Package 'GSABenchmark'

Title: Tools for benchmarking single-cell gene set analysis methods
Description: GSABenchmark is a package designed for benchmarking scRNA-seq gene set analysis (scGSA) methods. It provides both traditional and novel benchmark metrics, as well as visualization tools. Currently, GSABenchmark supports 17 scGSA methods.
Authors: Andrei-Florian Stoica [aut, cre] (ORCID: <https://orcid.org/0000-0002-5253-0826>)
Maintainer: Andrei-Florian Stoica <[email protected]>
License: MIT + file LICENSE
Version: 1.1.0
Built: 2026-05-30 09:18:05 UTC
Source: https://github.com/bioc/GSABenchmark

Help Index

Create an aggregate rank plot from a summary object

Description

This function creates an aggregate rank plot from a summary object.

Usage

aggregateRankPlot(
  smr,
  title = NULL,
  xLab = "Method",
  sigDigits = 2,
  rankMethod = c("min", "average", "first", "last", "random", "max"),
  ...
)

Arguments

smr

Complete summary list generated with allBenchmarkResults or runBenchmark.
title

Plot title.
xLab

x axis label.
sigDigits

Number of significant digits used when displaying mean ranks. If NULL, the mean ranks will not be displayed.
rankMethod

Rank method. Choose between 'min', 'average', 'first', 'last', 'random' and 'max'. Default in 'min'.
...

Additional parameters passed to other functions.

Value

A ggplot object.

Examples

sPath <- system.file('extdata', 'smr.qs2', package='GSABenchmark')
smr <- qs2::qs_read(sPath)
p <- aggregateRankPlot(smr)

Plot the complete list of benchmark summaries

Description

This function plots the complete list of benchmark summaries.

Usage

allBenchmarkPlots(smr, titleSuffix = NULL, ...)

Arguments

smr

Complete summary list generated with allBenchmarkResults or runBenchmark.
titleSuffix

Suffix used to create plot titles If NULL, the plots will have no titles.
...

Additional parameters passed to other functions.

Value

A list of ggplot objects.

Examples

sPath <- system.file('extdata', 'smr.qs2', package='GSABenchmark')
smr <- qs2::qs_read(sPath)
plots <- allBenchmarkPlots(smr)

Generate all benchmark results with some precomputed arguments. This function generates all benchmark results by using precomputed values for normSilDF, dimMat and maxDist.

Description

Generate all benchmark results with some precomputed arguments. This function generates all benchmark results by using precomputed values for normSilDF, dimMat and maxDist.

Usage

allBenchmarkResults(
  scObj,
  labelCol,
  geneSets,
  gsaMethods,
  checkLabels = TRUE,
  normSilDF = NULL,
  dimMat = NULL,
  maxDist = NULL,
  efBenchmark = NULL,
  runEFBenchmark = TRUE,
  verbose = TRUE
)

Arguments

scObj

A Seurat or SingleCellExperiment object.
labelCol

The metadata column containing the ground truth annotation.
geneSets

A list of gene sets. If not NULL while efBenchmark is NULL, the efficiency benchmark will be run.
gsaMethods

Character vector of gene set analysis methods.
checkLabels

Whether to check that geneSetNames is a subset of the values in labelCol.
normSilDF

Data frame of normalized silhouettes.
dimMat

UMAP dimensionality reduction matrix of the single-cell expression object. Unused if normSilDF is NULL.
maxDist

Maximum UMAP distance in the single-cell expression object. Unused if normSilDF or dimMat is NULL.
efBenchmark

A list of data frames generated with efficiencyBenchmark.
runEFBenchmark

Whether to run efficiency benchmark.
verbose

Whether the output of the efficiency benchmark should be verbose. Ignored if runEFBenchmark is FALSE.

Details

This function is designed to save some computational time when multiple runs are sequentially performed for the same single-cell expression object and cell identity classes, but different choices of gene sets (so long as they still describe the same classes) or methods. If a single run is planned rather than multiple ones, runBenchmark is a more straightforward choice, as it takes care of generating normSilDF and dimMat.

Value

A list of benchmark results.

Examples

scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
scObj <- qs2::qs_read(scoPath)
gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
geneSets <- qs2::qs_read(gsPath)
scObj <- hammers::computeSilhouette(scObj, 'label')
normSilDF <- hammers::normalizeSilhouette(scObj, 'label')
dimMat <- scLang::scPCAMat(scObj)
maxDist <- max(dist(dimMat))
smr <- allBenchmarkResults(scObj, 'label', geneSets, c('CSOA', 'Zscore'),
TRUE, normSilDF, dimMat, maxDist, runEFBenchmark=FALSE)

Plot a list of summary data frames

Description

This function plots a list of summary data frames with methods as rows, gene sets and the average of scores across all gene sets as columns.

Usage

benchmarkPlots(smr, titleSuffix = NULL, lastPlotLegendLab = "Metric", ...)

Arguments

smr

List of summary data frames, whether boundary, MCC or global.
titleSuffix

Suffix used to create plot titles If NULL, the plots will have no titles.
lastPlotLegendLab

The legend label of the last plot.
...

Additional arguments passed to scorePlot.

Value

A list of ggplot objects.

Examples

sPath <- system.file('extdata', 'smr.qs2', package='GSABenchmark')
smr <- qs2::qs_read(sPath)
plots <- benchmarkPlots(smr[[1]])

Create correlation plots for method results

Description

This function creates correlation plots for method results.

Usage

corrPlots(
  scObj,
  smr,
  titlePrefix = "Correlation plot",
  titleInfixes = NULL,
  titleSuffix = NULL,
  ...
)

Arguments

scObj

A Seurat or SingleCellExperiment object.
smr

Complete summary list generated with allBenchmarkResults or runBenchmark.
titlePrefix

Prefix used to create a title with subplotTitle. Ignored if titleSuffix is NULL.
titleInfixes

A character vector of title infixes used to create a title with subplotTitle. Ignored if titleSuffix is NULL.
titleSuffix

Suffix used to create plot titles If NULL, the plots will have no titles.
...

Additional arguments passed to henna::correlationPlot.

Value

A named list of ggplot objects.

Examples

scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
scObj <- qs2::qs_read(scoPath)
sPath <- system.file('extdata', 'smr.qs2', package='GSABenchmark')
smr <- qs2::qs_read(sPath)
plots <- corrPlots(scObj, smr)

Calculate correlation matrix for method results

Description

This function calculates the correlation matrix for all the methods for an identity class.

Usage

corrSummary(scObj, smr, corMethod = "spearman")

Arguments

scObj

A Seurat or SingleCellExperiment object.
smr

List containing boundary, MCC and global lists of data frames.
corMethod

Correlation method.

Value

A list of correlation matrices.

Examples

scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
scObj <- qs2::qs_read(scoPath)
sPath <- system.file('extdata', 'smr.qs2', package='GSABenchmark')
smr <- qs2::qs_read(sPath)
m <- corrSummary(scObj, smr)

Extract running times and peak memory usage for gene set analysis methods

Description

This function extracts running times and peak memory usage for gene set analysis methods.

Usage

efficiencyBenchmark(
  scObj,
  labelCol,
  geneSets,
  gsaMethods,
  checkLabels = TRUE,
  verbose = TRUE
)

Arguments

scObj

A Seurat or SingleCellExperiment object.
labelCol

The metadata column containing the ground truth annotation.
geneSets

A list of gene sets. If not NULL while efBenchmark is NULL, the efficiency benchmark will be run.
gsaMethods

Character vector of gene set analysis methods.
checkLabels

Whether to check that geneSetNames is a subset of the values in labelCol.
verbose

Whether output should be verbose.

Value

A list of two data frames, the first comprising running times in seconds, the second comprising peak memory usage in mebibytes.

Examples

scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
scObj <- qs2::qs_read(scoPath)
gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
geneSets <- qs2::qs_read(gsPath)
ef <- efficiencyBenchmark(scObj, 'label', geneSets[1], 'CSOA',
verbose=FALSE)

Create gene set rank plots for the method results

Description

This function creates gene set rank plots for method results.

Usage

geneSetRankPlots(
  smr,
  rankMethod = "min",
  titlePrefix = "Distribution of ranks",
  titleInfixes = NULL,
  titleSuffix = NULL,
  xLab = "Method",
  ...
)

Arguments

smr

Complete summary list generated with allBenchmarkResults or runBenchmark.
rankMethod

Rank method. Choose between 'min', 'average', 'first', 'last', 'random' and 'max'. Default in 'min'.
titlePrefix

Prefix used to create a title with subplotTitle. Ignored if titleSuffix is NULL.
titleInfixes

A character vector of title infixes used to create a title with subplotTitle. Ignored if titleSuffix is NULL.
titleSuffix

Suffix used to create plot titles If NULL, the plots will have no titles.
xLab

x axis label.
...

Additional arguments passed to henna::rankPlot.

Value

A named list of ggplot objects.

Examples

sPath <- system.file('extdata', 'smr.qs2', package='GSABenchmark')
smr <- qs2::qs_read(sPath)
plots <- geneSetRankPlots(smr)

Create MDS plots for method results

Description

This function creates MDS plots for method results.

Usage

mdsPlots(
  scObj,
  smr,
  titlePrefix = "MDS plot",
  titleInfixes = NULL,
  titleSuffix = NULL,
  drawScores = TRUE,
  ...
)

Arguments

scObj

A Seurat or SingleCellExperiment object.
smr

Complete summary list generated with allBenchmarkResults or runBenchmark.
titlePrefix

Prefix used to create a title with subplotTitle. Ignored if titleSuffix is NULL.
titleInfixes

A character vector of title infixes used to create a title with subplotTitle. Ignored if titleSuffix is NULL.
titleSuffix

Suffix used to create plot titles If NULL, the plots will have no titles.
drawScores

Whether to render scores on the plot. If set to TRUE, the third column of the input data frame will be numeric and scores will be taken from there.
...

Additional arguments passed to henna::densityPlot.

Value

A named list of ggplot objects.

Examples

scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
scObj <- qs2::qs_read(scoPath)
sPath <- system.file('extdata', 'smr.qs2', package='GSABenchmark')
smr <- qs2::qs_read(sPath)
plots <- mdsPlots(scObj, smr)

Plot a data frame consisting of gene set analysis method peak memory usage This function plots data frame consisting of method peak memory usages with methods as rows, gene sets and the gene set average as columns.

Description

Plot a data frame consisting of gene set analysis method peak memory usage This function plots data frame consisting of method peak memory usages with methods as rows, gene sets and the gene set average as columns.

Usage

memoryPlot(efBenchmark, title = NULL, xLab = "Peak memory usage (MiB)", ...)

Arguments

efBenchmark

A list of dataframes generated with efficiencyBenchmark.
title

Plot title.
xLab

x axis label.
...

Additional parameters to be passed to scorePlot.

Value

A ggplot object.

Examples

sPath <- system.file('extdata', 'smr.qs2', package='GSABenchmark')
smr <- qs2::qs_read(sPath)
memoryPlot(smr[[5]])

Create metric rank plots for the method results

Description

This function creates metric rank plots for method results.

Usage

metricRankPlots(
  smr,
  titlePrefix = "Distribution of ranks",
  titleInfixes = metricNames(),
  titleSuffix = NULL,
  rankMethod = "min",
  xLab = "Method",
  ...
)

Arguments

smr

Complete summary list generated with allBenchmarkResults or runBenchmark.
titlePrefix

Prefix used to create a title with subplotTitle. Ignored if titleSuffix is NULL.
titleInfixes

A character vector of title infixes used to create a title with subplotTitle. Ignored if titleSuffix is NULL.
titleSuffix

Suffix used to create plot titles If NULL, the plots will have no titles.
rankMethod

Rank method. Choose between 'min', 'average', 'first', 'last', 'random' and 'max'. Default in 'min'.
xLab

x axis label.
...

Additional arguments passed to henna::rankPlot.

Value

A named list of ggplot objects.

Examples

sPath <- system.file('extdata', 'smr.qs2', package='GSABenchmark')
smr <- qs2::qs_read(sPath)
plots <- metricRankPlots(smr)

Create Jaccard tile plots for method binary predictions

Description

This function creates Jaccard tile plots for method binary predictions.

Usage

predJaccardPlots(
  predictionsSmr,
  titlePrefix = "Binary prediction Jaccard plot",
  titleInfixes = NULL,
  titleSuffix = NULL,
  ...
)

Arguments

predictionsSmr

Binary predictions summary.
titlePrefix

Prefix used to create a title with subplotTitle. Ignored if titleSuffix is NULL.
titleInfixes

A character vector of title infixes used to create a title with subplotTitle. Ignored if titleSuffix is NULL.
titleSuffix

Suffix used to create plot titles If NULL, the plots will have no titles.
...

Additional arguments passed to henna::tilePlot.

Value

A named list of ggplot objects.

Examples

sPath <- system.file('extdata', 'smr.qs2', package='GSABenchmark')
smr <- qs2::qs_read(sPath)
plots <- predJaccardPlots(smr$predictions)

Create a ratio rank plot for the method results

Description

This function creates a ratio rank plot for the method results.

Usage

ratioPlot(
  smr,
  title = NULL,
  nItems = 25,
  xLab = "Maximum over mean ratio",
  yLab = "Metric",
  legendLab = "Method",
  ...
)

Arguments

smr

Complete summary list generated with allBenchmarkResults or runBenchmark.
title

Plot title.
nItems

Number of retained iterms.
xLab

x axis label.
yLab

y axis label.
legendLab

Legend label.
...

Additional arguments passed to henna::classRank.

Value

A ggplot object.

Examples

sPath <- system.file('extdata', 'smr.qs2', package='GSABenchmark')
smr <- qs2::qs_read(sPath)
p <- ratioPlot(smr)

Run AddModuleScore

Description

This function runs AddModuleScore.

Usage

runAddModuleScore(
  scObj,
  geneSets,
  slot = "data",
  pool = rownames(scObj),
  nbin = 24,
  ctrl = 100,
  seed = 1
)

Arguments

scObj

A Seurat or SingleCellExperiment object.
geneSets

A named list of gene sets.
slot

Gene expression slot.
pool

The set from which features to be compared with signature genes are selected. Defaults to all features.
nbin

Number of bins of aggregate expression levels for pool features.
ctrl

Number of control features chosen from the same bin for each feature.
seed

Random seed.

Details

Reimplemented from https://github.com/satijalab.

Value

A single-cell expression object with the results saved as a metadata column.

Examples

scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
scObj <- qs2::qs_read(scoPath)
gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
geneSets <- qs2::qs_read(gsPath)
scObj <- runAddModuleScore(scObj, geneSets)

Run AUCell

Description

This function runs AUCell using escape.

Usage

runAUCell(scObj, geneSets, slot = "data", ...)

Arguments

scObj

A Seurat or SingleCellExperiment object.
geneSets

A named list of gene sets.
slot

Gene expression slot.
...

Additional parameters passed to runEscapeMethod.

Value

A single-cell expression object with the results saved as a metadata column.

Examples

if (requireNamespace("AUCell", quietly=TRUE)){
    scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
    scObj <- qs2::qs_read(scoPath)
    gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
    geneSets <- qs2::qs_read(gsPath)
    scObj <- runAUCell(scObj, geneSets)
    }

Generate all benchmark results This function performs the entire GSABenchmark pipeline.

Description

Generate all benchmark results This function performs the entire GSABenchmark pipeline.

Usage

runBenchmark(
  scObj,
  labelCol,
  geneSets,
  gsaMethods,
  runEFBenchmark = TRUE,
  verbose = TRUE
)

Arguments

scObj

A Seurat or SingleCellExperiment object.
labelCol

The metadata column containing the ground truth annotation.
geneSets

A list of gene sets. If not NULL while efBenchmark is NULL, the efficiency benchmark will be run.
gsaMethods

Character vector of gene set analysis methods.
runEFBenchmark

Whether to run efficiency benchmark.
verbose

Whether the output of the efficiency benchmark should be verbose. Ignored if runEFBenchmark is FALSE.

Details

A wrapper around allBenchmarkResults. Slower for repeated runs, but it does not require users to manually generate normSilDF and dimMat.

Value

A list of benchmark results.

Examples

scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
scObj <- qs2::qs_read(scoPath)
gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
geneSets <- qs2::qs_read(gsPath)
smr <- runBenchmark(scObj, 'label', geneSets, c('CSOA', 'Zscore'), FALSE)

Generate all benchmark results for shuffled gene sets This function generates all benchmark results for shuffled gene sets.

Description

Generate all benchmark results for shuffled gene sets This function generates all benchmark results for shuffled gene sets.

Usage

runBenchmarkShuffle(
  scObj,
  labelCol,
  geneSets,
  gsaMethod,
  runEFBenchmark = TRUE
)

Arguments

scObj

A Seurat or SingleCellExperiment object.
labelCol

The metadata column containing the ground truth annotation.
geneSets

A list of gene sets. If not NULL while efBenchmark is NULL, the efficiency benchmark will be run.
gsaMethod

Name of the gene set analysis method.
runEFBenchmark

Whether to run efficiency benchmark.

Value

A list of benchmark results.

Examples

scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
scObj <- qs2::qs_read(scoPath)
gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
geneSets <- qs2::qs_read(gsPath)
scObj <- runMethodShuffle(scObj, 'label', geneSets, 'CSOA', 0.2, 0.2)
smr <- runBenchmarkShuffle(scObj, 'label', geneSets, 'CSOA', FALSE)

Run gene set analysis methods

Description

This function runs the gene set analysis methods.

Usage

runGSAMethods(
  scObj,
  labelCol,
  geneSets,
  gsaMethods,
  infix = NULL,
  outputFun = silently_run
)

Arguments

scObj

A Seurat or SingleCellExperiment object.
labelCol

The metadata column containing the ground truth annotation.
geneSets

A named list of gene sets.
gsaMethods

Character vector of gene set analysis methods.
infix

Infix to add between method name and gene set name in single-cell expression object. The string consisting of the method name and the infix is separated by the gene set name with a '_' character.
outputFun

Choose between silently_run (suppress all warnings and messages) or identity (do not suppress them). Default is silently_run.

Value

A Seurat or SingleCellExpression object with the results of the runs stored as metadata columns.

Examples

scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
scObj <- qs2::qs_read(scoPath)
gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
geneSets <- qs2::qs_read(gsPath)
scObj <- runGSAMethods(scObj, 'label', geneSets, c('CSOA', 'Zscore'))

Run GSVA

Description

This function runs GSVA using GSVA.

Usage

runGSVA(scObj, geneSets, slot = "data", ...)

Arguments

scObj

A Seurat or SingleCellExperiment object.
geneSets

A named list of gene sets.
slot

Gene expression slot.
...

Additional parameters passed to runGSVAMethod.

Value

A single-cell expression object with the results saved as a metadata column.

Examples

scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
scObj <- qs2::qs_read(scoPath)
gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
geneSets <- qs2::qs_read(gsPath)
scObj <- runGSVA(scObj, geneSets)

Run JASMINE

Description

This function runs JASMINE.

Usage

runJASMINE(
  scObj,
  geneSets,
  slot = "data",
  method = c("oddsratio", "likelihood")
)

Arguments

scObj

A Seurat or SingleCellExperiment object.
geneSets

A named list of gene sets.
slot

Gene expression slot.
method

One of 'oddsratio' and 'likelihood'.

Details

Reimplemented from https://github.com/NNoureen/JASMINE. The method's paper can be found at https://doi.org/10.7554/eLife.71994.

Value

A single-cell expression object with the results saved as a metadata column.

Examples

scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
scObj <- qs2::qs_read(scoPath)
gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
geneSets <- qs2::qs_read(gsPath)
scObj <- runJASMINE(scObj, geneSets)

Run MDT using decoupleR

Description

This function runs MDT using decoupleR.

Usage

runMDT(scObj, geneSets, slot = "data", ...)

Arguments

scObj

A Seurat or SingleCellExperiment object.
geneSets

A named list of gene sets.
slot

Gene expression slot.
...

Additional parameters passed to runDecoupleRMethod.

Value

A single-cell expression object with the results saved as a metadata column.

Examples

if (requireNamespace("ranger", quietly=TRUE)){
    scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
    scObj <- qs2::qs_read(scoPath)
    gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
    geneSets <- qs2::qs_read(gsPath)
    scObj <- runMDT(scObj, geneSets)
}

Run gene set analysis method on shuffled gene sets

Description

This function runs a gene set analysis method on shuffled gene sets.

Usage

runMethodShuffle(
  scObj,
  labelCol,
  geneSets,
  gsaMethod,
  loss = c(0, 0.2),
  noise = c(0, 0.2),
  doGrid = TRUE,
  averageReplicates = TRUE,
  seeds = c(1, 2, 3),
  outputFun = identity
)

Arguments

scObj

A Seurat or SingleCellExperiment object.
labelCol

The metadata column containing the ground truth annotation.
geneSets

A named list of gene sets.
gsaMethod

Name of the gene set analysis method.
loss

A numeric vector of gene loss values. Must be in [0, 1).
noise

A numeric vector of noise values. Must be in [0, 1).
doGrid

Whether to run the methods for each loss-noise combination.
averageReplicates

Whether to average replicates. If TRUE, a single set of scores will be returned for each loss-noise combination for which the method is run. If FALSE, distinct sets of scores will be returned for each replicate.
seeds

A numeric vector of random seeds passed to hammers::shuffleGenes. Its length determines the number of replicates.
outputFun

Choose between silently_run (suppress all warnings and messages) or identity (do not suppress them). Default is silently_run.

Value

A Seurat or SingleCellExpression object with the results of the runs stored as metadata columns.

Examples

scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
scObj <- qs2::qs_read(scoPath)
gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
geneSets <- qs2::qs_read(gsPath)
scObj <- runMethodShuffle(scObj, 'label', geneSets, 'CSOA', 0.2, 0.2)

Run MLM using decoupleR

Description

This function runs MLM using decoupleR.

Usage

runMLM(scObj, geneSets, slot = "data", ...)

Arguments

scObj

A Seurat or SingleCellExperiment object.
geneSets

A named list of gene sets.
slot

Gene expression slot.
...

Additional parameters passed to runDecoupleRMethod.

Value

A single-cell expression object with the results saved as a metadata column.

Examples

scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
scObj <- qs2::qs_read(scoPath)
gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
geneSets <- qs2::qs_read(gsPath)
scObj <- runMLM(scObj, geneSets)

Run ORA using decoupleR

Description

This function runs ORA using decoupleR.

Usage

runORA(scObj, geneSets, slot = "data", ...)

Arguments

scObj

A Seurat or SingleCellExperiment object.
geneSets

A named list of gene sets.
slot

Gene expression slot.
...

Additional parameters passed to runDecoupleRMethod.

Value

A single-cell expression object with the results saved as a metadata column.

Examples

scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
scObj <- qs2::qs_read(scoPath)
gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
geneSets <- qs2::qs_read(gsPath)
scObj <- runORA(scObj, geneSets)

Run pagoda2

Description

This function runs pagoda2.

Usage

runPagoda2(scObj, geneSets, slot = "data", ...)

Arguments

scObj

A Seurat or SingleCellExperiment object.
geneSets

A named list of gene sets.
slot

Gene expression slot.
...

Additional arguments passed to pagoda2::score.cells.puram.

Value

A single-cell expression object with the results saved as a metadata column.

Examples

scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
scObj <- qs2::qs_read(scoPath)
gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
geneSets <- qs2::qs_read(gsPath)
scObj <- runPagoda2(scObj, geneSets)

Run PLAGE

Description

This function runs PLAGE using GSVA.

Usage

runPLAGE(scObj, geneSets, slot = "data", ...)

Arguments

scObj

A Seurat or SingleCellExperiment object.
geneSets

A named list of gene sets.
slot

Gene expression slot.
...

Additional parameters passed to runGSVAMethod.

Value

A single-cell expression object with the results saved as a metadata column.

Examples

scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
scObj <- qs2::qs_read(scoPath)
gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
geneSets <- qs2::qs_read(gsPath)
scObj <- runPLAGE(scObj, geneSets)

Run Singscore

Description

This function runs Singscore.

Usage

runSingscore(scObj, geneSets, slot = "data", ...)

Arguments

scObj

A Seurat or SingleCellExperiment object.
geneSets

A named list of gene sets.
slot

Gene expression slot.
...

Additional arguments passed to singscore::simpleScore.

Value

A single-cell expression object with the results saved as a metadata column.

Examples

scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
scObj <- qs2::qs_read(scoPath)
gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
geneSets <- qs2::qs_read(gsPath)
scObj <- runSingscore(scObj, geneSets)

Run SiPSiC

Description

This function runs SiPSiC.

Usage

runSiPSiC(scObj, geneSets, slot = "counts", ...)

Arguments

scObj

A Seurat or SingleCellExperiment object.
geneSets

A named list of gene sets.
slot

Gene expression slot.
...

Additional arguments passed to SiPSiC::getPathwayScores.

Value

A single-cell expression object with the results saved as a metadata column.

Examples

scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
scObj <- qs2::qs_read(scoPath)
gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
geneSets <- qs2::qs_read(gsPath)
scObj <- runSiPSiC(scObj, geneSets)

Run ssGSEA

Description

This function runs ssGSEA using escape.

Usage

runssGSEA(scObj, geneSets, slot = "data", ...)

Arguments

scObj

A Seurat or SingleCellExperiment object.
geneSets

A named list of gene sets.
slot

Gene expression slot.
...

Additional parameters passed to runEscapeMethod.

Value

A single-cell expression object with the results saved as a metadata column.

Examples

scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
scObj <- qs2::qs_read(scoPath)
gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
geneSets <- qs2::qs_read(gsPath)
scObj <- runssGSEA(scObj, geneSets)

Run UCell

Description

This function runs UCell using escape.

Usage

runUCell(scObj, geneSets, slot = "data", ...)

Arguments

scObj

A Seurat or SingleCellExperiment object.
geneSets

A named list of gene sets.
slot

Gene expression slot.
...

Additional parameters passed to runEscapeMethod.

Value

A single-cell expression object with the results saved as a metadata column.

Examples

if (requireNamespace("UCell", quietly=TRUE)){
    scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
    scObj <- qs2::qs_read(scoPath)
    gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
    geneSets <- qs2::qs_read(gsPath)
    scObj <- runUCell(scObj, geneSets)
    }

Run UDT using decoupleR

Description

This function runs UDT using decoupleR.

Usage

runUDT(scObj, geneSets, slot = "data", ...)

Arguments

scObj

A Seurat or SingleCellExperiment object.
geneSets

A named list of gene sets.
slot

Gene expression slot.
...

Additional parameters passed to runDecoupleRMethod.

Value

A single-cell expression object with the results saved as a metadata column.

Examples

if (requireNamespace("rpart", quietly=TRUE)){
    scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
    scObj <- qs2::qs_read(scoPath)
    gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
    geneSets <- qs2::qs_read(gsPath)
    scObj <- runUDT(scObj, geneSets)
    }

Run VAM

Description

This function runs VAM.

Usage

runVAM(scObj, geneSets, slot = "data", ...)

Arguments

scObj

A Seurat or SingleCellExperiment object.
geneSets

A named list of gene sets.
slot

Gene expression slot.
...

Additional arguments passed to VAM::vam.

Value

A single-cell expression object with the results saved as a metadata column.

Examples

scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
scObj <- qs2::qs_read(scoPath)
gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
geneSets <- qs2::qs_read(gsPath)
scObj <- runVAM(scObj, geneSets)

Run Zscore

Description

This function runs Zscore using GSVA.

Usage

runZscore(scObj, geneSets, slot = "data", ...)

Arguments

scObj

A Seurat or SingleCellExperiment object.
geneSets

A named list of gene sets.
slot

Gene expression slot.
...

Additional parameters passed to runGSVAMethod.

Value

A single-cell expression object with the results saved as a metadata column.

Examples

scoPath <- system.file('extdata', 'scObj.qs2', package='GSABenchmark')
scObj <- qs2::qs_read(scoPath)
gsPath <- system.file('extdata', 'geneSets.qs2', package='GSABenchmark')
geneSets <- qs2::qs_read(gsPath)
scObj <- runZscore(scObj, geneSets)

Plot a data frame consisting of gene set analysis method scores This function plots a data frame consisting of method scores with methods as rows, gene sets and the gene set average as columns.

Description

Plot a data frame consisting of gene set analysis method scores This function plots a data frame consisting of method scores with methods as rows, gene sets and the gene set average as columns.

Usage

scorePlot(
  scoreDF,
  title = NULL,
  xLab = "Score",
  yLab = "Method",
  isDecreasing = FALSE,
  palette = "grDevices::Plasma",
  legendLab = "Gene set",
  pointSize = 1.5
)

Arguments

scoreDF

A summary data frame.
title

Plot title.
xLab

x axis label.
yLab

y axis label.
isDecreasing

Logical; whether the methods should be displayed on the plot in decreasing order of the obtained average scores. If FALSE (as default), the methods will be displayed in increasing order of the average scores.
palette

Color palette.
legendLab

Legend label.
pointSize

Point size.

Value

A ggplot object.

Examples

sPath <- system.file('extdata', 'smr.qs2', package='GSABenchmark')
smr <- qs2::qs_read(sPath)
scorePlot(smr[[1]][[1]])

Show supported methods

Description

This function shows the methods currently supported by GSABenchmark.

Usage

supportedMethods()

Value

A character vector of supported methods.

Examples

supportedMethods()

Plot a data frame consisting of gene set analysis method running times This function plots data frame consisting of method running times with methods as rows, gene sets and the gene set average as columns.

Description

Plot a data frame consisting of gene set analysis method running times This function plots data frame consisting of method running times with methods as rows, gene sets and the gene set average as columns.

Usage

timePlot(efBenchmark, title = NULL, xLab = "Running time (s)", ...)

Arguments

efBenchmark

A list of dataframes generated with efficiencyBenchmark.
title

Plot title.
xLab

x axis label.
...

Additional parameters to be passed to scorePlot.

Value

A ggplot object.

Examples

sPath <- system.file('extdata', 'smr.qs2', package='GSABenchmark')
smr <- qs2::qs_read(sPath)
timePlot(smr[[5]])