Convert raw peak data to peak list (centroided data)
Peaklist
= mspeaks(X
, Intensities
)
[Peaklist
, PFWHH
]
= mspeaks(X
, Intensities
)
[Peaklist
, PFWHH
, PExt
]
= mspeaks(X
, Intensities
)
mspeaks(X
, Intensities
,
...'Base', BaseValue
, ...)
mspeaks(X
, Intensities
,
...'Levels', LevelsValue
, ...)
mspeaks(X
, Intensities
,
...'NoiseEstimator', NoiseEstimatorValue
,
...)
mspeaks(X
, Intensities
,
...'Multiplier', MultiplierValue
, ...)
mspeaks(X
, Intensities
,
...'Denoising', DenoisingValue
, ...)
mspeaks(X
, Intensities
,
...'PeakLocation', PeakLocationValue
, ...)
mspeaks(X
, Intensities
,
...'FWHHFilter', FWHHFilterValue
, ...)
mspeaks(X
, Intensities
,
...'OverSegmentationFilter', OverSegmentationFilterValue
,
...)
mspeaks(X
, Intensities
,
...'HeightFilter', HeightFilterValue
, ...)
mspeaks(X
, Intensities
,
...'ShowPlot', ShowPlotValue
, ...)
mspeaks(X
, Intensities
,
...'Style', StyleValue
, ...)
finds
relevant peaks in raw, noisy peak signal data, and creates Peaklist
= mspeaks(X
, Intensities
)Peaklist
,
a twocolumn matrix, containing the separationaxis value and intensity
for each peak. X
is a vector of separationunit
values for a set of signals with peaks. Intensities
is
a matrix of intensity values for a set of peaks that share the same
separationunit range.
[
returns Peaklist
, PFWHH
]
= mspeaks(X
, Intensities
)PFWHH
,
a twocolumn matrix indicating the left and right locations of the
full width at half height (FWHH) markers for each peak. For any peak
not resolved at FWHH, mspeaks
returns the peak
shape extents instead. When Intensities
includes
multiple signals, then PFWHH
is a cell
array of matrices.
[
returns Peaklist
, PFWHH
, PExt
]
= mspeaks(X
, Intensities
)PExt
,
a twocolumn matrix indicating the left and right locations of the
peak shape extents determined after wavelet denoising. When Intensities
includes
multiple signals, then PExt
is a cell array
of matrices.
mspeaks(
calls X
, Intensities
,
...'PropertyName
', PropertyValue
,
...)mspeaks
with optional properties
that use property name/property value pairs. You can specify one or
more properties in any order. Enclose each PropertyName
in
single quotation marks. Each PropertyName
is
case insensitive. These property name/property value pairs are as
follows:
mspeaks(
specifies
the wavelet base. X
, Intensities
,
...'Base', BaseValue
, ...)
mspeaks(
specifies
the number of levels for the wavelet decomposition. X
, Intensities
,
...'Levels', LevelsValue
, ...)
mspeaks(
specifies the method to estimate the threshold, X
, Intensities
,
...'NoiseEstimator', NoiseEstimatorValue
,
...)T
,
to filter out noisy components in the first highband decomposition
(y_h
).
mspeaks(
specifies
the threshold multiplier constant. X
, Intensities
,
...'Multiplier', MultiplierValue
, ...)
mspeaks(
controls
the use of wavelet denoising to smooth the signal. Choices are X
, Intensities
,
...'Denoising', DenoisingValue
, ...)true
(default)
or false
.
mspeaks(
specifies
the proportion of the peak height to use to select the points used
to compute the centroid separationaxis value of the respective peak. X
, Intensities
,
...'PeakLocation', PeakLocationValue
, ...)PeakLocationValue
must
be a value ≥ 0
and ≤ 1
.
Default is 1.0
.
mspeaks(
specifies
the minimum full width at half height (FWHH), in separation units,
for reported peaks. Peaks with FWHH below this value are excluded
from the output list X
, Intensities
,
...'FWHHFilter', FWHHFilterValue
, ...)Peaklist
.
mspeaks(
specifies the minimum distance, in separation units,
between neighboring peaks. When the signal is not smoothed appropriately,
multiple maxima can appear to represent the same peak. Increase this
filter value to join oversegmented peaks into a single peak.X
, Intensities
,
...'OverSegmentationFilter', OverSegmentationFilterValue
,
...)
mspeaks(
specifies
the minimum height for reported peaks. Peaks with heights below this
value are excluded from the output list X
, Intensities
,
...'HeightFilter', HeightFilterValue
, ...)Peaklist
.
mspeaks(
controls
the display of a plot of the original and the smoothed signal, with
the peaks included in the output matrix X
, Intensities
,
...'ShowPlot', ShowPlotValue
, ...)Peaklist
marked.
mspeaks(
specifies
the style for marking the peaks in the plot. X
, Intensities
,
...'Style', StyleValue
, ...)
mspeaks
finds peaks in data from any separation
technique that produces signal data, such as spectroscopy, nuclear
magnetic resonance (NMR), electrophoresis, chromatography, or mass
spectrometry.

Vector of separationunit values for a set of signals with peaks.
The number of elements in the vector equals the number of rows in
the matrix  

Matrix of intensity values for a set of peaks that share the
same separationunit range. Each row corresponds to a separationunit
value, and each column corresponds to either a set of signals with
peaks or a retention time. The number of rows equals the number of
elements in vector  

Integer from Default:  

Integer from Default:  

Character vector or scalar that specifies the method to estimate
the threshold,
 

Positive real value that specifies the threshold multiplier constant. Default:  

Controls the use of wavelet denoising to smooth the signal.
Choices are  

Value that specifies the proportion of the peak height to use
to select the points to compute the centroid separationaxis value
of the respective peak. The value must be
Default:  

Positive real value that specifies the minimum full width at
half height (FWHH), in separation units, for reported peaks. Peaks
with FWHH below this value are excluded from the output list Default:  

Positive real value that specifies the minimum distance, in separation units, between neighboring peaks. When the signal is not smoothed appropriately, multiple maxima can appear to represent the same peak. Increase this filter value to join oversegmented peaks into a single peak. Default:  

Positive real value that specifies the minimum height for reported peaks. Default:  

Controls the display of a plot of the original signal and the
smoothed signal, with the peaks included in the output matrix
 

Character vector specifying the style for marking the peaks in the plot. Choices are:


Twocolumn matrix where each row corresponds to a peak. The
first column contains separationunit values (indicating the location
of peaks along the separation axis). The second column contains intensity
values. When 

Twocolumn matrix indicating the left and right locations of
the full width at half height (FWHH) markers for each peak. For any
peak not resolved at FWHH, 

Twocolumn matrix indicating the left and right locations of
the peak shape extents determined after wavelet denoising. When 
Load a MATfile, included with the Bioinformatics Toolbox™ software,
that contains two mass spectrometry data variables, MZ_lo_res
and Y_lo_res
. MZ_lo_res
is
a vector of m/z values for a set of spectra. Y_lo_res
is
a matrix of intensity values for a set of mass spectra that share
the same m/z range.
load sample_lo_res
Adjust the baseline of the eight spectra stored in Y_lo_res
.
YB = msbackadj(MZ_lo_res,Y_lo_res);
Convert the raw mass spectrometry data to a peak list by finding the relevant peaks in each spectrum.
P = mspeaks(MZ_lo_res,YB);
Plot the third spectrum in YB
,
the matrix of baselinecorrected intensity values, with the detected
peaks marked.
P = mspeaks(MZ_lo_res,YB,'SHOWPLOT',3);
Smooth the signal using the mslowess
function.
Then convert the smoothed data to a peak list by finding relevant
peaks and plot the third spectrum.
YS = mslowess(MZ_lo_res,YB,'SHOWPLOT',3);
P = mspeaks(MZ_lo_res,YS,'DENOISING',false,'SHOWPLOT',3);
Use the cellfun
function
to remove all peaks with m/z values less than 2000 from the eight
peaks listed in output P
. Then plot the peaks of
the third spectrum (in red) over its smoothed signal (in blue).
Q = cellfun(@(p) p(p(:,1)>2000,:),P,'UniformOutput',false); figure plot(MZ_lo_res,YS(:,3),'b',Q{3}(:,1),Q{3}(:,2),'rx') xlabel('Mass/Charge (M/Z)') ylabel('Relative Intensity') axis([0 20000 5 95])
[1] Morris, J.S., Coombes, K.R., Koomen, J., Baggerly, K.A., and Kobayash, R. (2005) Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum. Bioinfomatics 21:9, 1764–1775.
[2] Yasui, Y., Pepe, M., Thompson, M.L., Adam, B.L., Wright, G.L., Qu, Y., Potter, J.D., Winget, M., Thornquist, M., and Feng, Z. (2003) A dataanalytic strategy for protein biomarker discovery: profiling of highdimensional proteomic data for cancer detection. Biostatistics 4:3, 449–463.
[3] Donoho, D.L., and Johnstone, I.M. (1995) Adapting to unknown smoothness via wavelet shrinkage. J. Am. Statist. Asso. 90, 1200–1224.
[4] Strang, G., and Nguyen, T. (1996) Wavelets and Filter Banks (Wellesley: Cambridge Press).
[5] Coombes, K.R., Tsavachidis, S., Morris, J.S., Baggerly, K.A., Hung, M.C., and Kuerer, H.M. (2005) Improved peak detection and quantification of mass spectrometry data acquired from surfaceenhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform. Proteomics 5(16), 4107–4117.