Description Usage Arguments Value References See Also Examples
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.
1 | msQuantify(x, xnew=NULL, measure="intensity")
|
x |
An object of class |
measure |
A character string specifying the measure to be used for quantification. Choices are
Default: |
xnew |
An object of class |
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, 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, 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, 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, 4(3):449–63, 2003.
msQuantifyIntensity
, msQuantifyCount
, msAlign
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | 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")
|
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