# This file shows how to implement the algorithm proposed in the following paper
# using the S-PLUS software package S+Proteome:
#
# Morris, J.S., Coombes, K.R., Koomen, J., Baggerly, K.A., and Kobayashi, R.,
# "Feature extraction and quantification for mass spectrometry
# in biomedical applications using the mean spectrum,"
# Bioinformatics, 21(9):1764-75, 2005.
#
# NOTE: The parameter setting for the following processing functions
# might not be optimal for the dataset used. A user is encouraged to
# use this file as a template and try out different parameter settings.
#==============================================================================
# load the msProcess package and the msBreast data package
library("msProcess")
library("msBreast")
#------------------------------------------------------------------------------
# select the entire set of spectra for the following processing
data(Breast2003QC, package="msBreast")
z <- Breast2003QC
#------------------------------------------------------------------------------
# denoising
# the output will be an msSet object with an additional element "noise"
# NOTE: we are overwriting the input with the output
z <- msDenoise(z, FUN="wavelet", thresh.scale=2)
#------------------------------------------------------------------------------
# local noise estimation
# the output will be an msSet object with an additional element "noise.local"
z <- msNoise(z, FUN="mean")
#------------------------------------------------------------------------------
# baseline correction
# the output will be an msSet object with an additional element "baseline"
z <- msDetrend(z, FUN="monotone")
#------------------------------------------------------------------------------
# intensity normalization
# the output will be an msSet object with an additional element "tic"
z <- msNormalize(z)
#------------------------------------------------------------------------------
# peak detection
# the output will be an msSet object with additional elements: "peak.class",
# "intensity.mean", "noise.mean", "noise.local.mean", and "use.mean".
# NOTE: we are detecting the peaks on the mean spectrum by setting use.mean=TRUE
z <- msPeak(z, FUN="simple", use.mean=TRUE, snr=2)
# visualize how the peak detection algorithm performed within a certain mass range
plot(z, process="msPeak", xlim=c(13000, 17000))
#------------------------------------------------------------------------------
# peak alignment
# we are skipping the peak alignment step because
# we can use the peaks detected on the mean spectrum to define the peak classes.
# visualize how the mean spectrum peaks perform within a certain mass range
plot(z, process="msAlign", subset=seq(1,96,8), xlim=c(13000, 17000), lty=c(1,4))
#------------------------------------------------------------------------------
# peak quantification
# the output will be an msSet object with an additional element "peak.matrix"
z <- msQuantify(z, measure="intensity")
# display the peak matrix as an image
image(z, what="peak.matrix")
#------------------------------------------------------------------------------
# check out what we have done to this dataset
summary(z)
#==============================================================================
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