run_all_metabolites2: Runall function to do complete predictions and evaluate...

In borenstein-lab/mimosa2: Model-based Integration of Metabolite Observations and Species Abundances

Runall function to do complete predictions and evaluate classification of metabolites as high or low abundance

Description

Runall function to do complete predictions and evaluate classification of metabolites as high or low abundance

Usage

run_all_metabolites2(
  genes,
  mets,
  file_prefix = "net1",
  correction = "fdr",
  cutoff = 0.1,
  net_method = "load",
  rxn_table_source = "",
  id_met = F,
  met_id_file = "",
  degree_filter = 0,
  minpath_file = "",
  net_file = "",
  quant = 0.5,
  plot_rank = F,
  plot_continuous = F,
  nonzero_filter = 3,
  rel_abund_mets = F
)

Arguments

genes	Gene abundances
mets	Metabolite abundances
file_prefix	Prefix for output files
correction	Type of multiple hypothesis correction to perform (bonferroni or fdr)
cutoff	Q/P-value cutoff for significance
net_method	Network generation method (see generate_genomic_network)
id_met	Whether metabolites have putative identifications that need to be processed
met_id_file	If id_met, file of metabolite identifications
degree_filter	Threshold for filtering currency metabolites
minpath_file	Optional file of pathways to filter network to
net_file	file containing network template to use
quant	Quantile above which a metabolite is "elevated", default is 0.5
plot_rank	Whether to generate plots of ranks of metabolite concentrations and scores, default is F
plot_continuous	Whether to generate plots of metabolite concentrations and scores, default is F
nonzero_filter	Minimum number of samples required to have nonzero concentrations and nonzero metabolic potential scores in order for metabolite to be evaluated, default is 3

Value

No return, writes output to file

borenstein-lab/mimosa2 documentation built on Dec. 19, 2024, 12:44 a.m.