Methods for Quality Control of Low-resolution MALDI-ToF Spectra

Michal Marczyk and Joanna Polanska

Data Mining Group, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Techonology,

Akademicka 16, Gliwice, Poland

Keywords:

MALDI-ToF, Protein Proﬁling, Quality Control.

Abstract:

Protein proﬁling of human blood serum or plasma using MALDI-ToF mass spectrometry may be used for

identiﬁcation of candidates for disease biomarkers. Due to many biological and technical difﬁculties emerging

during preparation of the sample and spectra measurement quality control step is becoming important. In

this study we compared different methods for ﬁnding low quality spectra based on the Pearson correlation

coefﬁcient and proposed two novel solutions. First one utilizes information about area under the measured

spectrum and other incorporates modeling of signal-to-noise ratio of spectra intensity by mixture of Gaussians.

Obtained results show that removing of outlying samples increases the similarity of spectra obtained within

the same experimental conditions. What is more important it increases reproducibility of peak detection by

decreasing the coefﬁcient of variation of peaks intensities within a group and increasing its prevalence. This

work shows that appropriate identiﬁcation and removing of low quality spectra is a necessary step in analysis

of mass spectrometry data and proposed tools are appropriate for quality control of MALDI-ToF data.

1 INTRODUCTION

Matrix-assisted laser desorption and ionization time

of ﬂight (MALDI-ToF) mass spectrometry (MS) can

detect components at a very low concentration lev-

els, which offers opportunities to discover diagnos-

tic markers for a number of major diseases includ-

ing cancer. Human blood serum or plasma may cap-

ture proteins and their fragments released from all

organs and tissues in different conditions. Investi-

gating of serum/plasma proteome using MALDI-ToF

MS called proteome proﬁling can be used for iden-

tiﬁcation of panels of marker candidates (Palmblad

et al., 2009). Due to requirements of low cost and

high speed of measurements obtained spectra are low-

resolution data and do not contain the isotopic enve-

lope of peaks.

Highly automated acquisition of a large number of

data increases the risk of receiving poor quality sig-

nals. MALDI-TOF results may also be weakened by

inadequate deposition of the sample on the plate, poor

cleaning of the plate between runs and other technical

factors. Quality control (QC) is a ﬁrst important step

in data analysis. Detection of the spectra with low

signal-to-noise ratio (SNR) allows eliminating heav-

ily noisy data from further analysis preventing false

discoveries of candidate markers which can lack bio-

logical relevance. It is important to properly exclude

outlying spectra to guarantee the reliability of discov-

ered patterns and reproducibility of MALDI-ToF ex-

periments.

The easiest and fastest way of quality control is the

visual inspection of measurements heat maps. Dif-

ferent colors are used to depict the intensity of indi-

vidual spectra, which are arranged in successive rows

on the graph (Figure 1). In the proteome proﬁl-

ing using human specimens most major peaks should

be visible on all spectra, so it is possible to visu-

ally determine outliers. This method is also effec-

tive to check the quality of calibration of the mass

spectrometer. Another way of QC is to use the area

under the curve of the whole spectrum (total ion cur-

rent, TIC). Number of ions that reach the detector at

the same experimental conditions and with use of the

same laser power should be similar. It is assumed

that only small deviations in the values of TIC for

the spectra obtained in one experiment should be ob-

served. A useful approach to check data similarity

is to calculate the Pearson correlation coefﬁcients be-

tween the raw spectra, and then plot a correlation ma-

trix (Hong et al., 2005) or use the diagnostic plots

(Whistler et al., 2007) to ﬁnd spectra which measure-

ments are different from the others. Another group

of methods involves placement of control signals in

analyzed sample. Such information may be used to

check only existence of control peaks or in more so-

172

Marczyk M. and Polanska J..

Methods for Quality Control of Low-resolution MALDI-ToF Spectra.

DOI: 10.5220/0004804201720177

In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS-2014), pages 172-177

ISBN: 978-989-758-012-3

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

phisticated methods like principal component analy-

sis models. It was proved that using statistical meth-

ods on control peaks is more reliable and suitable for a

large number of spectra (Coombes et al., 2003). Since

most human cells, despite the variety of functions in

the body, are very similar to each other in terms of

its structure, it is possible to deﬁne a standard set of

proteins that are present in most experimental condi-

tions. In this way we can deﬁne a positive control

for start-up of the mass spectra, which may indicate

poor performance of real peak detection (Slany et al.,

2009).

Figure 1: Heat map for 3 cancers dataset.

In this study we investigated methods for ﬁnding

the low quality MALDI-ToF spectra which can be

used directly on the measured signals as a ﬁrst step

of mass spectra analysis pipeline. We chose two pro-

cedures from the literature that are using the Pear-

son correlation coefﬁcient and introduced two new

algorithms: one using an outlier detection algorithm

on TIC and one using a Gaussian mixture modeling

(GMM) on a robust version of SNR. We analyzed the

effect of using different quality control algorithms on

the integrity of the spectra within a group and its in-

ﬂuence on performance of peak detection.

2 MATERIALS AND METHODS

2.1 Data

Three different MALDI-ToF datasets obtained for

proteomic proﬁling were used to compare quality

control algorithms: 3 cancers dataset, larynx dataset

and ovarian dataset. First dataset consists of 120 can-

cer patients from which 35 patients are with squa-

mous cell cancer located in head and neck region

(HNC), 35 patient are with colorectal cancer (CRC)

and 50 patients are with non-small cell lung cancer

(LC) and 45 volunteers in control group. Samples

were collected before the start of a therapy of patients

and measured using 4 technical replicates of the same

sample giving 659 spectra (1 damaged ﬁle). Spec-

tra were recorded in the mass range between 2 and 13

kDa (Pietrowska et al., 2012). Second dataset consists

of 54 patients with a squamous cell cancer located at

larynx. Blood samples were collected before the start

of therapy (sample A), 2 weeks after the start of ra-

diotherapy (sample B) and 46 weeks after the end of

radiotherapy (sample C). Not all patients had taken

samples B and C. There were 4 technical replicates

of each sample giving 620 spectra in total. Spectra

were recorded in the mass range between 2 and 13

kDa (Widlak et al., 2011). Third dataset consists of

93 ovarian cancer patients and 77 normal blood serum

samples. Spectra were recorded in the mass range be-

tween 3.5 and 20 kDa (Wu et al., 2006).

3 cancers and larynx datasets were kindly pro-

vided by the authors of the original research

articles (Pietrowska et al., 2012)(Widlak et al.,

2011) only for testing different QC algorithms.

Ovarian cancer dataset was downloaded from

http://bioinformatics.med.yale.edu/MSDATA2.

2.2 Spectra Pre-processing

Mass spectrum pre-processing involves removal of a

measurement error, while retaining the essential bio-

logical information contained in the sample. It is an

obligatory step to appropriately extract signal peaks

which describe composition of analyzed material (Hi-

lario et al., 2006). First, spectra are resampled to

a common M/Z interval. Next, in baseline correc-

tion step we estimate the noise within multiple shifted

window and regresses the varying baseline to the win-

dow points using a spline approximation. Spectra are

aligned using peak alignment by fast Fourier trans-

form algorithm (Wong et al., 2005) modiﬁed by in-

troducing values of parameters to be relative to mean

M/Z of analyzed segment. To detect signal peaks

we used algorithm based on transforming signal into

wavelet space from MassSpecWavelet package (Du

et al., 2006).

2.3 Algorithms for QC

In (Hong et al., 2005) a correlation matrix (Figure

2) was developed as a QC tool in surface-enhanced

laser desorption/ionization (SELDI). It is a mass spec-

trometry method which differs to MALDI-ToF only

in ionization phase, but measured signals have simi-

lar shapes and properties. The rationale behind this

approach is to assume that the protein expression pro-

ﬁles of samples obtained in the same experimental

MethodsforQualityControlofLow-resolutionMALDI-ToFSpectra

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Table 1: Comparison of different quality control methods using a percent of deleted spectra and the median similarity in three

datasets. CRC - colorectal cancer, CTR - control group, HNC - head and neck cancer, LC - lung cancer, A,B,C - groups

representing different time points, CTR - control group, OVC - ovarian cancer. None - results without quality control, Corr -

method based on a correlation matrix, DP - method using the diagnostic plot, TIC - method based on a total ion current, SNR

- method based on a signal-to-noise ratio of spectrum intensities.

3 cancers Larynx Ovarian

Measure Method CRC CTR HNC LC A B C CTR OVC

Deleted Corr 3.57 3.33 7.86 16.08 0.00 1.89 3.13 2.60 1.08

spectra DP 3.57 3.33 7.86 16.08 0.00 0.00 2.08 2.60 1.08

[%] TIC 3.57 3.89 7.86 16.08 0.46 0.94 0.52 7.79 4.30

SNR 3,57 6.11 7.86 17.09 8.33 8.96 5.21 38.96 25.81

Median none 0.653 0.765 0.576 0.564 0.779 0.781 0.803 0.613 0.647

similarity Corr 0.680 0.785 0.650 0.695 0.779 0.791 0.815 0.632 0.652

DP 0,680 0.785 0.650 0.695 0.779 0.781 0.811 0.632 0.652

TIC 0,680 0.788 0.650 0.695 0.778 0.781 0.804 0.662 0.640

SNR 0,680 0.801 0.650 0.697 0.777 0.780 0.804 0.757 0.695

conditions should be comparable and thus that the

correlation among spectra intensities should be high.

Authors of algorithm suggest that the inter-spectral

correlation coefﬁcient values of R from 0.95 to 0.97

are attainable and representative ﬁgure-of-merit for

quality data in analysis of SELDI sample replicates.

Due to the complexity of protein signals from human

specimens and individual variability of the patients,

we set new thresholds for the correlation coefﬁcients

using clustering procedure (Figure 3). Hierarchical

clustering was performed on the mean correlation co-

efﬁcients of the spectra intensities using a shortest Eu-

clidean distance metric. Application of other distance

metrics in most cases give similar results. Optimal

number of clusters was found by maximizing a sil-

houette measure. Sample with mean correlation co-

efﬁcient belonging to the most frequent cluster are

treated are high quality spectra. The remaining spec-

tra are suspected for poor quality and removed from

further analysis.

Figure 2: A correlation matrix for larynx dataset.

Another application of the idea of measuring a re-

lationship between spectra intensities within a group

is creating the diagnostic plots (Figure 4). Algo-

0.2 0.3 0.4 0.5 0.6 0.7 0.8

100

120

140

160

Mean Pearson correlation coeff.

Counts

10/620 spectra removed

Figure 3: Histogram of the mean Pearson correlation coefﬁ-

cient for larynx dataset with threshold value marked by the

dashed line.

rithm of (Whistler et al., 2007) starts with generating

a pairwise similarity matrix using the Pearson corre-

lation coefﬁcient on normalized intensity values for

each spectrum. To visually depict the data, the diag-

nostic plot is drawn as 1 minus the mean of Pearson

correlation coefﬁcients against the range of the corre-

lation coefﬁcients. In our implementation due to high

non-normality of distribution of the correlation coef-

ﬁcient we introduced robust measures of location and

dispersion such as the median value and the interquar-

tile range instead of the mean and a range. Authors

of the algorithm established values of cut-off for 1-

the mean of correlation coefﬁcient by comparing the

results depicted in the diagnostic plots to other eval-

uation methodologies, such as principal component

analysis of the processed spectra or SNR, and by com-

paring the number of peaks in each spectrum with the

average number of peaks for all spectra in the dataset.

In this paper to ﬁnd the cut-off value we introduced

a method based on a hierarchical clustering described

in a previous paragraph.

The number of measured ions for spectra in the

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0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0.1

0.2

0.3

0.4

0.5

1−median(r)

IQR(r)

54/659 spectra removed

Proper

Low quality

Figure 4: Diagnostic plot for 3 cancers dataset with thresh-

old value marked by the dashed line.

same experiment should be comparable, so any mean-

ingful deviances in area under the spectrum intensi-

ties should be treated as outlying measurements. Our

studies showed that spectra with lower TIC have a

negative impact on results of further analysis than the

one with too high TIC due to the presence of multi-

ple peaks resulting from noise (data not presented).

To reduce skewness of distribution of TIC and ex-

tract samples with low values of TIC we initially

perform logarithmic transform of TIC. Spectra with

lower area under the signal are found using outlier de-

tection method for skewed data (Hubert and Van der

Veeken, 2008) on logarithm of TIC (Figure 5).

−1 0 1 2 3 4 5

Logaritm of Total Ion Current

Counts

55/659 spectra removed

Figure 5: Histogram of TIC in logarithmic scale for 3 can-

cers dataset with threshold value marked by the dashed line.

SNR is a commonly used measure of the signal

quality. It makes use of the mean and the standard

deviation of intensities computed for each sample.

These measures of location and dispersion are very

sensitive to outliers. For each spectrum we have a se-

ries of tens of thousands points, only some of which

are associated with signiﬁcantly higher intensity val-

ues forming signal peaks. We propose to use a robust

SNR measure deﬁned as the median signal intensity

to its median absolute deviation. In contrast to the

classical approach of ﬁltering data with low SNR, in

our application we are looking for the samples with

too high SNR values. High value of robust SNR cal-

culated for mass spectrum intensities indicates a large

amount of only very low peaks, which may be the re-

sult of noise occurrence. To identify the cut-off value

we introduced an idea of modeling the distribution of

SNR using Gaussian mixture model (Figure 6). We

ﬁnd the optimal number of components by minimiz-

ing Bayesian information criterion. In order to dis-

tinguish Gaussian components consisting low quality

spectra we use a k-means clustering procedure, which

classiﬁes estimated components into two groups in

three dimensional space with coordinates given by

means, standard deviations and weights of Gaussian

components. Then we remove samples which belong

to cluster of the components located at the right hand

side of the robust SNR scale (Marczyk et al., 2013).

20 40 60 80 100 120 140 160

Counts

SNR_robust [%]

54/170 spectra removed

Data

Model

Components

Figure 6: Histogram of robust SNR for ovarian dataset with

threshold value marked by the dashed line.

3 RESULTS

Four selected quality control algorithms have been ap-

plied to study three MALDI-ToF datasets containing

different number of groups and different number of

spectra within a group. All procedures were applied

to raw data without information of the group mem-

bership. Inﬂuence of introducing quality control as a

pre-processing step on the integrity of the spectra and

a performance of peak detection was obtained for data

within the groups.

In Table 1 we presented percentages of deleted

spectra after using different quality control tools.

In some cases all algorithms remove the same low

quality signals which results in the same further re-

sults (group CRC and HNC in 3 cancers dataset).

For group LC all methods indicated that about 17%

of data should be removed. In larynx and ovarian

datasets SNR method cleaned much more spectra than

other algorithms. For almost all calculations methods

Corr and DP removed the same amount of data.

To quantify inﬂuence of quality control algorithms

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Table 2: Comparison of different quality control methods using the median CV and a prevalence of peaks in three datasets.

CRC - colorectal cancer, CTR - control group, HNC - head and neck cancer, LC - lung cancer, A,B,C - groups representing

different time points, CTR - control group, OVC - ovarian cancer. None - results without quality control, Corr - method based

on a correlation matrix, DP - method using the diagnostic plot, TIC - method based on a total ion current, SNR - method based

on a signal-to-noise ratio of spectrum intensities.

3 cancers Larynx Ovarian

Measure Method CRC CTR HNC LC A B C CTR OVC

Median None 134.65 113.68 123.59 142.70 73.02 86.34 68.80 54.76 64.60

CV Corr 130.85 109.37 109.89 120.69 72.85 80.20 68.07 52.62 64.40

[%] DP 130.85 109.37 109.89 120.69 73.02 86.34 68.80 52.62 64.40

TIC 130.85 107.89 109.89 120.69 72.73 85.83 68.98 49.65 64.27

SNR 130.85 103.54 109.89 119.76 69.38 82.66 68.45 54.03 68.16

Median none 37.86 40.56 40.71 35.68 31.48 34.91 33.59 42.86 40.86

prevalence Corr 38.52 40.80 42.64 38.92 30.09 33.89 32.53 42.67 41.30

[%] DP 38.52 40.80 42.64 38.92 31.48 34.91 33.24 42.67 41.30

TIC 38.52 41.62 42.64 38.92 30.70 34.76 34.03 40.85 40.45

SNR 38.52 42.01 42.64 38.79 32.58 35.49 34.34 46.81 43.48

on data integrity we calculated the similarity mea-

sure (Frank et al., 2008) using all points of spectra.

The intra-group similarity was deﬁned as the median

of spectra similarity calculated pairwise between the

group members (Table 1). In almost all cases exe-

cuting quality control step increased intra-group sim-

ilarity. For 3 cancers and ovarian datasets the best re-

sults are obtained after using SNR method. For larynx

dataset using Corr method gave the highest similarity.

To measure the peaks reproducibility which is a

good assessment of the overall consistency of a set

we ﬁrst detect peaks in a mean spectrum which is

constructed as the average of intensities of all spec-

tra in a given dataset. Next, we detected peaks for an

individual measurements and performed peak match-

ing to the reference based on M/Z distance. We set

a maximum considered value for the difference be-

tween peaks to 0.3% * M/Z. We calculated a preva-

lence and the coefﬁcient of variation for the peaks in-

tensities and summarize it within the groups calculat-

ing the median value (Table 2). In almost all cases

use of quality control algorithms decreased CV and

increased peaks prevalence. For CTR and LC groups

of 3 cancers dataset SNR method gave the best results.

In two cases Corr method gave lower CV than others.

For ovarian dataset using TIC method we got the low-

est CV. Introducing SNR method in this dataset lead

to increased prevalence of peaks but in cost of reduced

CV, which is still similar to the case when no quality

control method is used.

4 DISCUSSION

Removing of low quality spectra has a big inﬂuence

on increasing the amount of association between sam-

ples. Providing better similarity of data within the

same experimental group may increase reliability of

protein proﬁling procedure. It may lead to more accu-

rate discrimination of samples between different treat-

ment groups and ﬁnding more signiﬁcant biomark-

ers. Quality control also signiﬁcantly increases repro-

ducibility of experiment results by decreasing vari-

ability of estimated intensities of peaks.

It is a desirable property of QC tools that crite-

rion for removing data is based on all samples in the

dataset and disregards information on measurement

group labels. Such an assumption makes this phase

independent to further data analysis like searching for

proteins that show different intensity levels between

two groups or data classiﬁcation. A violation of this

condition can lead to the loss of FDR control. All

methods used in this study satisfy an assumption of

independence.

For two algorithms adapted from the literature

we introduced a new method for separation of spec-

tra with different quality using a hierarchical cluster-

ing. In original implementations these values were

set manually by the researcher. The visual inspection

of histograms of the correlation coefﬁcients and the

diagnostic plots with thresholds estimated using our

method, together with presented results of analysis,

proved that it is a good approach to automate process

of ﬁnding cut-off values for different datasets.

Quality control process implicates reducing di-

mensionality of the dataset by rejecting the spectra

resulting from measurement errors. In most of the

applications it is proper to delete no more than 10-

15% of data. In ovarian dataset SNR method re-

moves about 30% of data which is not tolerable. This

may induce a discussion if a MALDI-ToF experiment

was conducted properly. However, applying of SNR

method signiﬁcantly increased similarity of ovarian

dataset and a prevalence of peak detection, which

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proves correctness of using this method. It may hap-

pen that there are too many low quality spectra in a

dataset and our method allows for their appropriate

control. But when the number of deleted spectra is

too large in point of view of the researcher we recom-

mend to use TIC method which also provided good

results.

5 CONCLUSIONS

Applying of rigorous procedures during preparation

of the sample and measurements of signal does not

guarantee that all spectra from the experiment are of

sufﬁcient quality for further data analysis. In order to

provide that only high quality data are used a com-

prehensive quality control step is required. For this

purpose we recommend to use our method based on

Gaussian mixture modeling of robust SNR measure.

It is a fully automatic algorithm and has a potential to

adapt cut-off values for removing spectra in different

sets of MALDI-ToF data. Eliminating of the outly-

ing signals with our method increases the similarity

of samples measured within the same experimental

conditions and reproducibility of peak detection al-

gorithms.

ACKNOWLEDGEMENTS

This work was ﬁnancially supported by Silesian Uni-

versity of Technology internal grant for young scien-

tists BKM/514/RAU-1/2013 (MM) and National Sci-

ence Centre grant no. UMO-2013/08/M/ST6/00924

(JP).

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