GSEA data

Enrich biological functions on significant candidate via a over representation analysis.

Usage

test_GSEA(
  x,
  type = c("GO", "KEGG", "REACTOME", "MSigDB"),
  species = "Human",
  contrasts = NULL,
  by_contrast = FALSE,
  topn = NULL,
  pAdjustMethod = "BH",
  category = NULL,
  subcategory = NULL,
  ...
)

Arguments

x

A SummarizedExperiment/DEGdata output from add_adjections or a charachter vector containing candidate identifier(SYMBOL, EntrezID, UniprotID or ENSEMBL).

type

Character, one of "GO","KEGG","REACTOME" and "MSigDB". The database for enrichment analysis.

species

The species name.

contrasts

Character, analyse results in which contrasts.

by_contrast

Logical(1). If true, draw enrichment on each contrast, else draw on the total significant candidates.

topn

Integer(1), only use topn list with most significant foldchange

pAdjustMethod

Character, one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none".

category, subcategory

Character. Work when type is "MSigDB". Use which subset of MSigDB. You can run msigdbr::msigdbr_collections() to get options.

...

Other parameters in GSEA() except the cutoff setting

Value

A gseaResult object of

Examples

if (FALSE) {
# Load example
data(Silicosis_pg)
data <- Silicosis_pg
data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";")

# Differential test
ecols <- grep("LFQ.", colnames(data_unique))
se <- make_se_parse(data_unique, ecols,mode = "delim")
filt <- filter_se(se, thr = 0, fraction = 0.4, filter_formula = ~ Reverse != "+" & Potential.contaminant!="+")
norm <- normalize_vsn(filt)
imputed <- impute(norm, fun = "MinProb", q = 0.05)
diff <- test_diff(imputed, type = "control", control  = c("PBS"), fdr.type = "Storey's qvalue")
# GSEA
check_organismDB_depends(organism = "Mouse") # check annotation package of Mouse
res_gsea <- test_GSEA(diff, contrasts = "W4_vs_PBS", species = "Mouse",type = "GO")
}