Description Usage Arguments Value See Also Examples
View source: R/get_ontologies.R
run_parallel_go
splits provided data frame into a list of data frames
based on the values in the column set_label and
using isplit
function. It then runs
get_all_ontologies
in parallel using foreach by sending each
data frame onto a worker.
1 2 |
dat |
Data frame with two columns: entrez and set_label. The column entrez should contain ENTREZ gene identifiers; the column set_label should contain labels for identifiers that will be analyzed as a group. |
species |
Character string specifying the species, must be one of two "human" (default) or "mouse". For GO analysis we use org.Hs.eg.db, and for mouse org.Mm.eg.db. |
universe |
Character vector of unique ENTREZ gene identifiers to be used as a universe. |
ontologies |
Character vector of ontologies for which to run the analysis. If not provided the analysis will be run for all ontologies: cellular component (CC), biological process (BP) and molecular function (MF). |
cores |
Integer value representing the number of cores to use. This
parameter is optional. If not provided
|
run_parallel |
Boolean indicating whether to run the execution in
parallel. Default is TRUE. If FALSE parameter |
Data frame with the results of GO enrichment.
get_ontology
to understand the output format.
1 2 3 4 5 6 7 8 9 10 11 12 13 | ## Not run:
# load test data:
data("human_symbol")
remove duplicate rows:
dat_clean <- deduplicate_rows(human_symbol)
# map symbol to ENTREZ gene id:
dat_mapped <- map_genes(dat_clean, id = "symbol", species = "human")
# extract universe
universe <- unique(dat_mapped$entrez)
# run GO enrichment in parallel for all three ontologies
res <- run_go(dat_mapped, species = "human", universe = universe)
## End(Not run)
|
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