- Open Access
Comparison of feature selection and classification for MALDI-MS data
© Liu et al; licensee BioMed Central Ltd. 2009
- Published: 7 July 2009
In the classification of Mass Spectrometry (MS) proteomics data, peak detection, feature selection, and learning classifiers are critical to classification accuracy. To better understand which methods are more accurate when classifying data, some publicly available peak detection algorithms for Matrix assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS) data were recently compared; however, the issue of different feature selection methods and different classification models as they relate to classification performance has not been addressed. With the application of intelligent computing, much progress has been made in the development of feature selection methods and learning classifiers for the analysis of high-throughput biological data. The main objective of this paper is to compare the methods of feature selection and different learning classifiers when applied to MALDI-MS data and to provide a subsequent reference for the analysis of MS proteomics data.
We compared a well-known method of feature selection, Support Vector Machine Recursive Feature Elimination (SVMRFE), and a recently developed method, Gradient based Leave-one-out Gene Selection (GLGS) that effectively performs microarray data analysis. We also compared several learning classifiers including K-Nearest Neighbor Classifier (KNNC), Naïve Bayes Classifier (NBC), Nearest Mean Scaled Classifier (NMSC), uncorrelated normal based quadratic Bayes Classifier recorded as UDC, Support Vector Machines, and a distance metric learning for Large Margin Nearest Neighbor classifier (LMNN) based on Mahanalobis distance. To compare, we conducted a comprehensive experimental study using three types of MALDI-MS data.
Regarding feature selection, SVMRFE outperformed GLGS in classification. As for the learning classifiers, when classification models derived from the best training were compared, SVMs performed the best with respect to the expected testing accuracy. However, the distance metric learning LMNN outperformed SVMs and other classifiers on evaluating the best testing. In such cases, the optimum classification model based on LMNN is worth investigating for future study.
- Feature Selection
- Support Vector Machine Classifier
- Feature Selection Method
- Peak Detection
- Mass Spectrometry Data
In proteome research, high-throughput mass spectrometry (MS) establishes an effective framework for biomedical diagnosis and protein identification . A mass spectrum data sample includes a sequence of mass/charge (m/z) ratios. Two types of mechanisms, low resolution and high resolution, that typically contain more than 10,000 data points ranging from 500 Da to 20000 Da, are used in mass spectrometry.
Mass spectrum data mining usually contains four steps: preprocessing, feature extraction or peak detection, feature selection and classification. Sometimes preprocessing and peak detection are merged as preprocessing. The main task in preprocessing is to purify the data and systematically represent the data for the following steps. The MS data contain two kinds of noise that damage the classification result: electric noise and chemical noise. MS data is generated with chemical noise through matrix or ion overloading, and the noise usually shows up as a baseline along the spectrum. Baseline correction computes the local minimum value, draws a baseline represented as the background noise, and subtracts the baseline from the spectrum. Williams et al  proposed a robust algorithm for computing the baseline correction of MALDI-MS spectra. Alternatively, because electronic noise is generated from the electronic instrument and is usually randomly distributed in the spectra, Chen et al  designed a wavelet-based de-noising that applies wavelet transformation and removes a certain amount of lower value wavelet coefficients. The de-noised data are normalized to systematically represent the spectra. The next crucial step is to extract features from the spectra and then form the initial complete feature set. The simplest way is to extract every data point as a discriminative feature and generate a huge feature set including more than 15,000 features [4, 5]. A more elaborate algorithm for peak detection and alignment is also available to perform an even more aggressive feature extraction [6–8].
To classify MALDI MS data, peak detection, feature selection, and classifier are generally important to obtain the final results. To compare public peak detection algorithms, Yang et al.  recently conducted an experimental study using five single spectrum based peak detection algorithms including Cromwell , CWT , PROcess , LMS , and LIMPIC . That study did not compare feature selection and classifiers for MALDI-MS data. "The curse of dimensionality" in MS data requires a powerful feature selection algorithm to choose the discriminative feature subset. While distance metric learning has drawn many researchers' attention, researchers recognize that different classifiers yield different results. Therefore, a comprehensive experimental study that compares these powerful methods of feature selection and different learning classifiers for the classification of MALDI-MS data has been sorely needed.
Support Vector Machine Recursive Feature Elimination (SVMRFE)  is a very popular method for feature selection based on the backward feature elimination that recursively removes the least ranking feature. Originally proposed for microarray data analysis, it has been widely used for feature selection in different areas including MS data analysis . Recently, Tang et al. designed a method of feature selection called the gradient based leave-one-out gene selection (GLGS) for classifying microarray data. The authors concluded that GLGS outperforms SVMRFE in microarray data analysis , a finding that our previous work corroborates in that we found that GLGS also effectively classified microarray data . To reach a more definitive understanding of how methods compare, we evaluated two methods of feature selection as well as popular learning classifiers in an experimental study on MALDI-MS data.
Preprocessing MALDI-MS data
Mass spectrum data has high dimensionality within a small sample size. Both chemical and electrical noises are involved in the signal, and the redundancy of the spectra, different reference points, and unaligned feature points increase the computational intensity and decrease the classification accuracy. In this section, we explain the preprocessing methods, including spectra re-sampling, wavelet de-noising, baseline correction, normalization, peak detection and alignment.
Spectra re-sampling and wavelet de-noising
Mass spectrum data presents in a discrete format along intervals that are not equal in the whole spectrum. For high-resolution data, the high-frequency noise and redundant data points harm the quality of the dataset. So, we have to set the common low-frequent mass value to every sample spectrum to have a unified representation. By using spline interpolation, we resample the data and confine the interval to a unified size. Before re-sampling, the sample spectrum has little variation from the true spectrum. The data is re-sampled to a standard discrete data that could be analyzed in a frequency domain. The electrical noise is generated in an almost randomly distributed way during the mass spectrum acquisition by the instrument. The next step is to use discrete wavelet transform to eliminate the electrical noise. By applying a wavelet transform, the original signal is decomposed into multi-level wavelet coefficients. By setting up a threshold value, given percentiles of lower value coefficients are removed. Then, we apply a polynomial filter of a second order to smooth the signal and improve data quality.
Baseline correction and normalization
Chemical contamination introduces the baseline effect and changes the true protein distribution. To minimize chemical noise, the baseline is subtracted from the spectrum. To obtain the baseline, the local minima are computed by assigning a shifting window size of 30 and a step size of 30. Then, we use spline interpolation to fit the baseline. After smoothing, the baseline is subtracted from all spectra. To compare sample spectra, we need to normalize the spectra using its total ion current to represent the data in a systematic scale.
Peak detection and qualification
The final feature acquisition of MS data is to obtain the peak position and its magnitude. Peak is the position of maximum intensity in a local area in spectrum, and particularly in mass spectrum, it refers to the mass location where ion count is the largest in a local m/z zone. The peak is identified where the first derivative is changing from a positive to a negative. In our mass spectrum experiment, the peak detection method proposed by Coombes et al  is performed on a mean spectrum rather than individual spectra. We used the ad hoc method based on signal-to-noise ratio to select the large peaks based on the preprocessing method described in reference .
To address the "curse of dimensionality" problem, three strategies have been proposed: filtering, wrapper and embedded methods. Filtering methods select subset features independently from the learning classifiers and do not incorporate learning. One of the weaknesses of filtering methods is that they only consider the individual feature in isolation and ignore possible interactions. Yet, the combination of these features may have a combination effect that does not necessarily follow from the individual performances of the features in that group. One of the consequences of the filtering methods is that we may end up with many highly correlated features; yet, any highly redundant information will worsen the classification and prediction performance. Furthermore, a limit on the number of features chosen may preclude the inclusion of all informative features.
Initial ranked feature set R = ; feature set S = [1,..., d];
- (2)Repeat until all features are ranked
Train a linear SVM with all the training data and variables in S;
Compute the weigh vector;
Compute the ranking scores for features in S;
Find the feature with the smallest ranking score;
Update R: R = R [e, R];
Update S: S = S - [e];
Output: Ranked feature list R.
Wrapper methods can noticeably reduce the number of features and significantly improve the classification accuracy . However, wrapper methods have the drawback of having a high computational load. With better computational efficiency and similar performance to wrapper methods, embedded methods simultaneously process feature selection with a learning classifier. To deal with the feature selection in microarray data classification, Tang et al. also proposed two gene selection methods: leave-one-out calculation sequential forward selection (LOOCSFS) and GLGS that is based on the calculation of the leave-one-out cross-validation error of LS-SVM . The GLGS algorithm can be categorized as an embedded method that differs greatly from previous wrapper and embedded approaches because the GLGS optimizes the evaluation criterion derived in a supervised manner in a transformed space with significantly reduced dimensions compared to the original space as it selects genes from the original gene set based on the results of the optimization. According to presented experimental results, the GLGS method is more appealing given it has the lowest generalization error .
Based on the above explanation, we employed SVMRFE and GLGS algorithms for feature selection in our experimental study.
Support vector machines
SVM  has been widely used in classification. It constructs an optimal hyperplane decision function in feature space that is mapped from the original input space by using kernels, briefly introduced as follows:
Three types of commonly used kernel functions are:
Linear Kernel k(x i ; x j ) = x i •x j
Polynomical Kernel k(x i ; x j ) = (1 + x i •x j ) p
Gaussian Kernel k(x i ; x j ) = exp(-||x i - x j ||2/2σ2)
Distance metric learning
Depending on the availability of training examples, the algorithms of distance metric learning can be divided into two categories: supervised distance metric learning and unsupervised distance metric learning. With the given class labels for training samples, supervised distance metric learning can be divided into global distance metric learning and local distance metric learning. The global learns the distance metric in a global sense, i.e., to satisfy all the pairwise constraints. The local approach is to learn the distance metric in a local setting, i.e., only to meet local pairwise constraints.
Unsupervised distance metric learning is also called manifold learning. Its main idea is to learn an underlying low-dimensional manifold whereby the geometric relationships between most of the observed data are preserved. Every dimension reduction approach works by essentially learning a distance metric without label information. Manifold learning algorithms can be divided into global linear dimension reduction approaches, including Principle Component Analysis (PCA) and Multiple Dimension Scaling (MDS), global nonlinear approaches, for instance, ISOMAP , local linear approaches, including Locally Linear Embedding (LLE)  and the Laplacian Eigenmap .
In supervised global distance metric learning, the representative work formulates distance metric learning as a constrained convex programming problem . In local adaptive distance metric learning, many researchers presented approaches to learn an appropriate distance metric to improve a KNN classifier [28–32]. Inspired by the work on neighborhood component analysis  and metric learning with the use of energy-based models , Weinberger et al. proposed a distance metric learning for Large Margin Nearest Neighbor classification (LMNN). Specifically, the Mahanalobis distance is optimized with the goal that the k-nearest neighbors always belong to the same class while examples from different classes are separated by a large margin . The LMNN has several parallels to learning in SVMs. For example, the goal of margin maximization and a convex objective function is based on the hinge loss. In multi-classification, the training time of SVMs scales at least linearly in the number of classes. By contrast, LMNN has no explicit dependence on the number of classes . We introduce the idea of LMNN as follows:
Slack variables ξ ij for all pairs of differently labeled inputs are introduced so that the hinge loss can be mimicked. The resulting SDP is given by:
x i - x l )M(x i - x l )-(x i - x j )M(x i - x j ) ≥ 1 - ξ ijl
ξ ijl ≥ 0
M ≥ 0
Other learning classifiers
Besides comparing learning classifiers LMNN and support vector machines with linear kernel (SVM_linear) and RBF kernel (SVM_rbf), we also applied several traditional classifiers including K-Nearest Neighbor Classifier (KNNC), Naïve Bayes Classifier (NBC), Nearest Mean Scaled Classifier (NMSC), Uncorrelated normal based quadratic Bayes Classifier recorded as UDC for the comparison study. The technical details about these learning classifiers can be found in reference .
Data sets and experiments
High resolution time-of-flight (TOF) mass spectrometry (MS) proteomics data set from surface-enhanced laser/desorption ionization (SELDI) ProteinChip arrays on 121 ovarian cancer cases and 95 controls. The data sources can be accessed by FDA-NCI Clinical Proteomics at http://home.ccr.cancer.gov/ncifdaproteomics/ppatterns.asp
The breast cancer QC SELDI spectra data set was studied by Pusztai et al. . Here, we utilized the data of 57 controls and 51 cases. The data set is available at: http://bioinformatics.mdanderson.org/Supplements/Datasets
Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) liver disease data set was collected by Ressom et al.  for peak selection using ant colony optimization. The data set consists of 78 hepatocellular carcinoma (HCC, also called malignant hepatoma, a primary malignancy cancer of the liver), 51 cirrhosis (cirrhosis is a consequence of chronic liver disease characterized by replacement of liver tissue by fibrous scar tissue as well as regenerative nodules leading to progressive loss of liver function), and 72 normal. The spectra were binned with bin size of 100 ppm, and the dimension was reduced from 136,000 m/z values to 23846 m/z bins. Since the two liver diseases have similar symptoms but different treatments, our effort is focused on the classification of these two different diseases, or the identification of HCC and cirrhosis.
Expected testing accuracy and standard errors (mean ± standard error, %) with classification models derived from best training, with the use of GLGS and SVMRFE feature selection algorithms and seven learning classifiers. Following the use of each feature selection algorithm on each data set, the best result as well as the classifier is highlighted in bold.
87.4 ± 5.8%
74.1 ± 6.9
80.9 ± 6.6
93.6 ± 3.8
82.8 ± 6.9
89.8 ± 3.9
78.9 ± 5.8
73.3 ± 8.5
87.1 ± 6.0
90.2 ± 4.5
74.1 ± 9.3
92.8 ± 4.1
81.8 ± 5.2
76.2 ± 9.1
90.8 ± 4.9
92.2 ± 3.9
80.5 ± 8.0
94.3 ± 4.1
82.1 ± 5.6
76.9 ± 8.0
89.5 ± 5.9
91.8 ± 4.3
81.1 ± 7.4
90.4 ± 6.0
89.6 ± 4.9
85.6 ± 8.3
95.8 ± 3.8
97.9 ± 2.0
89.9 ± 6.0
98.2 ± 2.7
90.4 ± 4.3
85.3 ± 7.9
96.4 ± 3.3
98.2 ± 1.8
90.5 ± 6.1
97.5 ± 3.1
88.0 ± 4.9
75.5 ± 6.7
88.6 ± 4.7
97.4 ± 1.6
77.4 ± 5.8
91.6 ± 3.2
Average testing under each dimension
Expected testing performance under best training
Best testing performance under best training
Best testing accuracy and standard errors (mean ± standard error, %) with classification models derived from best training, with the use of GLGS and SVMRFE feature selection algorithms and seven learning classifiers. By using each feature selection algorithm on each data set, the best result as well as the classifier is highlighted in bold.
88.0 ± 5.8%
80.5 ± 8.6
88.3 ± 6.3
96.6 ± 2.9
87.9 ± 7.0
95.3 ± 3.4
79.9 ± 5.3
75.8 ± 9.0
90.8 ± 5.6
90.9 ± 4.5
76.0 ± 9.1
96.5 ± 3.7
82.6 ± 5.1
77.8 ± 9.1
92.1 ± 4.4
92.6 ± 3.8
81.8 ± 7.6
96.5 ± 4.0
82.7 ± 5.4
78.0 ± 8.0
91.3 ± 5.6
92.5 ± 4.4
82.4 ± 7.7
91.7 ± 5.8
89.6 ± 4.9
85.6 ± 8.3
95.8 ± 3.8
97.9 ± 2.0
89.9 ± 6.0
98.2 ± 2.7
90.4 ± 4.3
85.3 ± 7.9
96.4 ± 3.3
98.2 ± 1.8
90.5 ± 6.1
97.5 ± 3.1
93.1 ± 4.4
88.3 ± 7.4
97.4 ± 3.2
99.2 ± 1.1
91.7 ± 4.5
99.0 ± 1.8
If we compare the results shown in Table 1 and Table 2, we found that the results obtained by using SVMs are the same in both tables, but the results of using other classifiers are different. In each experiment, with the use of other classifiers, there are multiple classification models, derived from the best trainings with different feature numbers. In this case, we calculated the average or expected testing value for Table 1 and obtained the best testing value for Table 2, respectively. On the other hand, by using SVM, we obtained a unique classification model derived from unique best training in each experiment; therefore, the results in Tables 1 and 2 are the same with the use of SVMs.
Regarding the expected testing performance under the best training, SVMs outperformed other classifiers. As for the best testing under best training, the best performance was associated with the learning classifier LMNN, which implies that distance metric learning is very promising for the classification of the MALDI-MS data., In these situations, it is the optimum classification model that delivers the best testing under the best training and, as such, is worthy of future investigation.
In comparison with the SVMRFE method, the GLGS feature selection method delivered a comparable and/or better performance in classifying microarray data; however, our experimental results showed that it does not perform as well as SVMRFE in classifying MALDI-MS data. This phenomenon is very interesting. In our opinion, it is caused by the difference between microarray data and MS data. Microarray data have a huge number of variables. It has a complicated correlation/interaction among genes as well as high redundancy. MALDI-MS data consist of mass/charge ratio values, after peak detection, correlation/interaction among peaks are generally not as complicated and much less redundancy exists. In such cases, SVMRFE is better than GLGS for classifying MS peak data.
The authors wish to thank ICASA (Institute for Complex Additive Systems Analysis, a division of New Mexico Tech) for the support of this study. This work was also supported by the Mississippi Functional Genomics Network (DHHS/NIH/NCRR Grant# 2P20RR016476-04). Special thanks go to Ms. Kimberly Lawson of the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School.
This article has been published as part of BMC Genomics Volume 10 Supplement 1, 2009: The 2008 International Conference on Bioinformatics & Computational Biology (BIOCOMP'08). The full contents of the supplement are available online at http://0-www.biomedcentral.com.brum.beds.ac.uk/1471-2164/10?issue=S1.
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