GABORLOCAL: Peak Detection In Mass Spectrum …ranger.uta.edu/~heng/papers/CSB_2008.pdf · GABORLOCAL: PEAK DETECTION IN MASS SPECTRUM BY GABOR FILTERS AND GAUSSIAN LOCAL MAXIMA Nha

May 24, 2008 17:24 WSPC/Trim Size: 11in x 8.5in for Proceedings 105henghuang

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GABORLOCAL: PEAK DETECTION IN MASS SPECTRUM BY GABOR FILTERS ANDGAUSSIAN LOCAL MAXIMA

Nha Nguyen

Department of Electrical Engineering, University of Texas at Arlington, TX, USAEmail: [email protected]

Heng Huang∗

Department of Computer Science and Engineering, University of Texas at Arlington, TX, USA∗Email: [email protected]

Soontorn Oraintara


An Vo


Mass Spectrometry (MS) is increasingly being used to discover disease related proteomic patterns. The peak detectionstep is one of most important steps in the typical analysis of MS data. Recently, many new algorithms have beenproposed to increase true position rate with low false position rate in peak detection. Most of them follow twoapproaches: one is denoising approach and the other one is decomposing approach. In the previous studies, thedecomposition of MS data method shows more potential than the first one. In this paper, we propose a new methodnamed GaborLocal which can detect more true peaks with a very low false position rate. The Gaussian local maximais employed for peak detection, because it is robust to noise in signals. Moreover, the maximum rank of peaks isdefined at the first time to identify peaks instead of using the sinal-to-noise ratio and the Gabor filter is used todecompose the raw MS signal. We perform the proposed method on the real SELDI-TOF spectrum with knownpolypeptide positions. The experimental results demonstrate our method outperforms other common used methodsin the receiver operating characteristic (ROC) curve.

1. INTRODUCTION

Mass Spectrometry (MS) is an analytical techniquehas been widely used to discover disease related pro-teomic patterns. From these proteomic patterns,researchers can identify bio-markers, make a earlydiagnosis, observe disease progression, response totreatment and so on. Peak detection is one of mostimportant steps in the analysis of mass spectrum be-cause its performance directly effects the other pro-cessing steps and final results such as profile align-ment 1, bio-marker identification 2 and protein iden-tification 3.

There are two types of peak detection ap-proaches: denoising 4, 5 and non-denoising (or de-composing) 6, 7 approaches. There are several simi-

lar steps between these two approaches such as base-line correction, alignment of spectrograms and nor-malization. They also use local maxima to detectpeak positions and use some rules to quantify peaks.Specially, both approaches use the signal to noise ra-tio (SNR) to remove some small energy peaks whosetheir SNR values are less than a threshold. However,in the denoising approach, before detecting peaks, adenoising step is added to reduce the noise of massspectrum data. In the non-denoising approach, a de-composition step is used to analyze mass spectruminto different scales before the peak detection by lo-cal maxima. When the smoothing step is appliedinto the denoising approach, it possibly removes bothnoise and signal. If the real peaks are removed by

∗Corresponding author.


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smoothing step, they can never be recovered in theother processing steps. As a result, we lose someimportant information and introduce error into MSdata analysis. Thus, the way we decompose a signalinto many scales without denoising is a really betterapproach with great potentials.

The SNR is used to identify peaks in both de-noising and non-denoising methods. In paper 6, P.Du et al estimated the SNR in the wavelet spaceand got much better results than the previous work.But they still failed to detect the peak at 147300 andsome peaks with small SNR. This problem came fromthe SNR value estimation and all previous methodsestimated the SNR value by using the relationshipbetween the peak amplitude and the surroundingnoise level. Since some sources of noise can also havehigh amplitudes, the high amplitude peak does notalways guarantee to be real peak. On the other hand,some low amplitude peaks can also be real peaks. Itis clearly that the way using SNR to quantify peaksis not efficient and accurate. More details of thisproblem will be discussed in section 3.4. In this pa-per, we propose a new robust decomposing basedMS peak detection approach. We use the Gabor fil-ters to create many scales from one signal withoutsmoothing. The Gaussian local maxima is exploitedto detect peaks instead of the local maxima becausethe Gaussian local maxima method is more robust tothe noise of mass spectrum. Finally, we use the max-imum rank (MR) of peaks to remove some false peaksinstead of the SNR. The real SELDI-TOF spectrumwith known polypeptide composition and position isused to evaluate our method. The experimental re-sults show that our new approach can detect bothhigh amplitude and small amplitude peaks with alow false position rate and is much better than theprevious methods.

2. METHODS

In this section, we first introduce the basic knowledgeof Gabor filters. After that, our proposed methodwhich is a combination of the Gabor filters and theGaussian local maxima will be detailed. At last, wewill use one example to show how our method works.

2.1. Gabor Filters

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Fig. 1. The real parts of the uniform Gabor filters.

The Gabor filters 8 were developed to create Gaus-sian transfer functions in the frequency domain.Thus, taking the inverse Fourier transform of thistransfer function, we get a filter closely resemblingto the Gabor filters. The Gabor filters have beenshown to have optimal combined localization in boththe spatial and the spatial-frequency domain 9, 10. Incertain applications, this filtering technique has beendemonstrated to be robust and fast 11 and the recur-sive implementation of 1D Gabor filtering has beenshown in paper 12. This recursive algorithm for theGabor filter achieves the fastest possible implementa-tion. For a signal consisting of N samples, this imple-mentation requires O(N) multiply-and-add (MADD)operations. A generic one dimensional Gabor func-tion and its Fourier transform are given by:

h(t) =1√2πσ

exp(− t2

2σ2) exp(j2πFit), (1)

H(f) = exp(− (f − Fi)2

2σ2f

), (2)

where σf = 1/(2πσ) represents the bandwidth of thefilter and Fi is the central frequency.

The Gabor filter can be viewed as a Gaussianmodulated by a complex sinusoid (with centre fre-quencies Fi). This filter responds to some frequency,but only in a localized part of the signal. The co-efficients of Gabor filters are complex. Therefore,


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the Gabor filters have one-side frequency support asshown in Fig. 2 and Fig. 4. We also illustrate thereal parts of the Gabor filters in Fig. 1 and Fig.3.

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Fig. 2. Frequency supports of the uniform Gabor filters.

Given a certain number of subbands, in orderto obtain a Gabor filter bank, the central frequen-cies Fi and bandwidths σf of these filters are chosento ensure that the half-peak magnitude supports ofthe frequency responses touch each other as shownin Fig. 2 and Fig.4. The Gabor filter bank can bedesigned to be uniform (in Fig. 2) or non-uniform (inFig. 4). In our experiments, we use the Gabor filterbank with nine non-uniform subbands.

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Fig. 3. The real parts of the non-uniform Gabor filters.

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After decomposing a MS signal, nine subbandsare created as follows:

yi(t) = hi(t) ∗ x(t), (3)

where x(t) is the input signal, i = 1,2,...,9, and ∗ isthe 1D convolution. This is an over-complete repre-sentation with the redundant ratio of 9.

2.2. GaborLocal Method

MS data

Full Frequency MS Signal A Full Frequency MS Signal B

Pre-processing Pre-processing

Gaussian Local Maxima

Gaussian Local Maxima

Number of Peaks Number of Peaks

Intersection

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Baseline Correction

Fig. 5. Flowchart of Gabor-Gaussian local maxima methodfor peak detection in the MS data.


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Our main idea is to amplify the true signal and com-press the noise of mass spectrum by using the Gaborfilter bank. After that, we use the Gaussian localmaxima to detect peaks and the maximum rank ofpeaks which will be defined later to quantify peaks.This method is named as Gabor filter - Gaussian lo-cal maxima (GaborLocal). Fig. 5 is the flowchart ofour GaborLocal method. The GaborLocal can be de-tailed into the four steps including the full frequencyMS signal generation, the peak detection, the peakquantification, and the intersection.

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Fig. 6. The frequency response of three raw MS signals -the 17th, 19th and 39th MS data of the CAMDA 2006.

2.2.1. Full frequency MS signal generation

Mass spectrum is decomposed to many scales by us-ing the Gabor filters after the baseline correction.Our purpose is to emphasize some hidden peaksburied by noise. When we analyze 60 MS signals ofthe CAMDA 2006 in the frequency domain, we noticethat the valuable information of these signals locatefrom zero to around 0.06 (rad/s), and the noises lo-cate from 0.06 to π (rad/s). The frequency responsesof three raw MS data (the 17th, 19th and 39th MSdata of the CAMDA 2006) are shown in Fig. 6 as anexample. Therefore, the bandwidth σf of the Gaborfilters which enhances peaks must be less than 0.06.In our experiments, we use σf = 0.01. If the uniformGabor filter is used, the number of scales must be

N =π

0.01≈ 314 scales. (4)

With 314 scales in (4), we know that the uni-

form Gabor filter is not efficient. If the non-uniformGabor filter is used, the number of scales should becalculated as follows:

σf =π

2N,

N = log2(π

σf),

N ≈ 8.3 scales with σf = 0.01. (5)

Based on the Eq. (5), we use the non-uniformGabor filters with 9 scales to decompose the MS data(we use CAMDA 2006 data 13 for experiments). Ifwe transform yi(t), hi(t) and x(t) in Eq. (3) into thefrequency domain, we get

Yi(f) = X(f).Hi(f), (6)

where X(f) is the frequency response of the raw MSsignal, Hi(f) is the frequency response of the ith

Gabor filter, and Yi(f) is the frequency response ofthe ith scale. After getting 9 signals according to 9frequency sub-bands in complex values, the full fre-quency signal A will be created by summing abovesignals in complex values first and taking their abso-lute values at the final. To create the full frequencysignal B, we take the absolute values for each sub-band and then sum all these sub-bands. After thisstep, we have two full frequency signals A and B.Let’s denote y(t) and Y (f) as the full frequency sig-nal in time domain and frequency domain, respec-tively.

Y (f) =∑

i=Ni

Yi(f), (7)

where Ni are the scales which are used to create thefull frequency signal. From Eq. (6) and (7), we get

Y (f) =∑

i=Ni

X(f)Hi(f)

= X(f)∑

i=Ni

Hi(f) = X(f)Hs(f), (8)

where Hs(f) =∑

i=NiHi(f) is called the summary

filter. From Eq. (2), the summary filter can formu-lated as follows

Hs(f) =∑

i=Ni

exp(− (f − Fi)2

2σ2f

). (9)

Our purpose in this step is to amplify the true signaland compress the noise. The black line in the Fig. 7is Hs(w) which can amplify the true signal from 0 to0.06 rad

s and compress noise from 0.06 to π. In this


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case, if we use Ni = [1 2 ... 9] we can get the sum-marized filter represented by the blue line in Fig. 7.The Fig. 9 shows the frequency response of the 19th

raw MS signal (blue line) and that of full frequencysignal (red line). We can see that the signal from 0 to0.06 is amplified and the noise from 0.06 to π is com-pressed. Therefore, in both full frequency MS signalA and B, all peaks have been emphasized to helpthe next peak detection step. In this step, baselinecorrection is also used and is detailed as follows

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Fig. 7. The frequency response of the summary filter.

Baseline correction The chemical noise or theion overloading is the main reason causing a varyingbaseline in mass spectrometry data. Baseline correc-tion is an important step before using Gabor filterto get the full frequency MS signals. The raw MSsignal xraw includes some real peaks xp, the baselinexb, and the noise xn.

xraw = xp + xb + xn. (10)

The baseline correction is used to remove the arti-fact xb. In this paper, we use ‘msbackadj’ function ofMATLAB to remove baseline. The msbackadj func-tion estimates a low-frequency baseline first which ishidden among the high-frequency noise and the sig-nal peaks and then subtracts the baseline from thespectrogram. This function follows the algorithms inAndrade et al.’s paper 14.

Illustration In order to understand this stepeasier, one example of the way to create full fre-

quency MS signal is shown in Fig. 8.

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Fig. 8. One example of the step named full frequency MSsignal generation. Raw MS data is the 19th MS signal ofCAMDA 2006.

In this example, the 19th MS signal of CAMDA2006 is chosen as raw MS data. After the baselinecorrection, MS signal is used as the input of the Ga-bor filters. A Gabor filter bank with 9 non-uniformsub-bands is employed to create 9 MS signals with 9different frequency sub-bands. In Fig. 8, the signalsof scale 1, 4, 8 and 9 are visualized. Some noisesin high frequency are separated from the MS signalof the scale 1, 2, ..., 5. In the MS signal underthe scales 6, ..., 9, all high intensity peaks are stillkept. After combining the MS signals of all scales intwo ways, the full frequency MS signal A and B arecreated. The comparison between the raw MS andfull frequency signal in frequency domain is shownin Fig. 9. This figure shows our purpose which am-plifies the important signal and compresses the noise


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has been achieved. We should remember that thisis just a compression of noise instead of removingnoise. As the outputs, two full frequency MS signalA and B will be used to detect peaks in the next stepinstead of raw MS data.

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Fig. 9. The frequency response of the 19th MS signal ofCAMDA 2006 before and after using the summary filter.

2.2.2. Peak detection by Gaussian local

maxima

All peaks are detected as many as possible by us-ing Gaussian local maxima with the full frequencyMS signal A as well as the full frequency MS sig-nal B. The Gaussian local maxima is used insteadof local maxima because Gaussian local maxima isrobust with noise in peak detection. Before detect-ing peaks, pre-processing step is also applied such aselimination peaks in the low-mass region. Now, theGaussian local maxima will be introduced as followsGaussian local maxima We assume that we wantto find local maxima of y(t). We should follow twosteps: computing derivative of y(t) and finding zerocrossing. The derivative of y(t) is approximated bythe finite difference as follows:

d(y(t))dt

= limh−>0

y(t + h)− y(t)h

≈ y(t + 1)− y(t).

(11)At t = t0, if the derivative of y(t) equals to zeroand has a change from positive to negative or fromnegative to positive, we have zero-crossing. If thederivative of y(t) changes from positive to negativeat t0, we have local maxima at t0. With discrete

signal, (11) can be rewritten as follows

d(y(n))dn

= y(n + 1)− y(n) = y(n) ∗ [1 − 1]. (12)

Unfortunately, MS data always have noise. Thus, weassume that Gaussian filter g(t, σ) is used to handlethe denoise in MS data (this is not a denoising step).Finally, derivative of y(t) ∗ g(t, σ) will replace thederivative of y(t) as follows

d(y(t) ∗ g(t, σ))dt

=d(

∫(y(τ).g(t− τ, σ)dτ))

dt

=∫

(y(τ).d(g(t− τ, σ))

dtdτ) = y(t) ∗ d(g(t, σ))

dt,

(13)where

g(t, σ) = exp(− t2

2σ2). (14)

Taking the derivative of g(t, σ) in (14), we have

d(g(t, σ))dt

=−t

σ2exp(− t2

2σ2). (15)

From (13) and (15), we have

d(y(t) ∗ g(t, σ))dt

= y(t) ∗ (−t

σ2exp(− t2

2.σ2)). (16)

Instead of finding zero crossing of d(y(t))dt , we find

zero-crossing of d(y(t)∗g(t,σ))dt by (16). With discrete

signal, (16) can be rewritten as follows

d(y(n) ∗ g(n, σ))dn

= y(n) ∗ v(n), (17)

where v(n) is listed in the table 1. Using Gaussian fil-ters makes the Gaussian local maxima method morerobust with noise.

2.2.3. Peak quantification by maximum

rank

After detecting many peaks in full frequency MS sig-nals, a new signal is obtained from these peaks. Thisnew signal will be the input of the next peak detec-tion loop where the Gaussian local maxima methodis also applied. Then, many loops are repeated untilthe number of peaks obtained is less than a thresh-old. Now, we define the maximum rank of peaks asfollows:

Maximum rank We assume n loops are usedand get m1 peaks at the loop 1, m2 peaks at loop2,...and mn peaks at the loop n. We have m1 >


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Table 1. The value of vector v(n) with different lengths.

length n = 1 2 3 4 5 6 7 8 9 10

5 0.0007 0.2824 0 -0.2824 -0.00076 0.0007 0.1259 0.7478 -0.7478 -0.1259 -0.00077 0.0007 0.0654 0.6572 0 -0.6572 -0.0654 -0.00078 0.0007 0.0388 0.4398 0.6372 -0.6372 -0.4398 -0.0388 -0.00079 0.0007 0.0254 0.2824 0.7634 0 -0.7634 -0.2824 -0.0254 -0.000710 0.0007 0.0180 0.1851 0.6572 0.5329 -0.5329 -0.6572 -0.1851 -0.0180 -0.0007

Table 2. Definition of maximum rank of peaks. Y means that the peak can be de-tected at that loop. N means that the peak can not be detected at that loop. The peakwith the maximum rank equaling to 1 is able to be detected at all of the loops. Thepeak with the maximum rank equaling to n only appeared at the first loop.

Maximum Rank Loop 1 Loop 2 Loop 3 Loop 4 ... Loop (n− 1) Loop n

1 Y Y Y Y ... Y Y2 Y Y Y Y ...Y N... ... ... ... ... ... ...n Y N N N ...N N

m2 > ... > mn. Maximum peak (MR) is defined asthe table 2.

We have mn peaks with MR = 1, mn−1 − mn

peaks with MR = 2,...and m1 − m2 peaks withMR = n. In our algorithm, the probability of thetrue peaks with MR = i is higher than with MR > i.

Demonstration Fig. 10 shows an example ofthe step named the peak quantification by using themaximum rank. First, the full frequency MS signal Ais used to detect peaks by using Gaussian local max-ima. At the loop 1, we can detect 1789 peaks. Fromthese 1789 peaks, we create a new signal with 1789positions. At the next loops 2, 3, 4, we can detect509, 143, 39 peaks, respectively. At the loop 5, 15peaks can be detected. Because we choose a thresh-old of 16 and number of peaks = 15 < 16, we stopat the loop 5. Actually, we can select the thresholdfrom 38 to 16 and also get 15 peaks at the final loop.Now, we get 15 peaks with MR = 1, 39 − 15 = 24peaks with MR = 2, 143 − 39 = 104 peaks withMR = 3, 509 − 143 = 366 peaks with MR = 4 and1789−509 = 1280 peaks with MR = 5. In this case,we only keep 15 peaks with MR = 1. We also do thesame on the full frequency MS signal B and can get12 peaks with MR = 1 at the last loop.

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Fig. 10. One example of the step named peak detection andquantification.


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2.2.4. Intersection

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Fig. 11. One example of the step named intersection.

Now, we have two results of peak detection fromtwo full frequency MS signals. The intersection oftwo above results will be the final result. For exam-ple, Fig. 11 shows how to do the intersection of tworesults. We have 15 peaks in the signal A and 12peaks in the signal B but we just get 9 peaks as thefinal result. With this result, we get 7 true peaksand 2 false peaks. This result shows that the trueposition rate equal to 7

7 = 1 and the false positionrate equal to 2

9 ≈ 0.22.

3. EXPERIMENTAL RESULTS ANDDISCUSSIONS

In this section, our GaborLocal method will be com-pared to two other most common used methods: theCromwell 4, 5 and the CWT 6. We will evaluatethe performance of three methods by using the ROCcurve that is the standard criterion in this area.

3.1. Cromwell Method

Cromwell method is implemented as a set of MAT-LAB scripts which can be downloaded from 15. Thealgorithms and the performance of the Cromwellwere described in 5, 4. The main idea of the Cromwellmethod can be summarized as follows

(a) Denoise the individual spectrum using the un-decimated discrete wavelet transform. The hardthresholding method was used to reset smallwavelet coefficients to zero. In these papers,the authors used the median absolute deviation(MAD) to estimate the thresholding.

(b) Estimate and remove the baseline artifact byusing a monotone local minimum curve on thesmoothed signal.

(c) Normalize the spectrum by dividing the total ioncurrent, defined to be the mean intensity of thedenoised and baseline corrected spectrum.

(d) Identify peaks by using local maxima and signalto noise ratio (SNR).

(e) Match peaks across spectrum and quantify peaksusing either the intensity of the local maximumor computing the area under the curve for theregion defined to be the peaks.

3.2. CWT Method

The algorithm of CWT method has been imple-mented in R (called as ‘MassSpecWavelet’) and theVersion 1.4 can be downloaded from 16. This methodwas proposed by Pan Du et al. 6 in 2006 and can besummarized as follows:

(a) Identify the ridges by linking the local maxima.Continuous wavelet transform (CWT) is used tocreate many scales from one mass spectrum. Thelocal maxima at each scale is detected. The nextstep is to link these local maxima as lines.


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(b) Identify the peaks based on the ridge lines.There were three rules to identify the majorpeaks. They are the scale with the maximumamplitude on the ridge line, the SNR being largerthan a threshold and the length of ridge beinglarger than a threshold. We should notice thatthe SNR is estimated in the wavelet space. Thisis a nice motivation of this method.

(c) Refine the peak parameter estimation.

3.3. Evaluation Using ROC Curve

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Fig. 12. Detailed receiver operating characteristic (ROC)curves obtained from 60 MS signals using Cromwell, CWT,and our GaborLocal method. The sensitivity is the true posi-tion rate.

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Fig. 13. Average receiver operating characteristic (ROC)curves obtained from 60 MS signals using Cromwell, CWT,and our GaborLocal method. The sensitivity is the true posi-tion rate.

The CAMDA 2006 dataset 13 of all-in-1 ProteinStandard II (Ciphergen Cat. # C100−007) is used toevaluate three algorithms: the Cromwell, the CWT,and our method. Because we know polypeptide com-position and position, we can estimate the true po-sition rate (TPR) and the false position rate (FPR).Another advantage of this dataset is that they arereal data and better than the simulated data in eval-uation.

The TPR is defined as the number of identifiedtrue peaks divided by the total number of true peaks.The FPR is defined as the number of falsely iden-tified peaks divided by the total number of iden-tified peaks. We call an identified peak as truepeak if it is located within the error range of 1%of the known m/z value of true peaks. There areseven polypeptides which create seven true peaksat 7034, 12230, 16951, 29023, 46671, 66433 and147300 of the m/z values. Fig. 12 shows the TPRand the FPR of three above methods with an as-sumption that there is only one charge. To calcu-late the ROC curve of Cromwell and CWT meth-ods, the SNR thresholding values are changed. TheSNR thresholding values are chosen from 0 to 20 forCromwell method, from 0 to 65 for CWT method.In our GaborLocal method, the threshold of num-ber of peaks is changed from 2000 to 10 to createthe ROC curve. In the Fig. 12, the performanceof Cromwell method is much worse than CWT andour GaborLocal methods. Most of ROC points ofCromwell method locate at the bottom of right cor-ner and most of ROC points of CWT and Gabor-Local methods are well placed on the top regions.In our method, some ROC points appear at the topline with TPR = 1 and some ROC points go withTPR = 1 and FPR = 0. However, it does not hap-pen to the CWT. Therefore, GaborLocal is the bestone.

If we take the average of those detailed ROC re-sults of Fig. 12, we get the average ROC curve asthe Fig. 13. We should notice that we take aver-age of all ROC points with the same SNR thresh-old (for Cromwell and CWT) and with the samepeak threshold (for our method). From the Fig. 13,the results of our method and CWT are much bet-ter than the Cromwell’s one. Therefore, the decom-posing approach without smoothing (both SWT and


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GaborLocal) is more efficient than the denoising ap-proach (like Cromwell). At the same FPR, the TPRof our method is consistently higher than the TPR ofCWT. Because the maximum rank was used to iden-tify peaks in the GaborLocal method instead of theSNR. It is clear that the utilizing maximum rank toidentify peak gives out valuable results. This methodhas a significant contribution to detect both high en-ergy and small energy peaks. The other advantage ofthis method is that the threshold of number of peakscan be created easier than the SNR. Therefore, theGaborLocal method is an more efficient and accuratemethod for real MS data peak detection.

3.4. Examples

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Fig. 14. The ROC curve of three methods such as Cromwell,CWT, and our method with the 17th mass spectrum signal

Now, we study one example shown in Fig. 15 in whichthe 17th spectrum signal of CAMDA 2006 dataset ispicked and tested with three above methods. Fig. 15(a) includes four sub-figures. The first sub-figure de-scribes the real peak positions and raw data. Thesecond and third sub-figure show the full frequencyMS signal A&B with identified peaks. The last sub-figure is the final result after doing intersection.

We get 12 peak candidates from the full fre-quency MS signal A and 10 peak candidates fromthe full frequency MS signal B. Finally, we get 7 peakcandidates after intersection of 12 and 10 peaks. Inthe result, our method can detect exactly 7 peaksover 7 true peaks. Fig. 15 (b) shows 9 detectable

peaks from CWT method. Among 9 above peaks,there are only 5 true peaks. The CWT loses twopeaks at 7034 and 147300 of the m/z values. Fig. 15(c) shows the result of Cromwell’s method. There arethree true peaks being detectable by this method.Some peaks with low SNRs can not be detected.Of course, if we decrease the SNR threshold, morepeaks can be detected. However, we also get morefalse peaks and the FPR will be increased dramati-cally. In general, if the thresholding values of threeabove methods are changed, we can get the ROCcurve in Fig. 14. From this figure, the performanceof our method keeps TPR = 1 with any value of theFPR (from 1 to 0). However, the TPR’s values ofCromwell and CWT methods decrease very quicklywhen the FPR’s value decreases. At the FPR = 0,the TPR of Cromwell method equals 0.1429. InCWT method, even the FPR ≈ 1, the TPR onlyequals to 0.8571. The CWT and Cromwell methodsare limited in peak detection performance becauseof the way using the SNR to identify peaks. Fig. 14and Fig. 15, we can prove that

(1) Decomposition of MS data makes peak detectioneasier.

(2) Using SNR to identify peaks can not detect lowSNR peaks.

(3) Using the MR can detect more true peaks thanusing the SNR.

4. CONCLUSION

In this paper, we proposed a new approach to solvepeak detection problem in MS data with promis-ing results. Our GaborLocal method combines theGabor filter with Gaussian local maxima approach.The maximum rank method is presented and used atthe first time to replace the previous SNR methodto identify true peaks. With real MS dataset, ourmethod gave out a much better performance in theROC curve compared to two other most commonused peak detection methods. In our future work,we will develop new protein identification methodbased on our GaborLocal approach.

References

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2. J. e. Li, “Independent validation of candidate breastcancer serum biomarkers identified by mass spec-trometry,” Clin Chem, vol. 51, pp. 2229–2235, 2005.

3. T. e. Rejtar, “Increased identification of peptides byenhanced data processing of high-resolution malditof/tof mass spectra prior to database searching,”Anal Chem, vol. 76, pp. 6017–6028, 2004.

4. J. Morris, K. Coombes, J. Koomen, K. Baggerly,and R. Kobayashi, “Feature extraction and quan-tification for mass spectrometry in biomedical appli-cations using the mean spectrum,” Bioinformatics,vol. 21, no. 9, pp. 1764–1775, 2005.

5. K. Coombes and et al., “Improved peak detectionand quantification of mass spectrometry data ac-quired from surface-enhanced laser desorption andionization by denoising spectra with the undec-imated discrete wavelet transform,” Proteomics,vol. 5, no. 16, pp. 4107–4117, 2005.

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overview and applications,” IEEE Transactions onImage Processing,, vol. 15, no. 5, pp. 1088–1099, May2006.

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15. U. M. A. C. Center, “The new model processor formass spectrometry data.” [Online]. Available: http://bioinformatics.mdanderson.org/cromwell.html

16. P. Du, “Mass spectrum processing bywavelet-based algorithms.” [Online]. Avail-able: http://bioconductor.org/packages/2.1/bioc/html/MassSpecWavelet.html


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Documents

GABORLOCAL: Peak Detection In Mass Spectrum …ranger.uta.edu/~heng/papers/CSB_2008.pdf · GABORLOCAL: PEAK DETECTION IN MASS SPECTRUM BY GABOR FILTERS AND GAUSSIAN LOCAL MAXIMA Nha