# mspeaks

Convert raw peak data to peak list (centroided data)

## Syntax

## Description

finds relevant peaks in raw, noisy peak signal data, and creates
`Peaklist`

= mspeaks(`X`

,`Intensities`

)`Peaklist`

, a two-column matrix, containing the separation-axis
value and intensity for each peak.

`[`

also returns `Peaklist`

,`PFWHH`

] = mspeaks(`X`

,`Intensities`

)`PFWHH`

, a two-column 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.

`[`

also returns `Peaklist`

,`PFWHH`

,`PExt`

] = mspeaks(`X`

,`Intensities`

)`PExt`

, a two-column 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(`

,
for any output variables, modifies the behavior of `X`

,`Intensities`

,`Name,Value`

)`mspeaks`

using one or more `Name=Value`

arguments. For example, obtain a
plot of the original signal, smoothed signal, and calculated peaks using
`mspeaks(X,Intensities,ShowPlot=true)`

.

## Examples

## Input Arguments

## Output Arguments

## Algorithms

`mspeaks`

converts raw peak data to a peak list (centroided data)
by:

Smoothing the signal using undecimated wavelet transform with Daubechies coefficients

Assigning peak locations

Estimating noise

Eliminating peaks that do not satisfy specified criteria

## References

[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 data-analytic strategy for protein biomarker discovery: profiling of
high-dimensional 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 surface-enhanced
laser desorption and ionization by denoising spectra with the undecimated discrete
wavelet transform. Proteomics *5(16)*, 4107–4117.

## Version History

**Introduced in R2007a**

## See Also

`mspalign`

| `msbackadj`

| `msdotplot`

| `msalign`

| `msheatmap`

| `mslowess`

| `msnorm`

| `msresample`

| `msppresample`

| `mssgolay`

| `msviewer`

### Topics

- Mass Spectrometry and Bioanalytics
- Preprocessing Raw Mass Spectrometry Data
- Visualizing and Preprocessing Hyphenated Mass Spectrometry Data Sets for Metabolite and Protein/Peptide Profiling
- Differential Analysis of Complex Protein and Metabolite Mixtures Using Liquid Chromatography/Mass Spectrometry (LC/MS)