Macarron: Macarron

View source: R/Macarron.R

MacarronR Documentation

Macarron

Description

Macarron

Usage

Macarron(
  input_abundances,
  input_annotations,
  input_metadata,
  input_taxonomy,
  output = "Macarron_output",
  metadata_variable = 1,
  min_prevalence = 0.7,
  execution_mode = "serial",
  standard_identifier = 1,
  anchor_annotation = 2,
  min_module_size = NULL,
  fixed_effects = NULL,
  random_effects = NULL,
  reference = NULL,
  cores = 1,
  plot_heatmap = TRUE,
  plot_scatter = FALSE,
  heatmap_first_n = 50,
  show_best = TRUE,
  priority_threshold = 0.9,
  per_module = 10,
  per_phenotype = 1000,
  only_characterizable = TRUE
)

Arguments

input_abundances

a comma-delimited file or dataframe (features x samples) containing metabolic feature intensities (abundances).

input_annotations

a comma-delimited file or dataframe (features x annotations) containing available feature annotations.

input_metadata

a comma-delimited file or dataframe (samples x metadata) containing sample metadata.

input_taxonomy

a comma-delimited file or dataframe containing the chemical class and subclass information of annotated features.

output

name of the folder where Macarron output files will be written. Default: "Macarron_output".

metadata_variable

Name or index of the column that identifies the phenotypes/conditions in the study. Default: Column 1 of metadata dataframe.

min_prevalence

prevalence threshold (percentage). Default = 0.7.

execution_mode

BiocParallel execution mode. Options: "serial" or "multi" Default = "serial".

standard_identifier

Name or index of column containing HMDB or PubChem IDs. Default: Column 1 in annotation dataframe.

anchor_annotation

Name or index of column containing common names of the annotated metabolite. Default: Column 2 of annotation dataframe.

min_module_size

Integer that defines the size of the smallest covariance module. Default: Cube root of number of prevalent metabolic features.

fixed_effects

Covariates for linear modeling with MaAsLin2. Default: All columns of metadata dataframe.

random_effects

Random effects for linear modeling with MaAsLin2. Default: NULL.

reference

Reference category (factor) in categorical metadata covariates containing three or more levels. Must be provided as a string of 'covariate,reference' semi-colon delimited for multiple covariates.

cores

MaAsLin2 option-The number of R processes to be run in parallel.

plot_heatmap

MaAslin2 option-Generate a heatmap for the significant associations. Default: TRUE

plot_scatter

MaAslin2 option-Generate scatter plots for the significant associations. Default: FALSE

heatmap_first_n

MaAslin2 option-Generate heatmap for top n significant associations. Default = 50

show_best

write 1000 or fewer highly prioritized metabolic features into a separate file. Default: TRUE

priority_threshold

cut-off of priority score for showing highly prioritized features. Default = 0.9

per_module

show first n highly prioritized features in a module. Default = 10

per_phenotype

show highly prioritized n features per phenotype/condition. Default = 1000

only_characterizable

show highly prioritized features in modules which contain at least one annotated metabolite. Default = TRUE

Value

mac.result dataframes containing metabolic features listed according to their priority (potential bioactivity) in a phenotype of interest.

Examples

prism_abundances = system.file("extdata", "demo_abundances.csv", package="Macarron")
prism_annotations = system.file("extdata", "demo_annotations.csv", package="Macarron")
prism_metadata = system.file("extdata", "demo_metadata.csv", package="Macarron")
met_taxonomy = system.file("extdata", "demo_taxonomy.csv", package="Macarron")
mets.prioritized <- Macarron::Macarron(input_abundances = prism_abundances,
                                       input_annotations = prism_annotations,
                                       input_metadata = prism_metadata,
                                       input_taxonomy = met_taxonomy)


biobakery/MACARRoN documentation built on July 1, 2024, 10:01 p.m.