msQuantify {msProcess}R Documentation

Mother Function for Peak Quantification

Description

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.

Usage

msQuantify(x, xnew=NULL, measure="intensity")

Arguments

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
"intensity"
quantifies a peak class using the maximum intensity in the corrected spectra within the span of the peak class.

"count"
quantifies a peak class using the number of peaks found in the corrected spectra within the span of the peak class.

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.

Value

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.

References

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.

See Also

msQuantifyIntensity, msQuantifyCount, msAlign.

Examples

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")

[Package msProcess version 1.0.5 Index]