Extract functional terms enriched in the DE genes, based on goseq

A wrapper for extracting functional GO terms enriched in a list of (DE) genes, based on the algorithm and the implementation in the goseq package

goseqTable(
  de.genes,
  assayed.genes,
  genome = "hg38",
  id = "ensGene",
  testCats = c("GO:BP", "GO:MF", "GO:CC"),
  FDR_GO_cutoff = 1,
  nTop = 200,
  orgDbPkg = "org.Hs.eg.db",
  addGeneToTerms = TRUE
)

Arguments

de.genes

A vector of (differentially expressed) genes

assayed.genes

A vector of background genes, e.g. all (expressed) genes in the assays

genome

A string identifying the genome that genes refer to, as in the goseq function

id

A string identifying the gene identifier used by genes, as in the goseq function

testCats

A vector specifying which categories to test for over representation amongst DE genes - can be any combination of "GO:CC", "GO:BP", "GO:MF" & "KEGG"

FDR_GO_cutoff

Numeric value for subsetting the results

nTop

Number of categories to extract, and optionally process for adding genes to the respective terms

orgDbPkg

Character string, named as the org.XX.eg.db package which should be available in Bioconductor

addGeneToTerms

Logical, whether to add a column with all genes annotated to each GO term

Value

A table containing the computed GO Terms and related enrichment scores

Details

Note: the feature length retrieval is based on the goseq function, and requires that the corresponding TxDb packages are installed and available

Examples

library(airway)
data(airway)
airway
#> class: RangedSummarizedExperiment 
#> dim: 63677 8 
#> metadata(1): ''
#> assays(1): counts
#> rownames(63677): ENSG00000000003 ENSG00000000005 ... ENSG00000273492
#>   ENSG00000273493
#> rowData names(10): gene_id gene_name ... seq_coord_system symbol
#> colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
#> colData names(9): SampleName cell ... Sample BioSample
dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
  colData = colData(airway),
  design = ~ cell + dex
)
dds_airway <- DESeq2::DESeq(dds_airway)
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
res_airway <- DESeq2::results(dds_airway)

res_subset <- deseqresult2DEgenes(res_airway)[1:100, ]
myde <- res_subset$id
myassayed <- rownames(res_airway)
if (FALSE) {
mygo <- goseqTable(myde,
  myassayed,
  testCats = "GO:BP",
  addGeneToTerms = FALSE
)
head(mygo)
}