation built on May 2, 2019, 3:41 p.m.