Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ---- echo=T, eval=F----------------------------------------------------------
# # Install LipidMS
# install.packages("LipidMS", dependencies = c("Depends", "Imports"))
#
# # load library
# library(LipidMS)
#
## ---- echo=T, eval=F----------------------------------------------------------
# # load LipidMS library
# library(LipidMS)
#
# # Example data files can be downloaded from:
# # https://drive.google.com/drive/folders/1hSYrQBkh-rAA-oiaKqGkrNL7uWQraV75?usp=sharing
#
# # get mzXML files from your working directory
# files <- dir()[grepl(".mzXML", dir())]
#
# # set the processing parameters
# acquisitionmode <- c("DIA", "DDA", "MS", "MS", "MS", "DIA")
# polarity <- "negative"
# dmzagglom <- 15
# drtagglom <- 500
# drtclust <- c(100, 200)
# minpeak <- c(5, 4)
# drtgap <- 5
# drtminpeak <- 15
# drtmaxpeak <- c(100, 200)
# recurs <- c(5, 10)
# sb <- c(3, 2)
# sn <- c(3, 2)
# minint <- c(500, 100)
# weight <- c(2, 3)
# dmzIso <- 5
# drtIso <- 5
#
# # for single file processing, only samples acquired in DIA or DDA will be processed
# files <- files[acquisitionmode %in% c("DIA", "DDA")]
# acquisitionmode <- acquisitionmode[acquisitionmode %in% c("DIA", "DDA")]
#
# # run the dataProcessing function to obtain the requires msobjects
# msobjects <- list()
#
# for (f in 1:length(files)){
# msobjects[[f]] <- dataProcessing(file = files[f],
# polarity = polarity,
# dmzagglom = dmzagglom,
# drtagglom = drtagglom,
# drtclust = drtclust,
# minpeak = minpeak,
# drtgap = drtgap,
# drtminpeak = drtminpeak,
# drtmaxpeak = drtmaxpeak,
# recurs = recurs,
# sb = sb,
# sn = sn,
# minint = minint,
# weight = weight,
# dmzIso = dmzIso,
# drtIso = drtIso)
# }
## ---- echo=T, eval=F----------------------------------------------------------
# # set annotation parameters
# dmzprecursor <- 5
# dmzproducts <- 10
# rttol <- 5
# coelcutoff <- 0.8
#
# # Annotate lipids
# ## If polarity is positive
# if (polarity == "positive"){
# for (m in 1:length(msobjects)){
# msobjects[[m]] <- idPOS(msobjects[[m]],
# ppm_precursor = dmzprecursor,
# ppm_products = dmzproducts,
# rttol = rttol,
# coelCutoff = coelcutoff)
# }
# }
#
# ## If polarity is negative
# if (polarity == "negative"){
# for (m in 1:length(msobjects)){
# msobjects[[m]] <- idNEG(msobjects[[m]],
# ppm_precursor = dmzprecursor,
# ppm_products = dmzproducts,
# rttol = rttol,
# coelCutoff = coelcutoff)
# }
# }
#
## ---- echo=T, eval=F----------------------------------------------------------
# # example code for idPEpos function
# pe <- idPEpos(msobject,
# ppm_precursor = ppm_precursor,
# ppm_products = ppm_products, rttol = 6,
# chainfrags_sn1 = c("mg_M+H-H2O", "lysope_M+H-H2O"),
# chainfrags_sn2 = c("fa_M+H-H2O", "mg_M+H-H2O"),
# intrules = c("mg_sn1/mg_sn2", "lysope_sn1/lysope_sn2"),
# rates = c("3/1", "3/1"), intrequired = c(T),
# dbs = dbs, coelCutoff = 0.8)
#
# # additional information about how to change rules is given in the documentation
# # of the following functions: chainFrags , checkClass, checkIntensityRules,
# # coelutingFrags, ddaFrags, combineChains and organizeResults. These functions
# # could be also empoyed to build customized identification functions.
## ---- echo=T, eval=F----------------------------------------------------------
# msobject <- idPOS(msobject)
# msobject <- plotLipids(msobject)
#
# # display the first plot
# msobject$plots[[1]]
# msobject$plots[["yourpeakIDofinterest"]]
#
# # save all plot to a pdf file
# pdf("plotresults.pdf")
# for (p in 1:length(msobject$plots)){
# print(msobject$plots[[p]])
# }
# dev.off()
## ---- echo=T, eval=F----------------------------------------------------------
# # load LipidMS library
# library(LipidMS)
#
# # Example data files can be downloaded from:
# # https://drive.google.com/drive/folders/1hSYrQBkh-rAA-oiaKqGkrNL7uWQraV75?usp=sharing
#
# # csv file with 3 columns: sample (mzXML file names), acquisitionmode
# # (MS, DIA or DDA) and sampletype (QC, group1, group2, etc.)
# metadata <- read.csv("Matadata.csv", sep=",", dec = ".")
#
# #==============================================================================#
# # Set processing parameters
# #==============================================================================#
#
# ###################
# # Peak-picking
# polarity <- "positive" # 6550 abrir hasta 50 ppm, con el orbi dejar en 15-20 ppm
# dmzagglom <- 15
# drtagglom <- 500
# drtclust <- c(100, 200)
# minpeak <- c(8, 5)
# drtgap <- 5
# drtminpeak <- 15
# drtmaxpeak <- 200
# recurs <- c(5, 10)
# sb <- c(3,2)
# sn <- c(3,2)
# minint <- c(5000, 1000)
# weight <- c(2, 3)
# dmzIso <- 5
# drtIso <- 5
#
# #==============================================================================#
# # Processing
# #==============================================================================#
#
# ###################
# # Peak-picking
# msbatch <- batchdataProcessing(metadata = metadata,
# polarity = polarity,
# dmzagglom = dmzagglom,
# drtagglom = drtagglom,
# drtclust = drtclust,
# minpeak = minpeak,
# drtgap = drtgap,
# drtminpeak = drtminpeak,
# drtmaxpeak = drtmaxpeak,
# recurs = recurs,
# sb = sb,
# sn = sn,
# minint = minint,
# weight = weight,
# dmzIso = dmzIso,
# drtIso = drtIso,
# parallel = parallel,
# ncores = ncores)
#
# save(msbatch, file="msbatch.rda.gz", compress = TRUE)
## ---- echo=T, eval=F----------------------------------------------------------
# #==============================================================================#
# # Set parameters
# #==============================================================================#
#
# ###################
# # Batch processing
# dmzalign <- 5
# drtalign <- 30
# span <- 0.4
# minsamplesfracalign <- 0.75
# dmzgroup <- 5
# drtagglomgroup <- 30
# drtgroup <- 15
# minsamplesfracgroup <- 0.25
# parallel <- TRUE
# ncores <- 2
#
# #==============================================================================#
# # Processing
# #==============================================================================#
#
# ###################
# # Alignment
# msbatch <- alignmsbatch(msbatch, dmz = dmzalign, drt = drtalign, span = span,
# minsamplesfrac = minsamplesfracalign,
# parallel = parallel, ncores = ncores)
#
# # rt deviation plot
# rtdevplot(msbatch)
# rtdevplot(msbatch, colorbygroup = FALSE)
#
# # tic plot
# plotticmsbatch(msbatch)
# plotticmsbatch(msbatch, colorbygroup = FALSE)
#
# ###################
# # Grouping
# msbatch <- groupmsbatch(msbatch, dmz = dmzgroup, drtagglom = drtagglomgroup,
# drt = drtgroup, minsamplesfrac = minsamplesfracgroup,
# parallel = parallel, ncores = ncores)
#
#
# #####################
# # Fill missing peaks
# msbatch <- fillpeaksmsbatch(msbatch)
#
#
# # Now we have a data matrix with all samples and features.
# View(msbatch$features)
## ---- echo=T, eval=F----------------------------------------------------------
# #==============================================================================#
# # Lipid annotation
# #==============================================================================#
#
# ###################
# # Lipid Annotation
# msbatch <- annotatemsbatch(msbatch)
#
#
# # Make plots for identified lipids
# for (m in 1:length(msbatch$msobjects)){
# if (msbatch$msobjects[[m]]$metaData$generalMetadata$acquisitionmode %in% c("DIA", "DDA")){
# msbatch$msobjects[[m]] <- plotLipids(msbatch$msobjects[[m]])
# }
# }
#
# print(msbatch$msobjects[[1]]$annotation$plots[[1]])
#
## ---- echo=T, eval=F----------------------------------------------------------
# ###################
# # features
# peaklist <- msbatch$features
# peaklistNoIso <- peaklist[peaklist$isotope %in% c("", "[M+0]"),]
#
# View(peaklistNoIso)
#
# write.csv(peaklist, file="peaklist.csv")
# write.csv(peaklistNoIso, file="peaklistNoIso.csv")
#
# ###################
# # annotations
#
# # results
# for (i in 1:length(msbatch$msobjects)){
# if (msbatch$msobjects[[i]]$metaData$generalMetadata$acquisitionmode %in% c("DIA", "DDA")){
# fileName <- gsub(".mzXML", "_summaryResults.csv" ,
# msbatch$msobjects[[i]]$metaData$generalMetadata$file[i])
# write.csv(msbatch$msobjects[[i]]$annotation$results, fileName, row.names = FALSE)
# }
# }
#
# # Annotated Peaklists
# for (i in 1:length(msbatch$msobjects)){
# if (msbatch$msobjects[[i]]$metaData$generalMetadata$acquisitionmode %in% c("DIA", "DDA")){
# fileName <- gsub(".mzXML", "_annotatedPeaklist.csv" ,
# msbatch$msobjects[[i]]$metaData$generalMetadata$file[i])
# write.csv(msbatch$msobjects[[i]]$annotation$annotatedPeaklist, fileName, row.names = FALSE)
# }
# }
#
# ###################
# # plots
#
# pdf("RTdevplot.pdf")
# rtdevplot(msbatch)
# rtdevplot(msbatch, colorbygroup = FALSE)
# dev.off()
#
# pdf("TIC.pdf", height = 7, width = 10)
# plotticmsbatch(msbatch)
# plotticmsbatch(msbatch, colorbygroup = FALSE)
# dev.off()
#
# # lipid id plots
# for (s in 1:length(msbatch$msobjects)){
# if (msbatch$msobjects[[s]]$metaData$generalMetadata$acquisitionmode %in% c("DIA", "DDA")){
# print(s)
# if (msbatch$msobjects[[s]]$metaData$generalMetadata$acquisitionmode == "DIA"){
# height <- 7
# } else {
# height <- 9
# }
# pdf(file = gsub(".mzXML", "_plots.pdf", msbatch$msobjects[[s]]$metaData$generalMetadata$file),
# width = 8, height = height)
# for ( pl in 1:length(msbatch$msobjects[[s]]$annotation$plots)){
# print(msbatch$msobjects[[s]]$annotation$plots[[pl]])
# }
# dev.off()
# }
# }
## ---- echo=T, eval=F----------------------------------------------------------
# # To run LipidMS shiny app execute:
# LipidMSapp()
## ---- echo=T, eval=F----------------------------------------------------------
# fas <- c("8:0", "10:0", "12:0", "14:0", "14:1", "15:0", "16:0", "16:1",
# "17:0", "18:0", "18:1", "18:2", "18:3", "18:4", "20:0", "20:1", "20:2",
# "20:3", "20:4", "20:5", "22:0", "22:1", "22:2", "22:3", "22:4", "22:5",
# "22:6", "24:0", "24:1", "26:0")
# sph <- c("16:0", "16:1", "18:0", "18:1")
# dbs <- createLipidDB(lipid = "all", chains = fas, chains2 = sph)
#
# # to use for identification function two additional data frames need to be added
# dbs$adductsTable <- LipidMS::adductsTable
# dbs$nlsphdb <- LipidMS::nlsphdb
## ---- echo=T, eval=F----------------------------------------------------------
# fas <- c("8:0", "10:0", "12:0", "14:0", "14:1", "15:0", "16:0", "16:1",
# "17:0", "18:0", "18:1", "18:2", "18:3", "18:4", "19:0", "20:0", "20:1",
# "20:2", "20:3", "20:4", "20:5", "22:0", "22:1", "22:2", "22:3", "22:4",
# "22:5", "22:6", "24:0", "24:1", "26:0")
# newfadb <- createLipidDB(lipid = "FA", chains = fas)
# dbs <- assignDB() # This function loads all DBs required
# dbs$fadb <- newfadb$fadb # Then, you can modify some of these DBs
## ---- echo=T, eval=F----------------------------------------------------------
# adductsTable <- LipidMS::adductsTable
# adductsTable <- data.frame(adduct = c(adductsTable$adduct, "M+X"),
# mdiff = c(adductsTable$mdiff, 52.65),
# charge = c(adductsTable$charge, 1),
# n = c(adductsTable$n, 1),
# stringsAsFactors = F)
## ---- echo=T, eval=F----------------------------------------------------------
# # The new adductsTable has to be also uploaded in the dbs list.
# dbs <- assignDB()
# dbs$adductsTable <- adductsTable
#
# idPCpos(msobject = LipidMSdata2::msobjectDIApos,
# adducts = c("M+H", "M+Na", "M+X"), dbs = dbs)
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