Description Usage Arguments Details Value References See Also Examples
Performs cross-spectral alignment of detected peaks.
1 |
x |
An object of class |
FUN |
A character string specifying the method to use for alignment. Choices are
Default: |
mz.precision |
A numeric value, used to construct the threshold
when performing clustering.
The default value is 0.003 because SELDI data is often assumed to have
+/- 0.3% mass drift, i.e., a peak at mass |
snr.thresh |
A non-negative numeric value. The peaks with signal-to-noise ratio larger than this value will be used to construct the common set of peak classes. Default: 10. |
... |
Additional arguments passed to the |
Currently, the mass accuracy of a mass spectrometer is proportional to the mass-to-charge (m/z) values. Thus, for a given set of spectra, the locations of the detected peaks will vary from spectrum to spectrum. In order to perform a comparative analysis of an ensemble of spectra, it is then a prerequisite to perform inter-sample alignment of the detected peaks. This process is normally called peak alignment or clustering.
The basic idea for peak alignment is to group peaks of similar molecular weight across all spectra into peak clusters or classes to form a superset, allowing for slight variations in mass. Each cluster is representative of a particular protein. Various methods have been proposed to align the peaks, which differ in how the superset is constructed.
An object of class msSet
, which is the input x
with the added element "peak.class"
: a matrix with peak classes as rows
and some summary statistics of the peak clusters as columns.
These statistics include the location, left bound, right bound
and peak span of the peak classes in both clock tick
("tick.loc"
, "tick.left"
, "tick.right"
, "tick.span"
)
and mass measure
("mass.loc"
, "mass.left"
, "mass.right"
, "mass.span"
).
Coombes, K.R., Tsavachidis, S., Morris, J.S., Baggerly, K.A., and Kuerer, H.M., “Improved peak detection and quantification of mass spectrometry data acquired from surface-enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform," Proteomics, 5:4107–17, 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., and 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.
T.W. Randolph and Y. Yasui, Multiscale Processing of Mass Spectrometry Data, Biometrics, 62:589–97, 2006.
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 | 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)
## extract the peak.class
z[["peak.class"]]
## visualize the alignment
plot(z, process="msAlign", subset=1:8, offset=100,
xlim=c(13000, 17000), lty=c(1,4))
|
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