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Wrapper function for Bootstrap Over-Representation Analysis (ORA)

Source: R/ORA.R
Run_bootstrap_ORA.Rd

This function performs a bootstrap-based over-representation analysis (ORA) on a given list of metabolites and background.

Usage

Run_bootstrap_ORA(
  marker_list,
  background,
  custom_universe = NULL,
  alpha_cutoff = 0.05,
  min_intersection = 3,
  consider_isobars = T,
  polarization_mode = NA,
  mass_range_ppm = 3,
  annot_db = "HMDB",
  annot_custom_db = NULL,
  use_LION = F,
  endogenous_only = T,
  pathway_assoc_only = F,
  remove_expected_predicted = T,
  annot_list = NULL,
  annot_weights = NULL,
  n_bootstraps = 50,
  boot_fract_cutoff = 0.5,
  q.val_cutoff = 0.2,
  selected_terms = NULL,
  adjust_contingency = T,
  report_ambiguity_scores = F
)

Arguments

marker_list

A vector or list of marker sets to analyze.

background

A list of named terms where each term is a character vector of metabolites.

custom_universe

An optional vector specifying a custom universe of terms. If not provided all metabolites in background will be used as universe.

alpha_cutoff

A numeric value indicating the alpha cutoff for significance.

min_intersection

An integer specifying the minimum intersection required between input list and a given term in background.

consider_isobars

A logical indicating whether to consider isobars.

polarization_mode

A parameter for polarization mode. If provided, it will be used to consider isomers and isobars.

mass_range_ppm

A numeric value for mass range in parts per million (ppm).

annot_db

A character string specifying the annotation database(s) used for annotation. Check build_iso_bg for more information.

annot_custom_db

An optional custom annotation database. If provided, it will be used alongside or instead of the specified annotation database.

use_LION

A logical indicating whether to use LION ontology for select isomers/isobars for background.

endogenous_only

A logical indicating whether to consider only endogenous compounds. Applies only for HMDB and CoreMetabolome annotation databases.

pathway_assoc_only

A logical indicating whether to consider only pathway-associated compounds. Applies only for HMDB and CoreMetabolome annotation databases.

remove_expected_predicted

A logical indicating whether to remove expected and predicted annotations. Applies only for HMDB and CoreMetabolome annotation databases.

annot_list

An optional list of annotations. If provided, it will be used instead of generating isomers from built-in formula to molecule mapping.

annot_weights

An optional list of annotation weights. If provided, it will be used in the ambiguity score calculation and bootstrap analysis.

n_bootstraps

An integer specifying the number of bootstrap iterations.

boot_fract_cutoff

A numeric value specifying the minimum bootstrap fraction cutoff required. Default to 0.5 .

q.val_cutoff

A numeric value specifying the q-value cutoff for significance.

selected_terms

An optional vector of selected terms to focus on. If provided, the analysis will be restricted to these terms.

adjust_contingency

A logical indicating whether to adjust the contingency table to account for isomeric ambiguity.

report_ambiguity_scores

A logical indicating whether to report ambiguity scores. If TRUE, ambiguity scores will be included in the results.

Value

A list containing the ORA results for each group of markers. Both filtered and per-bootstrap results are provided.

References

Badia-i-Mompel, P., Nagai, J. S., & Saez-Rodriguez, J. (2022). decoupleR: A flexible tool to handle various modes of biological network analysis. Bioinformatics Advances, 2(1), vbac016. https://doi.org/10.1093/bioadv/vbac016

Examples

if (FALSE) { # \dontrun{
data("example_ORA_markers", package = "S2IsoMErData")
data("example_ORA_obj", package = "S2IsoMErData")
object = example_ORA_obj
enrich_res <- Run_bootstrap_ORA(
  marker_list = example_ORA_markers,
  background = object$pathway_list,
  polarization_mode = object$polarization_mode,
  mass_range_ppm = object$mass_range_ppm,
  annot_db = object$Annotation_database,
  annot_custom_db = object$Custom_database,
  use_LION = ifelse(stringr::str_detect(object$background_name, "LION"), TRUE, FALSE),
  endogenous_only = object$endogenous_only,
  pathway_assoc_only = object$pathway_assoc_only,
  remove_expected_predicted = object$remove_expected_predicted,
  annot_weights = object$annotation.weights,
  consider_isobars = object$consider_isobars,
  annot_list = object$annotations
)
} # }