normulticlassisallgs: Multi-class (N>1) Metabolomic Study with dataset with...

In idrblab/NOREVA: R Package for Systematic Optimization of Metabolomic Data Processing

Multi-class (N>1) Metabolomic Study with dataset with Internal Standards (ISs) and the corresponding data of golden standards for performance evaluation using Criterion e.

Description

this function enables the performance assessment of metabolomic data processing for multi-class dataset (with internal standards but without quality control sample) using five criteria, and can scan thousands of processing workflows and rank them based on their performances.

Usage

normulticlassisallgs(fileName, IS, GS)

Arguments

fileName	Allows the user to indicate the NAME of peak table resulted from PrepareInuputFiles() (default = null).
IS	Allows the user to indicate the column number(s) where the internal standard(s) locate (default = null) If there is only one internal standard (IS), the column number of this IS should be listed If there are multiple ISs, the column numbers of all ISs should be listed and separated using comma For example, the value of argument IS that is set to “2,6,8,n” indicates that the metabolites in the 3rd, 7th, 9th, and (n+1)th columns of your input peak table should be considered to be the IS metabolites.
GS	Allows the user to indicate the name of the file that contains the spike-in compounds (default = null). The file should be in a .csv format, which provides the concentrations of spike-in compounds.

Examples

library(NOREVA)
multi_is_data <- PrepareInuputFiles(dataformat = 1,
rawdata = "Multiclass_with_IS.csv")
normulticlassisallgs(fileName = multi_is_data,
GS = "Multiclass_with_IS_GoldenStandard.csv", IS = "3,4,5")

idrblab/NOREVA documentation built on April 17, 2025, 2:04 p.m.