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Extract genes from enriched pathways

get_pathway_genes.Rd

Parses pathway-level gene members from an enrichment result and returns either a long table, pathway-indexed list, or a unique vector of genes.

Usage

get_pathway_genes(
  x,
  pathways = NULL,
  top_n = 10,
  pathway_column = c("Description", "ID"),
  gene_column = c("auto", "geneID", "core_enrichment"),
  gene_sep = "/",
  return_type = c("long", "list", "vector")
)

Arguments

x

An enrichResult/gseaResult, or a list containing element enrich (e.g. output from get_msigdb_enrichment()).

pathways

Optional character vector of pathway names to keep. Matches against pathway_column.

top_n

Number of top pathways to use when pathways is NULL. Ranking uses p.adjust (then pvalue) when available. Default: 10.

pathway_column

Which enrichment column to match pathway names against: "Description" (default) or "ID".

gene_column

Which column stores pathway members. "auto" (default) uses "geneID" when present, otherwise "core_enrichment".

gene_sep

Delimiter used in pathway gene strings (default "/").

return_type

One of "long", "list", or "vector".

Value

Depending on return_type:

  • "long": data frame with pathway_id, pathway, and gene.

  • "list": named list of character vectors (genes per pathway).

  • "vector": unique character vector of genes.

Examples

if (requireNamespace("msigdbr", quietly = TRUE)) {
  vista <- example_vista()
  msig <- get_msigdb_enrichment(
    vista,
    sample_comparison = names(comparisons(vista))[1],
    regulation = "Both",
    msigdb_category = "H",
    from_type = "ENSEMBL"
  )
  pathway_tbl <- get_pathway_genes(msig, top_n = 5, return_type = "long")
  head(pathway_tbl)
}
#>     pathway_id      pathway            gene
#> 1 UN_ANNOTATED UN_ANNOTATED ENSG00000003402
#> 2 UN_ANNOTATED UN_ANNOTATED ENSG00000004799

# \donttest{
data("count_data", package = "VISTA")
data("sample_metadata", package = "VISTA")

vista <- create_vista(
  counts = count_data,
  sample_info = sample_metadata,
  column_geneid = "gene_id",
  group_column = "cond_long",
  group_numerator = "treatment1",
  group_denominator = "control"
)
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing

msig <- get_msigdb_enrichment(
  vista,
  sample_comparison = names(comparisons(vista))[1],
  regulation = "Up",
  species = "Homo sapiens",
  from_type = "ENSEMBL"
)
#> 

pathway_tbl <- get_pathway_genes(msig, top_n = 5, return_type = "long")
head(pathway_tbl)
#>                         pathway_id                          pathway
#> 1 HALLMARK_TNFA_SIGNALING_VIA_NFKB HALLMARK_TNFA_SIGNALING_VIA_NFKB
#> 2 HALLMARK_TNFA_SIGNALING_VIA_NFKB HALLMARK_TNFA_SIGNALING_VIA_NFKB
#> 3 HALLMARK_TNFA_SIGNALING_VIA_NFKB HALLMARK_TNFA_SIGNALING_VIA_NFKB
#> 4 HALLMARK_TNFA_SIGNALING_VIA_NFKB HALLMARK_TNFA_SIGNALING_VIA_NFKB
#> 5 HALLMARK_TNFA_SIGNALING_VIA_NFKB HALLMARK_TNFA_SIGNALING_VIA_NFKB
#> 6 HALLMARK_TNFA_SIGNALING_VIA_NFKB HALLMARK_TNFA_SIGNALING_VIA_NFKB
#>              gene
#> 1 ENSG00000003402
#> 2 ENSG00000067082
#> 3 ENSG00000099860
#> 4 ENSG00000102804
#> 5 ENSG00000105835
#> 6 ENSG00000107968
# }