diff --git a/patRoon_codes/Non_target_neg.R b/patRoon_codes/Non_target_neg.R
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+################################Non-target screening of CSF with patRoon 2.1.0###############################
+
+
+###Load library
+library(patRoon)
+
+###Set all options
+#Set the path of the working directory
+setwd("../../../../patRoon_codes/")
+
+#set the name of the output folder
+output <- "report_NT"
+
+# Modify this value to point to the location of the MetFrag2.4.5-CL.jar file
+options(patRoon.path.MetFragCL = "~/opt/metfrag/MetFrag2.4.5-CL.jar")
+
+options(patRoon.path.obabel = "~/babel/bin/")
+
+
+####Initialitation
+
+#Load the analysis.csv that contains the path where the mzML files are. There is one different anaylsis.csv per LC mode (RP or HILIC) and per ionization mode (positive or negative), althought this code is just for the negative mode.
+
+#Positive ionization files
+anaInfoNeg <- read.csv("analysis_RP_NEG.csv") #note that for analyzing HILIC the analysis.csv need to be changed here
+
+#Set the localization of database
+myLocalDatabasePath <- ".../patRoon_files/databases/AD_database.csv"
+
+
+###Extract features
+library(xcms)
+
+#To find negative features
+
+fList <- findFeatures(anaInfoNeg, "xcms3", param = xcms::CentWaveParam(ppm = 5, peakwidth = c(10, 30), mzdiff = -0.001, mzCenterFun = "wMean", integrate = 1L, snthresh = 10, prefilter = 
+                                                                            c(3, 100), noise = 7500, fitgauss = FALSE, firstBaselineCheck = TRUE),verbose = TRUE)
+
+###Group features
+library(BiocParallel)
+BiocParallel::register(BiocParallel::SerialParam(), default = TRUE)
+
+fGroups <- groupFeatures(fList, "xcms3")
+
+library ("MetaClean") 
+fGroups <- filter(fGroups, absMinIntensity = 100,
+                  relMinReplicateAbundance = NULL, maxReplicateIntRSD = NULL,
+                  blankThreshold = 3, removeBlanks = TRUE,
+                  retentionRange = NULL, mzRange = NULL)
+
+###Retrieve MS peak lists
+avgPListParams <- getDefAvgPListParams(clusterMzWindow = 0.005)
+mslists <- generateMSPeakLists(fGroups, "mzr", maxMSRtWindow = 5, 
+                               precursorMzWindow = 0.5, avgFeatParams = 
+                               avgPListParams, avgFGroupParams =
+                               avgPListParams)
+
+mslists <- filter(mslists, withMSMS = TRUE, absMSIntThr = NULL, absMSMSIntThr = NULL, relMSIntThr = NULL,
+                        relMSMSIntThr = NULL, topMSPeaks = NULL, topMSMSPeaks = 25,
+							deIsotopeMS = FALSE, deIsotopeMSMS = FALSE)
+	
+							 
+###Find compound structure candidates 							 
+compounds <- generateCompounds(fGroups, mslists, "metfrag", method = "CL",
+                               dbRelMzDev = 5, fragRelMzDev = 5, 
+                               fragAbsMzDev = 0.002, adduct = "[M-H]-", 
+                               database = "csv",
+                               extraOpts = 
+                                 list(LocalDatabasePath = myLocalDatabasePath),
+                               scoreTypes = c("fragScore","score", "individualMoNAScore"),
+                               maxCandidatesToStop = 500, errorRetries = 200, timeoutRetries = 20) 
+							   
+
+###Report results
+#Create the csv tables with groups and peak intensities 
+reportCSV(fGroups, path = output, reportFeatures = FALSE,
+          compounds = compounds, compoundsNormalizeScores = "max")
+		  
+# Summary of MetFrag Results in a a Single Table 
+MFsummary <- as.data.table(compounds)
+outputSummary <- paste(output, "MFsummary.csv", sep = "/")
+write.csv(MFsummary, outputSummary)
+
+#Plots of every group of features (in the set)
+reportPDF(fGroups, path = output, reportFGroups = TRUE, 
+          reportFormulaSpectra = TRUE, compounds = compounds, 
+          compoundsNormalizeScores = "max",
+          MSPeakLists = mslists)
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