ct.plot.go.dag: Gene Ontology enrichment sets plotting

In cellTree: Inference and visualisation of Single-Cell RNA-seq data as a hierarchical tree structure

Description

Plots DAG of significantly enriched terms for all topics, along with ancestor nodes.

Usage

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)))

Arguments

go.results	GO Enrichment result list object, such as returned by compute.go.enrichment.
up.generations	Integer (optional). Number of generations above significant nodes to include in the subgraph.
only.topics	Integer vector (optional). If not NULL, vector of topics that should be included in the plot (otherwise all topic enrichment sets are used).
file.output	String (optional). If not NULL, pathname of file to write the plot to.
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.

Value

An igraph object with the annotated GO DAG.

Examples

# 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)

cellTree documentation built on Nov. 8, 2020, 5:05 p.m.