Perform the enrichment analysis (GSEA) on the genes

Usage

RunGSEA(
  srt = NULL,
  group.by = NULL,
  test.use = "wilcox",
  DE_threshold = "p_val_adj < 0.05",
  scoreType = "std",
  geneID = NULL,
  geneScore = NULL,
  geneID_groups = NULL,
  geneID_exclude = NULL,
  IDtype = "symbol",
  result_IDtype = "symbol",
  species = "Homo_sapiens",
  db = "GO_BP",
  db_update = FALSE,
  db_version = "latest",
  db_combine = FALSE,
  convert_species = TRUE,
  Ensembl_version = NULL,
  mirror = 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 containing the results of differential expression analysis (RunDEtest). If specified, the genes and groups will be extracted from the Seurat 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.
scoreType: This parameter defines the GSEA score type. Possible options are "std", "pos", "neg". By default ("std") the enrichment score is computed as in the original GSEA. The "pos" and "neg" score types are intended to be used for one-tailed tests (i.e. when one is interested only in positive ("pos") or negateive ("neg") enrichment).
geneID: A character vector specifying the gene IDs.
geneScore: A numeric vector that specifies the gene scores, for example, the log2(fold change) values of gene expression.
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.
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".
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".
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.

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 gseaResult 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).

Examples

if (FALSE) { # \dontrun{
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
pancreas_sub <- RunDEtest(
  pancreas_sub,
  group.by = "CellType"
)
pancreas_sub <- RunGSEA(
  pancreas_sub,
  group.by = "CellType",
  DE_threshold = "p_val_adj < 0.05",
  scoreType = "std",
  db = "GO_BP",
  species = "Mus_musculus"
)
GSEAPlot(
  pancreas_sub,
  db = "GO_BP",
  group.by = "CellType",
  plot_type = "comparison"
)
GSEAPlot(
  pancreas_sub,
  db = "GO_BP",
  group.by = "CellType",
  group_use = "Ductal",
  id_use = "GO:0006412"
)
GSEAPlot(
  pancreas_sub,
  db = "GO_BP",
  group.by = "CellType",
  group_use = "Ductal",
  id_use = c(
    "GO:0046903", "GO:0015031", "GO:0007600"
  )
)

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

# Or use "geneID", "geneScore" and
# "geneID_groups" as input to run GSEA
de_df <- dplyr::filter(
  pancreas_sub@tools$DEtest_CellType$AllMarkers_wilcox,
  p_val_adj < 0.05
)
gsea_out <- RunGSEA(
  geneID = de_df[["gene"]],
  geneScore = de_df[["avg_log2FC"]],
  geneID_groups = de_df[["group1"]],
  db = "GO_BP",
  species = "Mus_musculus"
)
GSEAPlot(
  res = gsea_out,
  db = "GO_BP",
  plot_type = "comparison"
)

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

Perform the enrichment analysis (GSEA) on the genes

Usage

Arguments

Value

See also

Examples