Gene Ontology enrichment — perform.GO.enrichment • IBRAP

Performs gene ontology enrichment for individual cluster differential expression results.

perform.GO.enrichment(
  result,
  whichOnto = "BP",
  feasibleGenes = NULL,
  mapping = "org.Hs.eg.db",
  ID = "symbol",
  nodeSize = 5,
  algorithm = "classic",
  statistic = "ks",
  rank.cutoff = 0.001,
  gene.col = "gene",
  pval.col = "p_val",
  cluster.col = "cluster",
  n.top.pathways = 10
)

Arguments

result: A database containing the differential expression results
whichOnto: Character. specifying one of the three GO ontologies, namely: "BP", "MF", "CC". Default = 'BP'
feasibleGenes: Character vector. vector containing a subset of gene identifiers. Only these genes will be used to annotate GO terms. Default value is NULL which means that there are no genes filtered.
mapping: Character. The name of the Bioconductor package containing the gene mappings for a specific organism. For example: mapping = "org.Hs.eg.db".
ID: Character. Specify the gene identifier to use. Currently only the following identifiers can be used: c("entrez", "genbank", "alias", "ensembl", "symbol", "genename", "unigene")
nodeSize: Numerical. Minimum number of genes required to consider a GO term. Default = 5
algorithm: Character. Which algorithm to use when testing for significant GO terms, options: 'classic', 'elim', 'weight', 'weight01', 'lea', 'parentchild'. Default = 'classic'
statistic: Character. Which statistic to use when testing for significant GO terms, options: 'fisher', 'ks', 't', 'globaltest', 'sum', 'ks.ties'. Default = 'ks'
rank.cutoff: Numerical. Which cut off to apply for pathway significance, this value will change according to the statistic applied. Default = 0.001
gene.col: Character. Which column name within differential expression results contains the genes. Default = 'gene.col'
pval.col: Character. Which column name within differential expression results contains the p values. Default = 'p_val'
cluster.col: Character. Which column name within differential expression results contains the cluster assignments. Default = 'cluster'
n.top.pathways: Numerical. How many top pathways per group should be retained. Default = 10

Value

A dataframe containing the top enriched pathways for each cluster

Examples


SCT_DE <- perform.seurat.diffexp.all(object = object, assay = 'SCT', test = 'MAST', identity = object@sample_metadata$celltype, latent.vars = 'original.project')
#> Error in perform.seurat.diffexp.all(object = object, assay = "SCT", test = "MAST",     identity = object@sample_metadata$celltype, latent.vars = "original.project"): could not find function "perform.seurat.diffexp.all"

SCT_DE_GO <- perform.GO.enrichment(result = SCT_DE)
#> Error in is.data.frame(result): object 'SCT_DE' not found

plot.GO.output(result = SCT_DE_GO) + ggplot2::ggtitle(label = 'SCT')
#> Error in plot.GO.output(result = SCT_DE_GO): object 'SCT_DE_GO' not found