msQuantify: Mother Function for Peak Quantification

In zeehio/msProcess: Protein Mass Spectra Processing

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")

zeehio/msProcess documentation built on May 4, 2019, 10:15 p.m.