A novel approach to denoising ion trap tandem mass spectra

A novel approach to denoising ion trap tandem mass spectra

by Jiarui Ding, Jinhong Shi, Guy Poirier, and Fang-Xiang Wu

University of Saskatchewan, Canada

Proteome Science 2009

Presenter : Kyowon Joeng

Why this paper?

• Related to my work (spectral pre-processing)

• A good summary on “features” of spectrum

• EASY

Outline

• Spectral pre-processing

• What they did

• Result

• Some features of spectrum

• Conclusion/criticism/discussion

Spectral pre-processing

• To increase the number of identified peptides• Spectrum clustering (Frank, J proteome Res 08; Tabb, Anal Chem 03)

• Precursor charge correction (Klammer, IEEE CSBC 05; Na, Anal

Chem 08)

• Denoising (Zhang, RCM 08) • Quality assessment (Na, J proteome Res 06; Bern, Bioinformatics 04)

• Need to be simple and fast• Need to be generic; otherwise, need to have a

killer application

What they did

• Denoising of spectrum • signal peaks: peaks from y or b ions• noisy peaks : other peaks

• Intensity normalization• Using interrelation features to assign Score to

each peak• New intensity = original intensity * Score

• Peak selection• Use morphological reconstruction filter• Select local maxima peaks

Intensity normalization : feature selection

• Score of a peak p is decided by 5 interrelation features

• F1 : # of peaks p’ such that |p-p’| = an a.a. mass (Good diff fraction)

• F2 : # of peaks p’ such that p+p’ = precursor mass (Complementary peaks)

• F3 : # of peaks p’ such that |p-p’| = H2O or NH3 mass (Neutral loss)

Intensity normalization : feature selection

• F4 : # of peaks p’ such that |p-p’| =CO or NH mass (Neutral loss)

• F5 : # of peaks p’ such that |p-p’| = isotope mass (Isotope)

• F1-F5 are normalized to have zero mean and one variance.

Intensity normalization : scoring

• Score = w0+w1F1+w2F2+w3F3+w4F4+w5F5

• w0 = 5 : Offset for non-negative score

• w1 = w2 = 1 : Good diff & complementary

• w3 = w4 = 0.2 : Neutral losses

• w5 = 0.5 : Isotope

• The weights are decided by referring to Sequest scoring function

Peak selection

• After intensity normalization, it is likely that signal peaks are local maxima.

• To select the local maxima, morphological reconstruction filter is adopted

Morphological filter

• State of the art filter in image processing

• Everyone used it at least one time; not so many knows it is the morphological filter.

• Flood Fill color tool = morphological filter

Morphological filter

• Given marker signal (or curve) and mask signal

• Dilate mask signal repeatedly until contour of dilated mask signal fits under marker signal.

• In each dilation, each point of marker signal takes the maximum value of its neighborhood.

Morphological filter

Dataset

• ISB : ESI ion trap 37,044 spectra

• TOV : LCQ DECA XP ion trap 22,576 spectra

• Database : ipi.Human protein database

• Mascot is used to evaluate denoising

Mascot parameters

Number of identified spectra

• Spectrum is identified if its Mascot ion score is larger than the identity threshold (no target decoy FDR is derived)

Number of identified spectra

False positive rate

• A spectrum in ISB dataset is false positive if it is identified in ipi.HUMAN database but it is not from the known 18 proteins.

Intensity normalization vs. peak selection

Features of spectrum

• Number of peaks• Total ion current (total intensity of a spectrum)• Good-Diff fraction• Total normalized intensity of peaks with

associated isotope peaks• Complements• Water losses• Signal to noise ratio

Features of spectrum

• The average intensity of the peaks• Total number of peaks having relative intensities

greater than x% of TIC

• Among them, only features considering m/z differences between peaks turned out to be significant. (Bern, Bioinformatics 04)

Conclusion

• A denoising algorithm that uses features of spectrum is introduced.

• It is simple and improves quality of spectrum

• 15-30% more spectra were identified by Mascot after denoising

Criticism : method

• Intensity normalization is too heuristic.• Among used features, neutral losses are often

observed in noisy peaks (e.g., precursor peaks).• Features were manually selected, and no new

feature was introduced.• The benefit of morphological filter is not clear.

Criticism : result

• Standard target-decoy analysis was not shown.• It is about denoising, but the result of denoising

is not directly shown.• Proposed scheme may not suitable for other

tools.• The running time of their algorithm is not shown;

only Mascot search time was shown.

Complement peaks associated with their intensities?

• For Discussion