Type:	Package
Title:	Competitive Gene Sets Test with the Mann-Whitney-Wilcoxon Test
Version:	1.2.4
Date:	2025-03-25
Maintainer:	Stefano Maria Pagnotta <pagnotta@unisannio.it>
Description:	Friendly implementation of the Mann-Whitney-Wilcoxon test for competitive gene set enrichment analysis.
Depends:	R (≥ 4.4), formattable (≥ 0.2.1), WriteXLS (≥ 6.7.0), igraph (≥ 2.1.4), visNetwork (≥ 2.1.2)
Suggests:	knitr, rmarkdown
License:	GPL (≥ 3)
URL:	<https://github.com/stefanoMP/massiveGST>, <http://www.massivegenesetstest.org/>
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-03-28 12:18:03 UTC; stefano
Author:	Stefano Maria Pagnotta [aut, cre, cph]
Repository:	CRAN
Date/Publication:	2025-03-28 12:50:01 UTC

Trim the table of results.

Description

This function trims the table of results from massiveGST function retaining the rows with a NES below the specified threshold.

Usage

cut_by_NES(ttable, NES_threshold = 0.6)

Arguments

Value

A data frame.

Note

the functions cut_by_NES, cut_by_logit2NES, and cut_by_significance can be nested. In the case the test has alternative = 'two.sided', it is better to use cut_by_logit2NES for a symmetric trim of both directions.

Author(s)

Stefano M. Pagnotta

References

Cerulo, Pagnotta (2022) doi:10.3390/e24050739

See Also

massiveGST, cut_by_logit2NES, cut_by_significance, summary.mGST, plot.mGST

Examples

library(massiveGST)

# get the gene profile
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "pre_ranked_list.txt")
geneProfile <- get_geneProfile(fname)

# get the gene-sets
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "h.all.v2024.1.Hs.symbols.gmt")
geneSets <- get_geneSets_from_local_files(fname)

# run the function
ans <- massiveGST(geneProfile, geneSets, alternative = "greater")

head(ans)
cut_by_NES(ans, NES_threshold = .65)
summary(cut_by_NES(ans, NES_threshold = .65))

Trim the table of results.

Description

This function trims the table of results from massiveGST function retaining the rows with a logit2NES below the specified threshold.

Usage

cut_by_logit2NES(ttable, logit2NES_threshold = 0.58)

Arguments

Value

A data frame.

Note

the functions cut_by_NES, cut_by_logit2NES, and cut_by_significance can be nested.

Author(s)

Stefano M. Pagnotta

References

Cerulo, Pagnotta (2022) doi:10.3390/e24050739

See Also

massiveGST, cut_by_NES, cut_by_significance,

summary.mGST, plot.mGST

Examples

library(massiveGST)

# get the gene profile
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "pre_ranked_list.txt")
geneProfile <- get_geneProfile(fname)

# get the gene-sets
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "h.all.v2024.1.Hs.symbols.gmt")
geneSets <- get_geneSets_from_local_files(fname)

# run the function
ans <- massiveGST(geneProfile, geneSets, alternative = "two.sided")

head(ans)

cut_by_logit2NES(ans)
cut_by_logit2NES(cut_by_significance(ans))

plot(cut_by_logit2NES(ans))

Trim the table of results.

Description

This function trims the table of results from massiveGST function according to the significance required.

Usage

cut_by_significance(ttable, 
  level_of_significance = 0.05, 
  where = c("BH.value", "bonferroni", "p.value")
)

Arguments

Details

BH.value is the adjustment of p-values according to Benijamini and Hockberg's method; B.value is the adjustment of p-values according to Bonferroni's method.

Value

A data frame.

Note

the functions cut_by_NES, cut_by_logit2NES, and cut_by_significance can be nested.

Author(s)

Stefano M. Pagnotta

References

Cerulo, Pagnotta (2022) doi:10.3390/e24050739

See Also

massiveGST, cut_by_logit2NES, cut_by_NES, summary.mGST, plot.mGST

Examples

library(massiveGST)

# get the gene profile
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "pre_ranked_list.txt")
geneProfile <- get_geneProfile(fname)

# get the gene-sets
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "h.all.v2024.1.Hs.symbols.gmt")
geneSets <- get_geneSets_from_local_files(fname)
# run the function
ans <- massiveGST(geneProfile, geneSets, alternative = "two.sided")

head(ans)
cut_by_significance(ans)

cut_by_significance(ans, level_of_significance = 0.05, where = "p")
cut_by_logit2NES(cut_by_significance(ans))

summary(cut_by_significance(ans, level_of_significance = 0.05, where = "bonferroni"))

plot(cut_by_significance(ans, level_of_significance = 0.05, where = "bonferroni"))

Compute the similarities between a collection of gene sets.

Description

Compute the similarities between a collection of gene sets using a convex function of the Jaccard and overlap indeces.

Usage

geneSets.sim(gs, eps = 0.25)

Arguments

Details

The similarity between the gene-set is computed a convex combination of the Jaccard and overlap similarities. See the reference for further details.

Value

returns an object of class "dist", where the values are the similaties between gene sets.

Author(s)

Stefano M. Pagnotta

References

Cerulo, Pagnotta (2022) doi:10.3390/e24050739

See Also

dist

Examples

library(massiveGST)

# get the gene-sets
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "h.all.v2024.1.Hs.symbols.gmt")
geneSets <- get_geneSets_from_local_files(fname)[1:5]

# compute the similarities
geneSets.sim(geneSets)
ssim <- geneSets.sim(geneSets)
ssim <- as.matrix(ssim)
diag(ssim) <- 1
ssim

Load a gene-profile from a txt file.

Description

Load a gene-profile from a txt file.

Usage

get_geneProfile(ffile)

Arguments

Details

The txt file contains two columuns separated by a tabulation. The first column is the gene name ( or entrez, ensembl, etc); the second column are the numeric values associated with each gene. The profile do not need to be sorted.

As an example, see the file in /massiveGST/extdata/pre_ranked_list.txt

See the path in the example below.

Value

A named list of numeric values.

Author(s)

Stefano M. Pagnotta

See Also

pre_ranked_list

Examples

fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "pre_ranked_list.txt")
fname
geneProfile <- get_geneProfile(fname)
class(geneProfile)
head(geneProfile)
tail(geneProfile)

Load the gene-sets collection from local gmt files

Description

Load the gene-sets collection from local gmt files

Usage

get_geneSets_from_local_files(ffiles)

Arguments

Value

A vector list of gene-sets

Author(s)

Stefano M. Pagnotta

See Also

write_geneSets_to_gmt

Examples

library(massiveGST)

# getting one collection
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "h.all.v2024.1.Hs.symbols.gmt")
length(geneSets <- get_geneSets_from_local_files(fname))
head(geneSets)

# getting two or more collections
geneSets <- get_geneSets_from_local_files(c(fname, fname))
length(geneSets)

hallmark gene sets

Description

This is the hallmark gene sets collection from the Molecular Signatures Databas.

Author(s)

Stefano M. Pagnotta

References

Liberzon et al. "The Molecular Signatures Database (MSigDB) hallmark gene set collection" Bioinformatics, Volume 27, Issue 12, June 2011, Pages 1739-1740 doi:10.1093/bioinformatics/btr260

massive Gene-Sets Test with Mann-Whitney-Wilcoxon statistics.

Description

Perform a competitive gene set enrichment analysis by applying the Mann-Withney-Wilcoxon test.

Usage

massiveGST(gene_profile, gene_sets, 
  cols_to_remove = NULL, 
  alternative = c("two.sided", "less", "greater")
  )

Arguments

Value

A data frame with columns

Author(s)

Stefano M. Pagnotta

References

Cerulo, Pagnotta (2022) doi:10.3390/e24050739

See Also

summary.mGST, plot.mGST, cut_by_logit2NES, cut_by_NES, cut_by_significance, hallmark gene sets

Examples

library(massiveGST)

# get the gene profile
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "pre_ranked_list.txt")
geneProfile <- get_geneProfile(fname)

# get the gene-sets
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "h.all.v2024.1.Hs.symbols.gmt")
geneSets <- get_geneSets_from_local_files(fname)

# run the function
ans <- massiveGST(geneProfile, geneSets, alternative = "two.sided")

ans

A wrapper to fisher.test to get over representation analysis of gene sets.

Description

The function massiveORT essentially is a wrapper to the function fisher.test in charge to 1) arrange the input to feed fisher.test in sequence for each gene set, 2) arrange the output in a data frame compatible with the other function of the package, and 3) compute the universe of genes for the analysis.

Usage

massiveORT(gene_list, gene_sets, universe = NULL, 
          alternative = c("greater", "less", "two.sided"))

Arguments

Details

This function allows to define externally or compute the universe of reference of the analysis. By default (universe = NULL), the universe is computed starting from the gene names contributing at least once in each gene set.

Value

A data frame with columns

Author(s)

Stefano M. Pagnotta

References

Cerulo, Pagnotta (2022) doi:10.3390/e24050739

See Also

fisher.test, cut_by_significance, hallmark gene sets

Examples

library(massiveGST)

# get the gene profile
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "pre_ranked_list.txt")
geneProfile <- get_geneProfile(fname)
geneList <- names(head(geneProfile, 1000))

# get the gene-sets
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "h.all.v2024.1.Hs.symbols.gmt")
geneSets <- get_geneSets_from_local_files(fname)

# run the function
ans <- massiveORT(geneList, geneSets)
cut_by_significance(ans)

plot(ans, geneSets, as.network = TRUE)

Graphical rendering of the enrichment analysis.

Description

This function displays the enrichment analysis results both as a bar-plot and a network of gene-sets.

Usage

## S3 method for class 'mGST'
plot(x, 
  gene_sets = NULL, 
  order_by = "logit2NES", 
  top = 30, 
  eps = 0.25, 
  as.network = FALSE, 
  similarity_threshold = 1/3, 
  manipulation = FALSE, 
  autoResize = TRUE, 
  ...
)

Arguments

Details

This function display the results of enrichment analysis both as a bar-plot and a network.

The network rendering is with the visNetwork package.

The similarity between the gene-set is computed a convex combination of the Jaccard and overlap similarities. See the reference for further details.

Value

In the case of network display, an object from the visNetwork package.

Author(s)

Stefano M. Pagnotta

References

Cerulo, Pagnotta (2022) doi:10.3390/e24050739

See Also

massiveGST, visNetwork::visNetwork(), visNetwork::visOptions(), hallmark gene sets

Examples

library(massiveGST)

# get the gene profile
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "pre_ranked_list.txt")
geneProfile <- get_geneProfile(fname)

# get the gene-sets
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "h.all.v2024.1.Hs.symbols.gmt")
geneSets <- get_geneSets_from_local_files(fname)

# run the function
ans <- massiveGST(geneProfile, geneSets, alternative = "two.sided")

# to get the bar-plot
plot(cut_by_significance(ans, level_of_significance = 0.1))

# to get the network of the gene-sets
plot(cut_by_significance(ans, level_of_significance = 0.1), 
     gene_sets = geneSets, as.network = TRUE)

FGFR3-TACC3 fusion positive gene profile

Description

This gene-profile comes from the paper in reference. It compares 9 FGFR3-TACC3 fusion positive samples versus 535 other samples in the GBM study from TCGA (Agilent platform).

Author(s)

Stefano M. Pagnotta

References

Frattini et al. "A metabolic function of FGFR3-TACC3 gene fusions in cancer" Nature volume 553, 2018 doi:10.1038/nature25171

Save the results in tab-separeted value file

Description

Save the data frame coming from the massiveGST function as tab-separeted value.

Usage

save_as_tsv(x, file_name = "massiveGST.tsv", sep = "\t", ...)

Arguments

Value

No return value.

Author(s)

Stefano M. Pagnotta

See Also

massiveGST

Examples

library(massiveGST)

# get the gene profile
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "pre_ranked_list.txt")
geneProfile <- get_geneProfile(fname)

# get the gene-sets
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "h.all.v2024.1.Hs.symbols.gmt")
geneSets <- get_geneSets_from_local_files(fname)

# run the function
ans <- massiveGST(geneProfile, geneSets, alternative = "two.sided")

# save the results
fname <- file.path(tempdir(), "massiveGST_results.tsv")
save_as_tsv(ans, file_name = fname)

Save the results in xls file format

Description

Save the data frame coming from the massiveGST function as Excel 2003 (XLS) or Excel 2007 (XLSX) files

Usage

save_as_xls(x, file_name = "massiveGST.xls", ...)

Arguments

Value

No return value.

Author(s)

Stefano M. Pagnotta

See Also

WriteXLS::WriteXLS(), massiveGST

Examples

library(massiveGST)

# get the gene profile
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "pre_ranked_list.txt")
geneProfile <- get_geneProfile(fname)

# get the gene-sets
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "h.all.v2024.1.Hs.symbols.gmt")
geneSets <- get_geneSets_from_local_files(fname)

# run the function
ans <- massiveGST(geneProfile, geneSets, alternative = "two.sided")

# save the results
fname <- file.path(tempdir(), "massiveGST_results.xls")
save_as_xls(ans, file_name = fname)

Generate summary tables

Description

This method handles the result of massiveGST function, to provide views of the table.

Usage

## S3 method for class 'mGST'
summary(object, 
  cols_to_remove = "link", 
  order_by = c("relevance", "NES", "logit2NES", "p.value", "BH.value", "bonferroni"), 
  top = NULL, 
  as.formattable = FALSE, 
  ...
)

Arguments

Value

A data frame.

Author(s)

Stefano M. Pagnotta

See Also

massiveGST

Examples

library(massiveGST)

# get the gene profile
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "pre_ranked_list.txt")
geneProfile <- get_geneProfile(fname)

# get the gene-sets
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "h.all.v2024.1.Hs.symbols.gmt")
geneSets <- get_geneSets_from_local_files(fname)

# run the function
ans <- massiveGST(geneProfile, geneSets, alternative = "two.sided")

summary(ans)
summary(ans, as.formattable = TRUE, order_by = "NES", top = 10)

Save a collection of gene-sets in a .gmt file format.

Description

Write a collection of gene sets as arranged in this package in a gmt file format.

Usage

write_geneSets_to_gmt(gs, fileName)

Arguments

Value

No return value.

Author(s)

Stefano M. Pagnotta

See Also

get_geneSets_from_local_files

Examples

library(massiveGST)

# get the gene-sets
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "h.all.v2024.1.Hs.symbols.gmt")
geneSets <- get_geneSets_from_local_files(fname)

# save the gene-sets
fname <- file.path(tempdir(), "hallmarks.gmt")
write_geneSets_to_gmt(geneSets, fileName = fname)