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Type:

Package

Title:

Simultaneous Enrichment Analysis

Version:

2.1.2

Author:

Mitra Ebrahimpoor

Maintainer:

Mitra Ebrahimpoor<mitra.ebrahimpoor@gmail.com>

Description:

SEA performs simultaneous feature-set testing for (gen)omics data. It tests the unified null hypothesis and controls the family-wise error rate for all possible pathways. The unified null hypothesis is defined as: "The proportion of true features in the set is less than or equal to a threshold." Family-wise error rate control is provided through use of closed testing with Simes test. There are some practical functions to play around with the pathways of interest.

Depends:

R (≥ 2.10), hommel (≥ 1.4), ggplot2

Suggests:

knitr, rmarkdown

License:

GPL-2 |GPL-3 [expanded from: GPL (≥ 2)]

Date:

2024-06-12

Encoding:

UTF-8

VignetteBuilder:

knitr

RoxygenNote:

7.3.1

NeedsCompilation:

Packaged:

2024-06-11 23:09:51 UTC; mitra

Repository:

CRAN

Date/Publication:

2024-06-11 23:30:02 UTC

Simultaneous Enrichment Analysis (SEA) of all possible feature-sets using the unified null hypothesis

Description

This package uses raw p-values of genomic features as input and evaluates any given list of feature-sets or pathways. For each set the adjusted p-value and TDP lower-bound are calculated. The type of test can be defined by arguments and can be refined as necessary. The p-values are corrected for every possible set of features, making the method flexible in choice of pathway list and test type.For more details see: Ebrahimpoor, M (2019) <doi:10.1093/bib/bbz074>

Details

The unified null hypothesis is tested using closed testing procedure and all-resolutions inference. It combines the self-contained and ompetitive approaches in one framework. In short, using p-values of the individual features as input, the package can provide an FWER-adjusted p-value along with a lower bound and a point estimate for the proportion of true discoveries per feature-set. The flexibility in revising the choice of feature-sets without inflating type-I error is the most important property of SEA.

Author(s)

Mitra Ebrahimpoor.

Maintainer: Mitra Ebrahimpoor<m.ebrahimpoor@lumc.nl>

References

Mitra Ebrahimpoor, Pietro Spitali, Kristina Hettne, Roula Tsonaka, Jelle Goeman,Simultaneous Enrichment Analysis of all Possible Gene-sets: Unifying Self-Containedand Competitive Methods, Briefings in Bioinformatics,bbz074https://doi.org/10.1093/bib/bbz074

SEA

Description

returns SEA chart (a data.frame) including the test results and estimates for the specifiedfeature-sets frompathlist.

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 ofall raw p-values in thedata or a single vector but in the latter case it should match thefeatureIDs vector

featureIDs

Vector of feature IDs. It can be the name of the covariate representing the IDs in thedata or a single vector but in the latter case it should match thepvalue vector

data

Optional data frame or matrix containing the variables inpvalue andfeatureIDs

pathlist

A list containing pathways defined byfeatureIDs. Checkout the vignettefor more details and available codes to create your own pathway

select

A vector. Number or names of pathways of interest from thepathlist of choice.If missing, all pathways of the database will be included

tdphat

Logical. IfTRUE the point estimate of the True Discoveries Proportionwithin each pathway will be calculated

selfcontained

Logical. IfTRUE the self-contained null hypothesis will be testedfor each pathway and the corresponding adj. p-value is returned

competitive

Logical. IfTRUE the default competitive null hypothesis will be testedfor each pathway and the corresponding adj. p-value is returned, you can define a threshold withthresh argument

thresh

A real number between 0 and 1. If specified, the competitive null hypothesis will be testedagainst 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

Author(s)

Mitra Ebrahimpoor

m.ebrahimpoor@lumc.nl

References

Examples

## Not run: ##Generate a vector of pvalues for a toy exampleset.seed(159)m<- 100pvalues <- runif(m,0,1)^5featureIDs <- as.character(1:m)# perform a self-contained test for all featuressetTest(pvalues, featureIDs, testype = "selfcontained")# create 3 random pathway of size 60, 20 and 45randpathlist=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 pathlistS1<-SEA(pvalues, featureIDs, pathlist=randpathlist)S1# get the seachart for only first two pathways of the randpathlistS2<-SEA(pvalues, featureIDs, pathlist=randpathlist, select=1:2)S2#sort the list by competitve p-value and select top 2topSEA(S2, by=Comp.adjP, descending = FALSE, n=2)#make an enrichment plot based on TDP.estimated of pathwaysplotSEA(S1,n=3)## End(Not run)

topSEA

Description

returns a plotof SEA-chart which illustratesproportion of discoveries per pathway.

Usage

plotSEA(object, by = "TDP.estimate", threshold = 0.005, n = 20)

Arguments

object

A SEA-chart object which is the output ofSEA function

by

the Variable which will we mapped.It should be either the TDP estimate or TDP bound.The default is TDP bound.

threshold

A real number between 0 and 1. Which will be used asa visual aid to distinguish significant pathways

n

Integer. Number of rows from SEA-chart object to be plotted.

Value

Returns a plot of SEA_chart according to the selected arguments

Author(s)

Mitra Ebrahimpoor

m.ebrahimpoor@lumc.nl

References

Examples

#See the examples for \code{\link{SEA}}

setTDP

Description

Estimates the TDP of the specified set of features.

Usage

setTDP(pvalue, featureIDs, data, set, alpha = 0.05)

Arguments

pvalue

The vector of p-values. It can be the name of the covariate representing the Vector ofraw p-values in thedata or a single vector but in the latter case it should match thefeatureIDs vector

featureIDs

The vector of feature IDs. It can be the name of the covariate representing the IDs in thedata or a single vector but in the latter case it should match thepvalue vector

data

Optional data frame or matrix containing the variables inpvalue andfeatureIDs

set

The selection of features defining the feature-set based on the thefeatureIDs.If missing, the set of all features is evaluated

alpha

The type I error allowed. The default is 0.05. NOTE: this shouls be consistent across the study

Value

A named vector including the lower bound and point estimate for the true discovery proportion (TDP)of the specified test for the feature-set is returned.

Author(s)

Mitra Ebrahimpoor

m.ebrahimpoor@lumc.nl

References

Examples

## Not run: set.seed(159)#generate random p-values with pseudo IDsm<- 100pvalues <- runif(m,0,1)^5featureIDs <- as.character(1:m)# perform a self-contained test for all featuressettest(pvalues, featureIDs, testype = "selfcontained")# estimate the proportion of true discoveries among all m featuressettdp(pvalues, featureIDs)# create a random pathway of size 60randset=as.character(c(sample(1:m, 60)))# estimate the proportion of true discoveries in a random set of size 50settdp(pvalues, featureIDs, set=randset)## End(Not run)

setTest

Description

calculates the adjusted p-value for the local hypothesis as defined bytesttypeandtestvalue.

Usage

setTest(pvalue, featureIDs, data, set, testype, testvalue)

Arguments

pvalue

The vector of p-values. It can be the name of the covariate representing the Vector ofraw p-values in thedata or a single vector but in the latter case it should match thefeatureIDs vector

featureIDs

The vector of feature IDs. It can be the name of the covariate representing the IDs in thedata or a single vector but in the latter case it should match thepvalue vector

data

Optional data frame or matrix containing the variables inpvalue andfeatureIDs

set

The selection of features defining the feature-set based on the thefeatureIDs.If missing, the set of all features is selected

testype

Character, type of the test: "selfcontained" or "competitive". Choosing the self-containedoption will automatically set the threshold to zero and thetestvalue is ignored. Choosing thecompetitive option without atestvalue will set the threshold to the overall estimated proportionof true hypotheses

testvalue

Optional value to test against. Setting this value to c along withtestype=="competitive" will lead to testing the null hypothesis against a threshold c.Note: this value needs to be a proportion

Value

The adjusted p-value of the specified test for the feature-set is returned.

Author(s)

Mitra Ebrahimpoor

m.ebrahimpoor@lumc.nl

References

Examples

## Not run: #Generate a vector of pvaluesset.seed(159)m<- 100pvalues <- runif(m,0,1)^5featureIDs <- as.character(1:m)# perform a self-contained test for all featuressettest(pvalues, featureIDs, testype = "selfcontained")# create a random pathway of size 60randset=as.character(c(sample(1:m, 60)))# perform a competitive test for the random pathwaysettest(pvalues, featureIDs, set=randset, testype = "competitive")# perform a unified null hypothesis test against 0.2 for a set of size 50settest(pvalues, featureIDs, set=randset, testype = "competitive", testvalue = 0.2 )## End(Not run)

topSEA

Description

returns a permutation of SEA-chart which rearrangesthe feature-sets according to the selected argument into ascending ordescending order.

Usage

topSEA(object, by, thresh = NULL, descending = TRUE, n = 20, cover)

Arguments

object

A SEA-chart object which is the output ofSEA function

by

Variable name by which the ordering should happen. It should be a column of SEA-chart.The default is TDP_bound.

thresh

A real number between 0 and 1. If specified the values of the variable defined inbywill be threshold accordingly.

descending

Logical. IfTRUE The output chart is organized in a descending order

n

Integer. Number of raws of the output chart

cover

An optional threshold for coverage, which must be a real number between 0 and 1.If specified, feature-sets with a coverage lower than or equal to this value are removed.

Value

Returns a subset of SEA_chart sorted according to the arguments

Author(s)

Mitra Ebrahimpoor

m.ebrahimpoor@lumc.nl

References

Examples

#See the examples for \code{\link{SEA}}

Movatterモバイル変換

Simultaneous Enrichment Analysis (SEA) of all possible feature-sets using the unified null hypothesis

Description

Details

Author(s)

References

SEA

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

topSEA

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

setTDP

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

setTest

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

topSEA

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples