analyse gene over-representation with GSEA

test_gene_overrepresentation() takes as input a `tbl` (with at least three columns for sample, feature and transcript abundance) or `SummarizedExperiment` (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and returns a `tbl` with the GSEA statistics

test_gene_overrepresentation(
  .data,
  .entrez,
  .do_test,
  species,
  .sample = NULL,
  gene_sets = NULL,
  gene_set = NULL
)

# S4 method for class 'SummarizedExperiment'
test_gene_overrepresentation(
  .data,
  .entrez,
  .do_test,
  species,
  .sample = NULL,
  gene_sets = NULL,
  gene_set = NULL
)

# S4 method for class 'RangedSummarizedExperiment'
test_gene_overrepresentation(
  .data,
  .entrez,
  .do_test,
  species,
  .sample = NULL,
  gene_sets = NULL,
  gene_set = NULL
)

Arguments

.data

A `tbl` (with at least three columns for sample, feature and transcript abundance) or `SummarizedExperiment` (more convenient if abstracted to tibble with library(tidySummarizedExperiment))

.entrez

The ENTREZ ID of the transcripts/genes

.do_test

A boolean column name symbol. It indicates the transcript to check

species

A character. For example, human or mouse. MSigDB uses the latin species names (e.g., \"Mus musculus\", \"Homo sapiens\")

.sample

The name of the sample column

gene_sets

A character vector. The subset of MSigDB datasets you want to test against (e.g. \"C2\"). If NULL all gene sets are used (suggested). This argument was added to avoid time overflow of the examples.

gene_set

DEPRECATED. Use gene_sets instead.

Value

A consistent object (to the input)

A `SummarizedExperiment` object

A `RangedSummarizedExperiment` object

Details

`r lifecycle::badge("maturing")`

This wrapper execute gene enrichment analyses of the dataset using a list of transcripts and GSEA. This wrapper uses clusterProfiler (DOI: doi.org/10.1089/omi.2011.0118) on the back-end.

# Get MSigDB data msigdb_data = msigdbr::msigdbr(species = species)

# Filter for specific gene collections if provided

msigdb_data = filter(msigdb_data, gs_collection

# Process the data msigdb_data |> nest(data = -gs_collection) |> mutate(test = map( data, ~ clusterProfiler::enricher( my_entrez_rank, TERM2GENE=.x |> select(gs_name, ncbi_gene), pvalueCutoff = 1 ) |> as_tibble() ))

References

Mangiola, S., Molania, R., Dong, R., Doyle, M. A., & Papenfuss, A. T. (2021). tidybulk: an R tidy framework for modular transcriptomic data analysis. Genome Biology, 22(1), 42. doi:10.1186/s13059-020-02233-7

Yu, G., Wang, L. G., Han, Y., & He, Q. Y. (2012). clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology, 16(5), 284-287. doi:10.1089/omi.2011.0118

Examples

## Load airway dataset for examples

  data('airway', package = 'airway')
  # Ensure a 'condition' column exists for examples expecting it

    SummarizedExperiment::colData(airway)$condition <- SummarizedExperiment::colData(airway)$dex



print("Not run for build time.")
#> [1] "Not run for build time."

# airway = airway[!rowData(airway)$entrez |> is.na(),] |> aggregate_duplicates(.transcript = entrez)
# df_entrez = mutate(df_entrez, do_test = feature %in% c("TNFRSF4", "PLCH2", "PADI4", "PAX7"))

if (FALSE) { # \dontrun{
  test_gene_overrepresentation(
    df_entrez,
    .sample = sample,
    .entrez = entrez,
    .do_test = do_test,
    species="Homo sapiens",
   gene_sets =c("C2")
  )
} # }