A novel preprocessing method using Hilbert Huang transform for MALDI-TOF and SELDI-TOF mass spectrometry data 吳立青 1

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A novel preprocessing method using Hilbert Huang transform for MALDI-TOF and SELDI-TOF mass spectrometry data

吳立青

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Outline

• Introduction• Methods• Data source• Methods of comparison• Results• Conclusion

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Introduction

• Using protein mass spectrometry to discriminate diseased from healthy individuals becomes more popular

• MALDI-TOF and SELDI-TOF have the advantage :– Fast– High through-put– Accuracy– Protein ID

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SELDI-TOF MS applications in clinical oncology

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The example of ovarian cancer data

•Large scale

•Full of noise

•Nonlinear and non-stationary

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The analysis of mass spectra seems to be simple.However, we do suffer from several problems ,and they need to

be solved.

Motivation

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Common preprocessing step

• Baseline subtraction• Denoising : very important and also complicated !

• Normalization• Peak detection• Peak alignment

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Noise component

• Chemical noise : – From the matrix material and sample

contaminations– One kind of the biochemical material

• Electrical noise : The physical characteristics of the machine – Do not mean anything actually

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Chemical noise

• Chemical component (organic acid)– We call it Matrix

• Ionization– Provide H+ to peptide or protein for ionization and

flight in the machine• Protection

– Protect the peptide or protein in the process of laser flash

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Problem

• The simulated model before did not separate the chemical noise from spectra.

• The chemical noise mixed with the machine noise is worse.

• A novel preprocessing method should be developed

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Kwon, D., M. Vannucci, et al. (2008). "A novel wavelet-based thresholding method for the pre-processing of mass spectrometry data that accounts for heterogeneous noise." Proteomics 8(15): 3019-29.

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Goal

• Develop a method which can be satisfied– The electrical noise should be removed– Preserve the significant peaks even the chemical

noise

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Methods

• Hilbert Huang transform– Denoising

• Modification– Baseline subtraction– Rescale– Peak detection

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Flow chart

Ovarian cancer data HHT

IMFs

Remove IMFs

Modification

Baseline subtraction

Rescale

Peak detection

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Hilbert Huang transform

• Method : Hilbert Huang transform (HHT)– Wu, Z., N. E. Huang, et al. (2007). "On the trend, detrending, and variability of nonlinear and

nonstationary time series." Proc Natl Acad Sci U S A 104(38): 14889-94.

– An adaptive data analysis method for nonlinear and non-stationary processes

– The main feature of HHT is the empirical mode decomposition (EMD)

– After the process of EMD, we get the intrinsic mode functions and remove several from them as noise

• Goal : denoising

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Process of EMD (1/5)

• Find the envelope of the local maxima

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• Find the envelope of the local minima

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• Compute the mean envelope from the maximum envelope and minimum envelope

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Process of EMD (4/5)• We get IMF1 (i.e. h1) by subtracting the mean

envelope m1 from the original signal X(t)

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• We take IMF1 as X(t) and repeat the same process and so on.

• We terminate the process untill the number of the extrema and the zero-crossing of IMFn

differ by more than one

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IMFs : 1~16

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

x 104

0

1

2

3

4

5

6

7

8

9x 10

9

HHT

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Modification

• Baseline subtraction– Remove the systematic artifacts

• Rescale– Shift the scale to positive

• Peak detection– Key feature of the preprocessing method– We compare several popular methods

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Peak detection

• We use three popular algorithms for peak detection– MassSpecWavelet (Du, Kibbe et al. 2006)– SpecAlign (Wong, Cagney et al. 2005)– PROcess (Li 2005)

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Data source

• Source : National Cancer Institute• Type : 50 ovarian cancer data• Meuleman, W., J. Y. Engwegen, et al. (2008). "Comparison of normalisation methods for surface-enhanced

laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data." BMC Bioinformatics 9: 88

• Kwon, D., M. Vannucci, et al. (2008). "A novel wavelet-based thresholding method for the pre-processing of mass spectrometry data that accounts for heterogeneous noise." Proteomics 8(15): 3019-29

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Methods of comparison

• Judgment– Count of peaks detected– Real location of the peaks in visual

• Interior comparison– HHT and modification+SpecAlign– HHT and modification+PROcess– HHT and modification+MassSpecWavelet

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Methods of comparison

• Exterior comparison– SpecAlign

• Abbreviation : SA

– PROcess• Interpolation : PRO1• Regression : PRO2

– MassSpecWavelet• Abbreviation : MSW

– PRO2+MSW• As suggested in Cruz-Marcelo, Guerra et al. 2008

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Raw data

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Results after HHT and modification

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Results of interior comparison

• Results of interior comparison

Algorithm \ peak detected 2000~ 4000 DA

4000~6000 DA

6000~8000 DA

8000~10000 DA

10000~12000 DA

12000~14000 DA

14000~15000 DA Total

HHT modification +MassSpecWavelet 35 32 27 21 44 38 21 218

HHT modification+SpecAlign 18 17 6 11 8 9 3 80

HHT modification+PROcess 21 18 19 13 14 13 10 108

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安追

3

13

0 2000 4000 6000 8000 10000 12000 14000 160000

5

10

15x 10

8

Modified HHT+MSW Peak detected : 218M over z range : whole region

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安追

3

13

0 2000 4000 6000 8000 10000 12000 14000 160000

5

10

15x 10

8

Modified HHT+SpecAlign Peak detected : 80M over z range : whole region

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安追

3

13

0 2000 4000 6000 8000 10000 12000 14000 160000

5

10

15x 10

8

Modified HHT+PROcess Peak detected : 108M over z range : whole region

Significant peak lost

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Results of exterior comparison

• Results of exterior comparisonAlgorithm \ peak detected 2000~

4000 DA4000~6000 DA

6000~8000 DA

8000~10000 DA

10000~12000 DA

12000~14000 DA

14000~15000 DA Total

mHHT+MassSpecWavelet 35 32 27 21 44 38 21 218

mHHT+SpecAlign 18 17 6 11 8 9 3 80

mHHT+PROcess 21 18 19 13 14 13 10 108

SpecAlign 67 43 21 18 16 14 7 186

PRO1 46 24 19 18 16 16 6 145

PRO2 40 23 8 12 13 14 4 114

MassSpecWavelet 51 39 25 24 22 20 7 188

PRO2+MSW 54 37 33 25 23 18 8 198

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安追

3

13

0 2000 4000 6000 8000 10000 12000 14000 160000

5

10

15x 10

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PRO1 Peak detected : 145M over z range : whole region

Meuleman, W., J. Y. Engwegen, et al. (2008). "Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data." BMC Bioinformatics 9: 88

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安追

3

13

0 2000 4000 6000 8000 10000 12000 14000 160000

5

10

15x 10

8

PRO2 Peak detected : 114M over z range : whole region


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安追

3

13

0 2000 4000 6000 8000 10000 12000 14000 160000

5

10

15x 10

8

MSW Peak detected : 188M over z range : whole region


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安追

3

13

6000 6500 7000 7500 8000 85000

5

10

15x 10

8

MSWPeak detected : 25M over z range : 6000~8000

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安追

3

13

0 2000 4000 6000 8000 10000 12000 14000 160000

5

10

15x 10

8

PRO2+MSW Peak detected : 198M over z range : whole region


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安追

3

13

6000 6500 7000 7500 8000 85000

5

10

15x 10

8

PRO2+MSWPeak detected : 33M over z range : 6000~8000

Ex

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Results

• Interior comparison:– HHT and modification+MSW covers the most of

the peaks– HHT and modification+SpecAlign pick the most

important peaks• Exterior comparison:

– PROcess miss the significant peaks– MassSpecWavelet and PRO2MSW have many

redundancies

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Results of validation

• Validation– Data source : Cathay General Hospital– Experiments :

• Divide into three experiments– Water only– VrD1

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Water

Sample : waterOrganic acid : CHCA (<1000 DA)

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VrD1

Sample : VrD1Type : proteinOrganic acid : CHCA (<1000 Da)Molecular weight : 5119 Da

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Algorithm\sample Water VrD1 (Mw: 5119) Peak located in M/Z=5119 of VrD1

Double charge of VrD1 (5119+2)/2

Number of peaks (>1000Da) detected in

VrD1

MassSpecWavelet 400 369 Detected Detected 340

SpecAlign 391 477 Detected Detected 355

HHT modification+SpecAlign 17 22 Detected Detected 5

The amount of peaks which HHT modification removed 95.7% 94.8% 98.6%

Number of the peaks detected

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The peaks of Water detected by MassSpecWacelet

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The peaks of VrD1 detected by MassSpecWacelet

Molecular weight : 5119 Da

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The peaks of Water detected by SpecAlign

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The peaks of VrD1 detected by SpecAlign


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The peaks of water detected by HHT modification + SpecAlign

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The peaks of VrD1 detected by HHT modification + SpecAlign


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The peaks of VrD1 detected by HHT modification + SpecAlign 0-5200Da

Whole


Double charge : (5119+2)/2

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• MassSpecWavelet and SpecAlign do not remove the noise

• HHT and modification+SpecAlign detects the least peaks but the most significant peaks

• HHT and modification+SpecAlign removes 98.6% of the peaks (>1000Da) which are redudancies and noise

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Conclusion

• HHT performs well at denoising• As the result of comparison, HHT and

modification can make the raw data more simple

• Simultaneously, HHT and modification preserve the significant information.

• After the preprocessing of HHT and modification , it is suggested that detect the peaks by SpecAlign

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Acknowledgement

• 感謝本所陳欣昊同學的投入• 感謝黃鄂院士以及本所博士班林澂同學• 感謝汐止國泰醫院的鄭宇哲博士的實驗驗證

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Thanks for your attention!

Documents

A novel preprocessing method using Hilbert Huang transform for MALDI-TOF and SELDI-TOF mass spectrometry data 吳立青 1