# This file shows how to implement the algorithm proposed in the following papers
# using the S-PLUS software package S+Proteome:
#
# 1. Randolph TW and Yasui Y,
# "Multiscale processing of mass spectrometry data,"
# Biometrics, 62:589-597, 2006.
#
# 2. Randolph TW, Mitchell BL, McLerran DF, Lampe PD, and Feng Z,
# "Quantifying peptide signal in MALDI-TOF mass spectrometry data,"
# Molecular & Cellular Proteomics, 4(12):1990-1999, 2005.
#
# 3. Randolph TW,
# "Scale-based normalization of spectral data,"
# Cancer Biomarkers, 2(3-4):135-144, 2006.
#
# 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("msDilution")
#------------------------------------------------------------------------------
# explore the build-in dataset Dilution2005Raw
# print out the summary of Dilution2005Raw
data(Dilution2005Raw, package="msDilution")
Dilution2005Raw
# plot the peptide mixture spectra by setting the interspectrum offset manually
plot(Dilution2005Raw, subset=Dilution2005Raw$coding$pep.ind, offset=1000)
# plot serum-only spectra by computing the offset automatically for better visualization
# it might take some time to compute the offset for the entire set of spectra
plot(Dilution2005Raw, subset=Dilution2005Raw$coding$ser.ind)
# visualize half of the serum + peptide mixture spectra as an image
image(Dilution2005Raw, subset=Dilution2005Raw$coding$mix.ind[1:125])
#------------------------------------------------------------------------------
# select a few spectra to demonstrate the pipeline processing functionalities
z <- Dilution2005Raw[, Dilution2005Raw$coding$mix.ind[1:8]]
#------------------------------------------------------------------------------
# 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="mrd", wavelet="s8", levels=6, keep.smooth=FALSE, keep.details=TRUE)
# visualize how the denoising algorithm performed within a certain mass range
plot(z, process="msDenoise", subset=NULL, xlim=c(25000, 28000), lwd=c(2,1))
# display the noise removed as an image
image(z, what="noise")
#------------------------------------------------------------------------------
# # local noise estimation is not needed for MRD approach
# # as only detail of a specific scale is kept.
#------------------------------------------------------------------------------
# # baseline correction is not needed for MRD approach
# # as smooth was removed when denoising
#------------------------------------------------------------------------------
# intensity normalization
# the output will be an msSet object with an additional element "tic"
z <- msNormalize(z, FUN="snv")
# visualize how the intensity normalization algorithm performed within a certain mass range
plot(z, process="msNormalize", subset=NULL, xlim=c(25000, 28000), lty=c(1,4))
#------------------------------------------------------------------------------
# peak detection
# the output will be an msSet object with additional elements "peak.list" and "use.mean"
z <- msPeak(z, FUN="mrd", use.mean=FALSE, snr=2)
# visualize how the peak detection algorithm performed within a certain mass range
plot(z, process="msPeak", subset=NULL, xlim=c(25000, 28000))
#------------------------------------------------------------------------------
# peak alignment
# the output will be an msSet object with an additional element "peak.class"
z <- msAlign(z, FUN="mrd", snr.thresh=2)
# visualize how the peak alignment algorithm performed within a certain mass range
plot(z, process="msAlign", subset=NULL, xlim=c(25000, 28000), 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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