findMacMod: Cluster metabolic features based on covarying abundances into...

In biobakery/MACARRoN: Prioritization of potentially bioactive metabolic features from epidemiological and environmental metabolomics datasets

Cluster metabolic features based on covarying abundances into modules

Description

Cluster metabolic features based on covarying abundances into modules

Usage

findMacMod(
  se,
  w,
  input_taxonomy,
  standard_identifier = 1,
  min_module_size = NULL,
  evaluateMOS = TRUE
)

Arguments

se	SummarizedExperiment object created using Macarron::prepInput().
w	distance matrix from function Macarron::makeDisMat().
input_taxonomy	chemical taxonomy file with 3 columns specifying annotation, subclass and class of annotated features. Can be created using the decorateID.R utility of Macarron. Annotation specified with "standard_identifier" and annotation in the first column of the chemical taxonomy file must match.
standard_identifier	name or index of column containing HMDB or PubChem IDs. Default: Column 1 in annotation dataframe.
min_module_size	minimum module size to be used for module identification with dynamicTreeCut::cutreeDynamic(). Default is cube root of number of prevalent features.
evaluateMOS	examine measure of success for modules identified using min_module_size, min_module_size + 5, min_module_size + 10, min_module_size - 5, min_module_size - 10

Value

mod.assn metabolic features clustered into "modules" based on covarying abundances and measures of success.

Examples

prism_abundances = system.file("extdata", "demo_abundances.csv", package="Macarron")
abundances_df = read.csv(file = prism_abundances, row.names = 1)
prism_annotations = system.file("extdata", "demo_annotations.csv", package="Macarron")
annotations_df = read.csv(file = prism_annotations, row.names = 1)
prism_metadata = system.file("extdata", "demo_metadata.csv", package="Macarron")
metadata_df = read.csv(file = prism_metadata, row.names = 1)
met_taxonomy = system.file("extdata", "demo_taxonomy.csv", package="Macarron")
taxonomy_df = read.csv(file = met_taxonomy)
mbx <- Macarron::prepInput(input_abundances = abundances_df,
                            input_annotations = annotations_df,
                            input_metadata = metadata_df)
w <- Macarron::makeDisMat(se = mbx)
modules.assn <- Macarron::findMacMod(se = mbx, 
                                     w = w,
                                     input_taxonomy = taxonomy_df) 

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