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Perform the enrichment analysis (over-representation) on the genes

Source: R/RunEnrichment.R
RunEnrichment.Rd

Perform the enrichment analysis (over-representation) on the genes

Usage

RunEnrichment(
  srt = NULL,
  group.by = NULL,
  test.use = "wilcox",
  DE_threshold = "avg_log2FC > 0 & p_val_adj < 0.05",
  geneID = NULL,
  geneID_groups = NULL,
  geneID_exclude = NULL,
  IDtype = "symbol",
  result_IDtype = "symbol",
  backend = c("cpp", "r"),
  species = "Homo_sapiens",
  db = "GO_BP",
  db_update = FALSE,
  db_version = "latest",
  db_combine = FALSE,
  convert_species = TRUE,
  Ensembl_version = NULL,
  mirror = NULL,
  features = NULL,
  TERM2GENE = NULL,
  TERM2NAME = NULL,
  minGSSize = 10,
  maxGSSize = 500,
  unlimited_db = c("Chromosome", "GeneType", "TF", "Enzyme", "CSPA"),
  GO_simplify = FALSE,
  GO_simplify_cutoff = "p.adjust < 0.05",
  simplify_method = "Wang",
  simplify_similarityCutoff = 0.7,
  cores = 1,
  verbose = TRUE,
  ...
)

Arguments

srt

A Seurat object or SummarizedExperiment object containing the results of differential expression analysis (RunDEtest()). If specified, the genes and groups will be extracted from the object automatically. If not specified, the geneID and geneID_groups arguments must be provided.

group.by

Name of one or more meta.data columns to group (color) cells by.

test.use

A character vector specifying the test to be used in differential expression analysis. This argument is only used if srt is specified.

DE_threshold

A character vector specifying the filter condition for differential expression analysis. This argument is only used if srt is specified.

geneID

A character vector specifying the gene IDs.

geneID_groups

A factor vector specifying the group labels for each gene.

geneID_exclude

A character vector specifying the gene IDs to be excluded from the analysis.

IDtype

A character vector specifying the type of gene IDs in the srt object or geneID argument. This argument is used to convert the gene IDs to a different type if IDtype is different from result_IDtype.

result_IDtype

A character vector specifying the desired type of gene ID to be used in the output. This argument is used to convert the gene IDs from IDtype to result_IDtype.

backend

Enrichment backend. "cpp" is the default and uses a fast native hypergeometric ORA implementation and returns the enrichment table without enrichResult objects. "r" uses clusterProfiler::enricher() and returns enrichResult objects in results. GO_simplify = TRUE currently uses the R backend.

species

A character vector specifying the species for which the gene annotation databases should be prepared. Can be "Homo_sapiens" or "Mus_musculus".

db

A character vector specifying the annotation sources to be included in the gene annotation databases. Can be one or more of "GO", "GO_BP", "GO_CC", "GO_MF", "KEGG", "WikiPathway", "Reactome", "CORUM", "MP", "DO", "HPO", "PFAM", "CSPA", "Surfaceome", "SPRomeDB", "VerSeDa", "TFLink", "hTFtarget", "TRRUST", "JASPAR", "ENCODE", "MSigDB", "CellTalk", "CellChat", "Chromosome", "GeneType", "Enzyme", "TF", "CytoTRACE2". MSigDB subcollections can be requested as "MSigDB_<collection>", such as "MSigDB_H" for human Hallmark and "MSigDB_MH" for mouse Hallmark. Note: "CytoTRACE2" is species-independent and downloads pre-trained model data required by RunCytoTRACE.

db_update

Whether the gene annotation databases should be forcefully updated. If set to FALSE, the function will attempt to load the cached databases instead. Default is FALSE.

db_version

A character vector specifying the version of the gene annotation databases to be retrieved. Default is "latest".

db_combine

Whether to combine multiple databases into one. If TRUE, all database specified by db will be combined as one named "Combined".

convert_species

Whether to use a species-converted database when the annotation is missing for the specified species. Default is TRUE.

Ensembl_version

An integer specifying the Ensembl version. Default is NULL. If NULL, the latest version will be used.

mirror

Specify an Ensembl mirror to connect to. The valid options here are "www", "uswest", "useast", "asia".

features

A named list of feature lists for custom enrichment gene sets. If provided, it takes precedence over TERM2GENE and db.

TERM2GENE

A data frame specifying the gene-term mapping for a custom database. The first column should contain the term IDs, and the second column should contain the gene IDs.

TERM2NAME

A data frame specifying the term-name mapping for a custom database. The first column should contain the term IDs, and the second column should contain the corresponding term names.

minGSSize

The minimum size of a gene set to be considered in the enrichment analysis.

maxGSSize

The maximum size of a gene set to be considered in the enrichment analysis.

unlimited_db

A character vector specifying the names of databases that do not have size restrictions.

GO_simplify

Whether to simplify the GO terms. If TRUE, additional results with simplified GO terms will be returned.

GO_simplify_cutoff

A character vector specifying the filter condition for simplification of GO terms. This argument is only used if GO_simplify is TRUE.

simplify_method

A character vector specifying the method to be used for simplification of GO terms. This argument is only used if GO_simplify is TRUE.

simplify_similarityCutoff

The similarity cutoff for simplification of GO terms. This argument is only used if GO_simplify is TRUE.

cores

The number of cores to use for parallelization with foreach::foreach. Default is 1.

verbose

Whether to print the message. Default is TRUE.

...

Passed to other functions.

Value

If input is a Seurat object, returns the modified Seurat object with the enrichment result stored in the tools slot.

If input is a geneID vector with or without geneID_groups, return the enrichment result directly.

Enrichment result is a list with the following component:

  • enrichment: A data.frame containing all enrichment results.

  • results: A list of enrichResult objects from the DOSE package.

  • geneMap: A data.frame containing the ID mapping table for input gene IDs.

  • input: A data.frame containing the input gene IDs and gene ID groups.

  • DE_threshold: A specific threshold for differential expression analysis (only returned if input is a Seurat object).

See also

PrepareDB, ListDB, EnrichmentPlot, RunGSEA, GSEAPlot

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
pancreas_sub <- RunDEtest(
  pancreas_sub,
  group.by = "CellType"
)
pancreas_sub <- RunEnrichment(
  pancreas_sub,
  group.by = "CellType",
  DE_threshold = "p_val_adj < 0.05",
  db = "GO_BP",
  species = "Mus_musculus"
)
EnrichmentPlot(
  pancreas_sub,
  db = "GO_BP",
  group.by = "CellType",
  plot_type = "comparison"
)

pancreas_sub <- RunEnrichment(
  pancreas_sub,
  group.by = "CellType",
  DE_threshold = "p_val_adj < 0.05",
  db = c("MSigDB", "MSigDB_MH"),
  species = "Mus_musculus"
)
EnrichmentPlot(
  pancreas_sub,
  db = "MSigDB",
  group.by = "CellType",
  plot_type = "comparison"
)
EnrichmentPlot(
  pancreas_sub,
  db = "MSigDB_MH",
  group.by = "CellType",
  plot_type = "comparison"
)

# Remove redundant GO terms
pancreas_sub <- RunEnrichment(
  pancreas_sub,
  group.by = "CellType",
  db = "GO_BP",
  GO_simplify = TRUE,
  species = "Mus_musculus"
)
EnrichmentPlot(
  pancreas_sub,
  db = "GO_BP_sim",
  group.by = "CellType",
  plot_type = "comparison"
)

# Or use "geneID" and "geneID_groups" as input to run enrichment
de_df <- dplyr::filter(
  pancreas_sub@tools$DEtest_CellType$AllMarkers_wilcox,
  p_val_adj < 0.05
)
enrich_out <- RunEnrichment(
  geneID = de_df[["gene"]],
  geneID_groups = de_df[["group1"]],
  db = "GO_BP",
  species = "Mus_musculus"
)
EnrichmentPlot(
  res = enrich_out,
  db = "GO_BP",
  plot_type = "comparison"
)

# Use a combined database
pancreas_sub <- RunEnrichment(
  pancreas_sub,
  group.by = "CellType",
  db = c(
    "KEGG", "WikiPathway", "Reactome", "PFAM", "MP"
  ),
  db_combine = TRUE,
  species = "Mus_musculus"
)
EnrichmentPlot(
  pancreas_sub,
  db = "Combined",
  group.by = "CellType",
  plot_type = "comparison"
)