quantify_terms: Computes the level of activation of the functions related to...

In babelomics/hipathia: HiPathia: High-throughput Pathway Analysis

Computes the level of activation of the functions related to the previously computed subpathways

Description

Computes the level of activation of the functions related to the previously computed subpathways

Usage

quantify_terms(
  results,
  metaginfo,
  dbannot,
  out_matrix = FALSE,
  normalize = TRUE
)

Arguments

results	List of results as returned by the hipathia function
metaginfo	Pathways object
dbannot	Either a string indicating which precomputed annotation to use ("uniprot" for Uniprot Keywords or "GO" for Gene Ontology terms), or a dataframe with the annotation of the genes to the functions. First column are gene symbols, second column the functions.
out_matrix	Boolean, whther the output object should be a matrix object. Default is FALSE, returning a SummarizedExperiment object.
normalize	Boolean, whether to normalize the matrix of pathway values with normalize_paths before quantifying the signal. Due to the nature of the Hipathia method, in which the length of each pathway may alter its signal rank, we strongly recommend to perform this normalization. This normalization removes the bias. Default is set to TRUE.

Value

Matrix with the level of activation of the functions in dbannot

Examples

data(results)
pathways <- load_pathways(species = "hsa", pathways_list = c("hsa03320",
"hsa04012"))
go_values <- quantify_terms(results, pathways, "GO")
uniprot_values <- quantify_terms(results, pathways, "uniprot")

babelomics/hipathia documentation built on July 27, 2022, 2:23 p.m.