## ----echo=FALSE--------------------------------------------------------- options(width=74) ## ----------------------------------------------------------------------- library(RMassBank) ## ----------------------------------------------------------------------- library(RMassBankData) ## ----eval=TRUE---------------------------------------------------------- file.copy(system.file("list/NarcoticsDataset.csv", package="RMassBankData"), "./Compoundlist.csv") ## ----eval=TRUE---------------------------------------------------------- RmbSettingsTemplate("mysettings.ini") ## ----echo=TRUE,eval=TRUE------------------------------------------------ loadRmbSettings("mysettings.ini") ## ----------------------------------------------------------------------- w <- newMsmsWorkspace() ## ----------------------------------------------------------------------- files <- list.files(system.file("spectra", package="RMassBankData"), ".mzML", full.names = TRUE) basename(files) # To make the workflow faster here, we use only 2 compounds: w@files <- files[1:2] ## ----------------------------------------------------------------------- loadList("./Compoundlist.csv") ## ----eval=TRUE,fig=TRUE------------------------------------------------- w <- msmsWorkflow(w, mode="pH", steps=c(1:4), archivename = "pH_narcotics") ## ----eval=FALSE--------------------------------------------------------- # plotRecalibration(w) ## ----eval=FALSE--------------------------------------------------------- # w <- msmsWorkflow(w, mode="pH", steps=1) # w <- msmsWorkflow(w, mode="pH", steps=2) # w <- msmsWorkflow(w, mode="pH", steps=3) # # etc. ## ----eval=FALSE--------------------------------------------------------- # lapply(w@spectra,function(s) s@found) ## ----eval=FALSE--------------------------------------------------------- # findProgress(w) ## ----eval=TRUE---------------------------------------------------------- # In the really evaluated workflow, we do the following: # we run steps 1 through 3, load the recalibration curve from a stored workflow # and recalibrate the data using that curve. storedW <- loadMsmsWorkspace(system.file("results/pH_narcotics_RF.RData", package="RMassBankData")) ## ----fig=TRUE----------------------------------------------------------- # Just to display the recalibration curve as calculated from # the complete dataset: storedW <- msmsWorkflow(storedW, mode="pH", steps=4) # Copy the recalibration to workspace w and apply it # (no graph displayed here) w@rc <- storedW@parent@rc w@rc.ms1 <- storedW@parent@rc.ms1 w <- msmsWorkflow(w, mode="pH", steps=4, archivename = "pH_narcotics", newRecalibration = FALSE) ## ----------------------------------------------------------------------- w <- msmsWorkflow(w, mode="pH", steps=c(5:8), archivename = "pH_narcotics") ## ----eval=FALSE--------------------------------------------------------- # archiveResults(w, filename) ## ----------------------------------------------------------------------- mb <- newMbWorkspace(w) mb <- resetInfolists(mb) mb <- loadInfolists(mb, system.file("infolists_incomplete", package="RMassBankData")) ## ----eval=FALSE--------------------------------------------------------- # mb <- resetInfolists(mb) # mb <- loadInfolists(mb, my_folder_with_csv_infolists_inside) ## ----eval=FALSE--------------------------------------------------------- # mb <- addPeaks(mb, my_corrected_Failpeaks.csv) ## ----echo=TRUE,eval=TRUE------------------------------------------------ mb <- mbWorkflow(mb, infolist_path="./Narcotics_infolist.csv") ## ----eval=FALSE--------------------------------------------------------- # mb <- resetInfolists(mb) # mb <- loadInfolists(mb, my_folder_with_csv_infolists_inside) ## ----------------------------------------------------------------------- mb <- resetInfolists(mb) mb <- loadInfolists(mb, system.file("infolists", package="RMassBankData")) ## ----eval=TRUE,echo=TRUE------------------------------------------------ mb <- mbWorkflow(mb) ## ----------------------------------------------------------------------- sessionInfo()