SEA: SEA

Description

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

Usage

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

Arguments

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.

Value

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

References

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

Examples

Run this code

# NOT RUN {
# }
# 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)
# }