cut_by_significance: Trim the table of results.

In massiveGST: Competitive Gene Sets Test with the Mann-Whitney-Wilcoxon Test

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

ttable	a data frame of "mGST" class coming from massiveGST function.
level_of_significance	a real value between 0.0 and 1.
where	a character string specifying where the level_of_significance has to be applied to the output; must be one of "p.value", "BH.value" (default), and "bonferroni"

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) \Sexpr[results=rd]{tools:::Rd_expr_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
geneSets <- get_geneSets_from_msigdbr(category = "H", what = "gene_symbol")

# 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"))

massiveGST documentation built on March 31, 2023, 8:59 p.m.