SEA: SEA
In rSEA: Simultaneous Enrichment Analysis

Description Usage Arguments Value Author(s) References See Also Examples

returns SEA chart (a data.frame) including the test results and estimates for the specified feature-sets from pathlist.

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SEA(pvalue, featureIDs, data, pathlist, select, tdphat = TRUE,
  selfcontained = TRUE, competitive = TRUE, thresh = NULL,
  alpha = 0.05)

`pvalue`	Vector of p-values. It can be the name of the covariate representing the Vector of all raw p-values in the `data` or a single vector but in the latter case it should match the `featureIDs` vector
`featureIDs`	Vector of feature IDs. It can be the name of the covariate representing the IDs in the `data` or a single vector but in the latter case it should match the `pvalue` vector
`data`	Optional data frame or matrix containing the variables in `pvalue` and `featureIDs`
`pathlist`	A list containing pathways defined by `featureIDs`. Checkout the vignette for more details and available codes to create your own pathway
`select`	A vector. Number or names of pathways of interest from the `pathlist` of choice. If missing, all pathways of the database will be included
`tdphat`	Logical. If `TRUE` the point estimate of the True Discoveries Proportion within each pathway will be calculated
`selfcontained`	Logical. If `TRUE` the self-contained null hypothesis will be tested for each pathway and the corresponding adj. p-value is returned
`competitive`	Logical. If `TRUE` the default competitive null hypothesis will be tested for each pathway and the corresponding adj. p-value is returned, you can define a threshold with `thresh` argument
`thresh`	A real number between 0 and 1. If specified, the competitive null hypothesis will be tested against this threshold for each pathway and the corresponding adj. p-value is returned
`alpha`	The type I error allowed for TDP bound. The default is 0.05.

A data.frame is returned including a list of pathways with corresponding TDP bound estimate, and if specified, TDP point estimate and adjusted p-values

Mitra Ebrahimpoor

m.ebrahimpoor@lumc.nl

Mitra Ebrahimpoor, Pietro Spitali, Kristina Hettne, Roula Tsonaka, Jelle Goeman, Simultaneous Enrichment Analysis of all Possible Gene-sets: Unifying Self-Contained and Competitive Methods, Briefings in Bioinformatics, , bbz074, https://doi.org/10.1093/bib/bbz074

setTest, topSEA,

## Not run: 
##Generate a vector of pvalues for a toy example
set.seed(159)

m<- 100
pvalues <- runif(m,0,1)^5
featureIDs <- as.character(1:m)

# perform a self-contained test for all features
setTest(pvalues, featureIDs, testype = "selfcontained")

# create 3 random pathway of size 60, 20 and 45
randpathlist=list(A=as.character(c(sample(1:m, 60))),
             B=as.character(c(sample(1:m, 20))),
             C=as.character(c(sample(1:m, 45))))


# get the seachart for the whole pathlist
S1<-SEA(pvalues, featureIDs, pathlist=randpathlist)
S1

# get the seachart for only first two pathways of the randpathlist
S2<-SEA(pvalues, featureIDs, pathlist=randpathlist, select=1:2)
S2

#sort the list by competitve p-value and select top 2
topSEA(S2, by=Comp.adjP, descending = FALSE, n=2)

#make an enrichment plot based on TDP.estimated of te pathways
#here pathways with more than 20% active signials will be identified
plotSEA(S1,threshold = 0.20,n=3)

## End(Not run)