clixo: Create Clique-Extracted Ontology (CliXO)

In herdiantrisufriyana/clixo: An implementation of the Clique-Extracted Ontology (CliXO) algorithm

Description

This function create clique-extracted ontology from a similarity matrix.

Usage

clixo(
  similarity,
  alpha = 0.01,
  beta = 0.5,
  feature_name = "feature",
  onto_prefix = "CliXO",
  os = "windows"
)

Arguments

similarity	Feature similarity, a square matrix of numerics containing feature-feature similarity measures.
alpha	A numeric of a noise parameter. Please see https://pubmed.ncbi.nlm.nih.gov/24932003/.
beta	A numeric of a parameter which deals with missing edges. Please see https://pubmed.ncbi.nlm.nih.gov/24932003/.
feature_name	A character to annotate feature (source)-ontology (target) relation in the resulting ontology.
onto_prefix	A character that precedes the resulting ontology names.
os	A character of operating system. This may be 'windows', 'linux', or 'mac'

Value

clique-extracted ontology, a data frame with rows for ontologies and four columns for source, target, similarity, and relation. Feature (source)- ontology (target) relation is annotated as 'feature' as defined by default for feature_name, while ontology-ontology relation is annotated as 'is_a'. To differentiate between feature and ontology names, an onto_prefix with ':' precedes an ontology name. All columns except similarity are characters. Similarity (a numeric) is a minimum threshold by which either features or ontologies (source) belong to an ontology (target).

Examples

## Create input example
input=input_example()

## Run CliXO algorithm
ontology=clixo(input$similarity)

herdiantrisufriyana/clixo documentation built on March 9, 2021, 2:56 p.m.