TESEA.Main: Topological edge set enrichment analysis
In blasern/TESEA: Topological Edge Set Enrichment Analysis

Description Usage Arguments Author(s) Examples

Topological edge set enrichment analysis

TESEA.Main(EdgeScore, pathwayEdge.db, weighted.score.type = 1,
  pathway = "kegg", gs.size.threshold.min = 15,
  gs.size.threshold.max = 1000, reshuffling.type = "edge.labels",
  nperm = 100, p.val.threshold = -1, FDR.threshold = 0.05, topgs = 1)

`EdgeScore`	A numeric vector. Each element is the differential score of an edge from `triangle_creation_score` or `calEdgeCorScore`.
`pathwayEdge.db`	A character vector, the length of it is the number of pathways.
`weighted.score.type`	A value. Edge enrichment correlation-based weighting: 0=no weight, 1=standard weigth, 2 = over-weigth. The default value is 1
`pathway`	A character string of pathway database. Should be one of "kegg","reactome", "nci","huamncyc","biocarta","spike" and "panther". The default value is "kegg"
`gs.size.threshold.min`	An integer. The minimum size (in edges) for pathways to be considered. The default value is 15.
`gs.size.threshold.max`	An integer. The maximum size (in edges) for pathways to be considered. The default value is 1000.
`reshuffling.type`	A character string. The type of permutation reshuffling: "edge.labels" or "gene.labels". The default value is "edge.labels".
`nperm`	An integer. The number of permutation reshuffling. The default value is 100.
`p.val.threshold`	A value. The significance threshold of NOM p-value for pathways whose detail results of pathways to be presented. The default value is -1, which means no threshold.
`FDR.threshold`	A value. The significance threshold of FDR q-value for pathways whose detail results of pathways to be presented. The default value is 0.05.
`topgs`	An integer. The number of top scoring gene sets used for detailed reports. The default value is 1.

Junwei Han, Xinrui Shi and Chunquan Li wrote the original in the ESEA package, small changes by Nello Blaser.

## Not run: 
# set random seed
set.seed(1)
# get data from ESEA
ESEA::initializeESEA()
edgesbackground <- ESEA::GetEdgesBackgrandData()
pathwayEdge.db <- ESEA::GetPathwayEdgeData()
dataset <- ESEA::GetExampleData("dataset")
class.labels <- ESEA::GetExampleData("class.labels")
controlcharacter <- ESEA::GetExampleData("controlcharactor") 
# calculate edge score (triangle version)
EdgeTriScore <- triangle_creation_score(dataset,
                                        class.labels, 
                                        controlcharacter, 
                                        edgesbackground)
# topological edge set enrichment analysis
Results_Tri <- TESEA.Main(
  EdgeTriScore, 
  pathwayEdge.db, weighted.score.type = 1,
  pathway = "kegg", gs.size.threshold.min = 15,
  gs.size.threshold.max = 1000, 
  reshuffling.type = "edge.labels", nperm = 1000,
  p.val.threshold= -1, FDR.threshold = 0.05, topgs =1)
# look at results
res_Tri <- Results_Tri[["summary"]]
res_Tri[res_Tri[, 'FDR q-val'] < 0.05, c("GS", "SIZE", "ES", "NES")]
##                                       GS SIZE       ES       NES
## 103 Maturity onset diabetes of the young   17  0.90011  2.373214
## 15   Antigen processing and presentation   56 -0.54973 -1.931995
## 126                p53 signaling pathway   70  0.46139  1.638150
## 147               Rap1 signaling pathway  980 -0.27030 -1.296834
## 148                Ras signaling pathway  976 -0.26085 -1.245997
# plot first pathway
PlotTESEAPathwayGraph(Results_Tri[['pathways']][[1]])

## End(Not run)