| msQuantify {msProcess} | R Documentation |
Given an msSet object containing a
peak.class element defining a
common set of peak classes,
this function returns either (i) a matrix of peak
intensities or (ii) a count of the peaks that are associated
with each peak class. The measure argument
is used to specify the output type.
msQuantify(x, xnew=NULL, measure="intensity")
x |
An object of class msSet containing a peak.class element. |
measure |
A character string specifying the measure to be used for quantification.
Choices are
Default: "intensity". |
xnew |
An object of class msSet.
This object may contain a set of spectra that were not used to originally generate
the peak classes. If the user wishes to quantify the original spectra, set
xnew=NULL. Default: NULL. |
The same input msSet object (x if xnew=NULL, xnew otherwise)
with an updated/new peak.matrix element. The rows and columns
of the peak.matrix are the peak class measures
and peak classes, respectively.
If measure="count", the element "peak.list" is also
updated with a class ID for each peak.
Morris, J.S., Coombes, K.R., Koomen, J., Baggerly, K.A., Kobayashi, R., ``Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum," Bioinformatics, textbf{21}(9):1764–75, 2005.
Tibshirani, R., Hastie, T., Narasimhan, B., Soltys, S., Shi, G., Koong, A., and Le, Q.T., ``Sample classification from protein mass spectrometry, by peak probability contrasts," Bioinformatics, textbf{20}(17):3034–44, 2004.
Yasui, Y., McLerran, D., Adam, B.L., Winget, M., Thornquist, M., Feng, Z., ``An automated peak identification/calibration procedure for high-dimensional protein measures from mass spectrometers," Journal of Biomedicine and Biotechnology, textbf{2003}(4):242–8, 2003.
Yasui, Y., Pepe, M., Thompson, M.L., Adam, B.L., Wright, Jr., G.L., Qu, Y., Potter, J.D., Winget, M., Thornquist, M., and Feng, Z., ``A data-analytic strategy for protein biomarker discovery: Profiling of high-dimensional proteomic data for cancer detection," Biostatistics, textbf{4}(3):449–63, 2003.
msQuantifyIntensity, msQuantifyCount, msAlign.
if (!exists("qcset")) data("qcset", package="msProcess")
## extract several spectra from the build-in
## dataset
z <- qcset[, 1:8]
## denoising
z <- msDenoise(z, FUN="wavelet", n.level=10, thresh.scale=2)
## local noise estimation
z <- msNoise(z, FUN="mean")
## baseline subtraction
z <- msDetrend(z, FUN="monotone", attach=TRUE)
## intensity normalization
z <- msNormalize(z)
## peak detection
z <- msPeak(z, FUN="simple", use.mean=FALSE, snr=2)
## peak alignment
z <- msAlign(z, FUN="cluster", snr.thresh=10, mz.precision=0.004)
## peak quantification using intensity
z <- msQuantify(z, measure="intensity")
## extract peak.matrix
z[["peak.matrix"]]
## visualize the peak.matrix
image(z, what="peak.matrix")