# script to run the TargetSearch analysis
# tested with R - 2.9.1 - Windows XP.
# Author: Alvaro Cuadros-Inostroza <inostroza@mpimp-golm.mpg.de>

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# PLEASE READ THE FOLLOWING BEFORE RUNNING THE SCRIPT          #
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# INSTRUCTIONS
#
# 1. This software requires R (>=2.9.0), Bioconductor (>=2.4) and
#    xcms (>= 1.16.3). If you already have installed them, go to
#    point 4.
#
# 2. Install R (>= 2.9.0) from http://www.r-project.org
#
# 3. Open R and install Bioconductor and xcms by entering:
#    source("http://bioconductor.org/biocLite.R")
#    biocLite()
#    biocLite("xcms")
#
# 4. Download the latest TargetSearch package (1.1.9 or newer) from 
#    http://bioconductor.org/packages/2.5/bioc/html/TargetSearch.html
#    and install it manually. In windows: Menu->Packages->Install
#    packages from local zip files.
#
#    Don't use biocLite() command, since it may install an older
#    version of TargetSearch.
#
# 5. Download the CDF files from DropMET http://prime.psc.riken.jp/
#    Save them in a working directory, c:\temp, for example, together
#    with the files 03.samples.txt, 04.rimLimits.txt and 05.library.txt

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# The R-Script begins here. Please Copy&Paste the following     #
# commands into the R-console. Lines beginning with # are       #
# comments.                                                     #
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# Set working directory. Change if necessary.
setwd("c:/temp")

# load library
library(TargetSearch)

# m/z range to be extracted from chromatogramas
massRange <- c(75,550)

# Import Samples. We assume that all the files are in the same working directory
samples <- ImportSamples("03.samples.txt", RIpath=".", CDFpath=".")

# Import Retention index Standards
rimLimits <- ImportFameSettings("04.rimLimits.txt", row.names = 1)

# Perform baseline correction and peak detection.
# Parameters: Window = smoothing window
#             IntThreshold = lowest intensity to be extracted
#             baseline = (TRUE/FALSE) Perform baseline correction
#             baseline.opts = a list with baseline options: threshold (0..1);
#                             0.5 will return a baseline "in the middle" of the noise
#                             (like in LECO)
RImatrix <- RIcorrect(samples, rimLimits, massRange=massRange, Window=15,
    IntThreshold=50, baseline=TRUE, baseline.opts=list(threshold = 0.5))

# Search for posible outliers
outliers <- FAMEoutliers(samples, RImatrix)

# Import Library:
# Use the top 20 masses of the reference spectrum to be search.
lib <- ImportLibrary("05.library.txt", RI_dev = c(15,8,3), TopMasses=20)

# Performs three step library search
lib <-  medianRILib(samples, lib)
corRI <- sampleRI(samples, lib)
peakData <- peakFind(samples, lib, corRI)

# Obtain Final Profile
metabProfile <- Profile(samples, lib, peakData)
finalProfile <- ProfileCleanUp(metabProfile, timeSplit=2, r_thresh=0.85)

# Write Results
Write.Results(lib, peakData, finalProfile)

# spectra Visualisation
plotAllSpectra(lib, peakData, pdfFile = "spectrum-constrast-Report.pdf")

# Call the GUI
TargetSearchGUI()