ct.plot.go.dag {cellTree} | R Documentation |
Plots DAG of significantly enriched terms for all topics, along with ancestor nodes.
ct.plot.go.dag(go.results, up.generations = 2, only.topics = NULL, file.output = NULL, p.val.threshold = go.results$adjusted.p.threshold, only.unique = FALSE, topic.colors = rainbow(length(go.results$results)))
go.results |
GO Enrichment result list object, such as returned by |
up.generations |
Integer (optional). Number of generations above significant nodes to include in the subgraph. |
only.topics |
Integer vector (optional). If not |
file.output |
String (optional). If not |
p.val.threshold |
Numeric (optional). P-value treshold to use to select which terms should be plotted. |
only.unique |
Only display terms that are only significant for one of the topics. |
topic.colors |
RGB colour vector (optional). Colors to use for each topic. |
An igraph object with the annotated GO DAG.
# Load pre-computed LDA model for skeletal myoblast RNA-Seq data from HSMMSingleCell package: data(HSMM_lda_model) # Load GO mapping database for 'homo sapiens': library(org.Hs.eg.db) # Compute GO enrichment sets for each topic: go.results = compute.go.enrichment(HSMM_lda_model, org.Hs.eg.db, bonferroni.correct=TRUE) go.dag.subtree = ct.plot.go.dag(go.results, up.generations = 2)