significant_met_own: Provides log2fold change and p value on custom metabolomics...

In metabolomicsworkbench/MetENP: Metabolite enrichment analysis and their associated enriched pathways

Provides log2fold change and p value on custom metabolomics data using t test.

Description

Provides log2fold change and p value on metabolomics data using t test. One can then calculate significant metabolites by using the dataframe generated from this function.

Usage

significant_met_own(
  metabolomics_data,
  met_col,
  metadata,
  normalization,
  factor1,
  factor2,
  factor_col,
  sample_col,
  p_adjust
)

Arguments

metabolomics_data	metabolomics data associated to refmet class
met_col	column with metabolite names
metadata	Metadata
normalization	method for normalization : "half_of_min": where the NAs are replaced by half of min values in the data "remove_NAs": where Cols with NAs values are removed "50percent": where cols with more than 50% NAs values are removed
factor1	first independent variable
factor2	second independent variable
factor_col	column name of the independent variables
sample_col	the column name having samples
p_adjust	Method for p value adjustment, i.e. "fdr"

Examples

stats_metabolites = significant_met_own(metabolomics_data=refmet_names,met_col="metabolite_name",
metadata=metadata, factor1='No shield', factor2='plus shield',
factor_col='treatment',sample_col='local_sample_id', p_adjust='fdr',normalization="half_of_min")

metabolomicsworkbench/MetENP documentation built on April 12, 2025, 7:55 p.m.