Post on 18-Dec-2015
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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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Process of EMD (2/5)
• Find the envelope of the local minima
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Process of EMD (3/5)
• 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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Process of EMD (5/5)
• 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
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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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Baseline subtraction
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Baseline subtraction
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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
8
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
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
MSW Peak detected : 188M 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
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
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
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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Results of validation
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
Molecular weight : 5119 Da
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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
Molecular weight : 5119 Da
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The peaks of VrD1 detected by HHT modification + SpecAlign 0-5200Da
Whole
Molecular weight : 5119 Da
Double charge : (5119+2)/2
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Results of validation
• 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!