knitr::opts_chunk$set(echo = TRUE)

library(MetClassNetR)
library(MsCoreUtils)
library(tidyverse)
library(dplyr)
library(QFeatures)

Overview

This vignette aims to compare different mapping strategies included in the package. For a description how to generate a multilayer network see vignette MultiLayerNetwork.

Preparation

For this Vignettes we use data from MTBLS1586.

# load("../VariablesFromVignette.RData")
path <- "extdata/MTBLS1586/"
exp <- "MTBLS1586_LC-MS_positive_reverse-phase_metabolite_profiling"

inputData <- 
  loadInputData(
    peakListF = 
      system.file(
        paste0(path, "m_", exp, "_v2_maf.tsv"), 
        package = "MetClassNetR"
        ), 
    transF = 
      system.file(
        paste0(path, "transformations_MTBLS1586.csv"), 
        package = "MetClassNetR"
        ),
    spectraF = 
      system.file(
        paste0(path, "ms2_", exp, ".mgf"), 
        package = "MetClassNetR"
        ), 
    gsmnF = 
      system.file(
        paste0(path, "WormJam-GEM-20190101_L3_no-side_no-comp.gml"), 
        package = "MetClassNetR"
        ),  
    resPath = "~/MetClassNetR_MultiLayerNetwork/", 
    met2NetDir = 
      paste0(
        find.package("MetClassNetR"), 
        "/Python/metabolomics2network-master/"
        ),
    configF =  
      system.file(
        paste0(path, "Metabolomics2NetworksData/JsonConf.txt"), 
        package = "MetClassNetR"
        ),
    idenMetF =  
      system.file(
        paste0(
          path, 
          "Metabolomics2NetworksData/IdentifiedMet_", 
          exp, 
          "_v2_maf.tsv"
          ), 
        package = "MetClassNetR"
        ),
    metF = 
      system.file(
        paste0(path, "Metabolomics2NetworksData/WormJamMetWithMasses.tsv"), 
        package = "MetClassNetR"
        ), 
    cleanMetF = FALSE
    ) 

Metabolomics2network

resFile <- "Res_Met2Net_MappedMet.txt"
mapMetToGSMN(inputData, resFile, method="metabolomics2network")

Map by ID

mapMetToGSMN(inputData, method="id_inchikey")

Map by class



MetClassNet/MetClassNetR documentation built on June 30, 2023, 2:12 p.m.