ct.plot.go.dag {cellTree} | R Documentation

## Gene Ontology enrichment sets plotting

### 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)

[Package cellTree version 1.16.0 Index]