A Survey on Feature Selection Methods in Microarray Gene Expression Data for Cancer Classification

Gunavathi C¹*, Premalatha K², Sivasubramanian K³

¹School of Information Technology and Engineering, VIT University, Vellore, India.

²Department of CSE, Bannari Amman Institute of Technology, Sathyamangalam, India.

³Department of ECE, K.S. Rangasamy College of Technology, Tiruchengode, India.

*Corresponding Author E-mail: gunavathi.cm@vit.ac.in

ABSTRACT:

Microarray technology is commonly used in the study of disease diagnosis using gene expression levels. It not only received the attention of the research community but also has a wide range of applications. The success of microarray technology depends on the precision of measurement, the usage of tools in data mining, analytical methods and statistical modeling. The feature selection methods are used to find an informative representation, by removing noisy and irrelevant features which would improve the classification performance. There exist several works in the literature to select the significant features from the microarray. This paper reviews the feature selection methods used to select significant genes from the microarray gene expression data for cancer classification.

KEYWORDS: Microarray, Feature Selection, Gene Expression, Cancer Classification, Gene Selection.

INTRODUCTION:

Microarray technology facilitates the assessment of Deoxyribonucleic acid (DNA) and Ribonucleic acid (RNA) variations. The raw microarray data are images that are transformed into gene expression matrices. The rows in the matrix correspond to genes, and the columns represent samples or trial conditions. The number in each cell signifies the expression level of a particular gene in a particular sample or condition^{1, 2}. Expression levels can be absolute or relative. If two rows are similar, it implies that the respective genes are co-regulated and perhaps functionally related. By comparing samples, differentially expressed genes can be identified.

The main shortcoming of gene expression data is that it includes thousands of genes and a small number of samples. Ample methods and techniques have been proposed for cancer/tumour classification using microarray gene expression data. A number of gene selection methods have been introduced to select the informative genes for cancer/tumour prediction and diagnosis. Feature or gene selection methods can be used to mine the genes that directly involved in the classification and to eliminate irrelevant genes and improve the classification accuracy.

FEATURE SELECTION METHODS:

In this section the works related with gene selection and tumour classification using microarray gene expression data are discussed. Ding and Peng used minimum redundancy–maximum relevance framework to select the features from the microarray³. This method is robust and generalizes unseen data and lead to significantly improved classifications in gene expression datasets. Two basic approaches for feature selection appear in machine learning and pattern recognition literature. A comparison between a group of different filter metrics and a wrapper sequential search procedure is carried out by Inza et al⁴. Duan et al proposed a feature selection method called Multiple SVM-recursive feature elimination (MSVM-RFE) that uses a backward elimination procedure⁵. This approach computes the feature ranking score from a statistical analysis of weight vectors of multiple linear SVMs trained on subsamples of the original training data at each step.

Yang et al proposed the methods for Gene selection (GS) namely GS1 and GS2 which are not affected by the unbalanced sample class sizes and did not assume any explicit statistical model on the gene expression values⁶. Self-organizing map (SOM) is used for clustering cancer data which is composed of important gene selection step⁷. This study presented a technique for cancer prediction from DNA microarray data. The prediction composed of two main steps which consist of the gene selection by statistic methodology and the clustering of cancer data using SOM.

An innovative generalization of Signal-to-noise ratio (SNR) for multiclass cancer discrimination through introduction of two indices, Gene dominant index and Gene dormant index (GDIs) is proposed by Tsai et al⁸. These two indices lead to the concepts of dominant and dormant genes with biological significance. The dominancy and dormancy of the identified biomarkers and their excellent discriminating power are demonstrated pictorially using the scatter plot of individual gene and 2-D Sammon's projection of the selected set of genes.

Liu et al proposed a gene selection method called Recursive feature addition (RFA) which combines supervised learning and statistical similarity measures⁹. Rough set concept with depended degrees is proposed by Wang and Gotoh¹⁰. In this method a small number of informative single gene and gene pairs are screened based on their depended degrees.

Chopra et al used gene pair combinations, called doublets, as input to the cancer classification algorithms, instead of the original expression values, and showed that the classification accuracy is consistently improved across different datasets and classification algorithms¹¹. An Ensemble gene selection (EGS) method is proposed to choose multiple gene subsets for classification purpose, where the significant degree of gene is measured by conditional mutual information or its normalized form¹². Different gene subsets have been obtained by setting different starting points of the search procedure. The subsets are used to train multiple base classifiers and aggregated into a consensus classifier by majority voting.

Yao and Li presented a method called Additive nonparametric margin maximum for case-based reasoning (ANMM4CBR) to obtain a robust classifier¹³. ANMM4CBR employed a Case-based reasoning (CBR) method for classification. In CBR, the rules that define the domain knowledge are difficult to obtain because only a small number of training samples are available. To select the most informative genes, feature selection via additively optimizing a nonparametric margin maximum criterion is proposed.

A feature selection approach based on statistically defined effective range of features for every class termed as Effective range based gene selection (ERGS) is proposed by Chandra and Gupta¹⁴. The basic principle behind ERGS is that higher weight is given to the feature that discriminates the classes clearly. Hyper-Box Enclosure (HBE) method based on mixed integer programming is used for the classification of some cancer types with a minimal set of predictor genes¹⁵.

Biomarker identifier (BMI) has been developed by Lee et al which identified the features with the ability to distinguish between two data groups of interest¹⁶. Margin Influence Analysis (MIA) is an approach designed to work with SVM for selecting informative genes¹⁷. The motivation for performing MIA lies in the fact that the margin of SVMs is an important factor which underlies the generalization performance of SVM models. The MIA could reveal genes which have statistically significant influence on the margin by using Mann-Whitney U test. Liu et al used a gene selection method called RFA, which combines supervised learning and statistical similarity measures¹⁸. To determine the final optimal gene set for prediction and classification, an algorithm named Lagging prediction peephole optimization (LPPO) is proposed.

A Group marker index (GMI), which is intuitive, of low-computational complexity gene selection method is presented by Tsai et al¹⁹. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. This method is efficient in the identification of both types of genes. This method is effective even when the sample size is small as well as when the class sizes are significantly different. Wang and Simon used a method based on a single gene to construct classification models²⁰. The genes with the most powerful univariate class discrimination ability are identified and simple classification rules are constructed for class prediction using the single genes.

Alonso et al proposed a method that relaxed the maximum accuracy criterion to select the combination of attribute selection and classification algorithm²¹. Some suggestions are also given to choose a suitable combination of attribute selection and classifying algorithms for accuracy when using a low number of gene expressions. Huang et al presented an improved Semi-supervised local fisher discriminant (iSELF) analysis for gene expression data classification²². This work preserves the global structure of unlabeled samples in addition to separating labeled samples in different classes from each other.

Maji presented a quantitative measure based on mutual information that incorporates the information of sample categories to measure the similarity between attributes²³. In this work, a supervised attribute clustering algorithm is used to find the groups of genes. It directly incorporates the information of sample categories into the attribute clustering process. The clusters are then refined incrementally based on sample categories. Sharma et al proposed an algorithm that divides genes into subsets first, the sizes of which are relatively small (roughly of size h), then selects informative smaller subsets of genes (of size r < h) from a subset and merges the chosen genes with another gene subset (of size r) to update the gene subset²⁴. This process is repeated until all subsets are merged into one informative subset. Sharma et al proposed a null space based feature selection method for gene expression data in terms of supervised classification²⁵. This method discards the redundant genes by applying the information of null space of scatter matrices.

Arevalillo and Navarro proposed a method named Maximum predictive–minimum redundancy (MPMR) for the selection of highly predictive genes having a low redundancy in their expression levels²⁶. The predictive accuracy is assessed by Classification and regression trees (CART) models. Bhalla and Agrawal focused on the study that relaxes the maximum accuracy criterion for feature selection and selects the combination of feature selection method and classifier²⁷. The method uses a small subset of features that gives the accuracy not statistically indicatively different than the maximum accuracy. By selecting the classifier that employs small number of features along with a good accuracy, the risk of over fitting (bias) is reduced. Du et al proposed a feature selection method by considering all kinds of genes in the original gene set in a multi-step process²⁸. The method eliminates the irrelevant, noisy and redundant genes and selects a subset of relevant genes at different stages. Song et al proposed a fast clustering-based feature selection algorithm for gene selection from the microarray based on two steps²⁹. In the first step, features are divided into clusters by using graph-theoretic clustering methods. In the second step, the most representative feature that is strongly related to target classes is selected from each cluster to form a subset of features. Xu et al proposed an improved SOM clustering algorithm which is based on neighborhood mutual information correlation measure³⁰.

Genetic algorithm based feature selection with kNN is introduced by Gunavathi and Premalatha³¹. Feature selection prior to classification plays a vital role and a feature selection technique which combines Discrete wavelet transform (DWT) and Moving window technique (MWT) is used by Bennet et al³². In this method, kNN, NBC, and SVM are used as the classifiers. Maulik and Chakraborty proposed a Fuzzy preference based rough set (FPRS) method for feature (gene) selection with semi supervised SVMs³³. The performance of the technique is compared with the SNR and Consistency based feature selection (CBFS) methods. Statistical methods and optimization algorithms are used for gene selection in Lung and Ovarian cancer by Gunavathi and Premalatha³⁴.

Sharma et al proposed a feature selection method based on fixed-point algorithm for cancer classification using DNA microarray gene expression data³⁵. In the fixed-point algorithm, between-class scatter matrix is used to compute the leading Eigen vector. This Eigen vector has been used to select genes. Sharma et al proposed a feature selection method using improved regularized linear discriminant analysis technique to select important genes³⁶. Wang et al proposed an Online feature selection (OFS) method in which an online learner is permitted to retain a classifier involving only a small and fixed number of features³⁷.

This work deals with two different tasks of online feature selection: 1) learning with full input, where a learner is allowed to access all the features to choose the subset of active features, and 2) learning with partial input, where only a limited number of features are permitted to be accessed for each instance by the learner. A comparative study on swarm intelligence techniques for feature selection in cancer classification is done in³⁸. Feature selection based on cuckoo search optimization for cancer classification is used in³⁹.

Table 1 shows the performance of various feature selection methods on different gene expression datasets used in the existing literature.

Table 1 Performance of the feature selection methods

S. No.	Reference	Methodology	Datasets used	Classification accuracy in percentage
1	Ding and Peng (2003)	Minimum redundancy–maximum relevance	Leukemia Colon NCI Lung Lymphoma	100.00 100.00 98.30 97.30 96.90
2	Inza et al (2004)	Comparison of filter and wrapper methods	Colon Leukemia	95.16 100.00
3	Duan et al (2005)	MSVM-RFE	Breast Colon Leukemia Lung	98.71 98.77 100.00 100.00
4	Yang et al (2006)	GS1 and GS2	Leukemia SRBCT GLIOMA Lung Carcinoma MLL Prostate DLBCL	98.60 100.00 82.00 95.10 90.20 97.20 95.10 96.10
5	Vanichayobon et al (2007)	SOM	Carcinoma Leukemia Lung	100.00 100.00 100.00
6	Tsai et al (2008)	GDIs	SRBCT Leukemia CNS Lung	97.80 94.70 78.40 96.55
7	Liu et al (2009)	RFA	MAQC-II breast cancer MAQC-II Multiple Myeloma Dataset	90.6± 2.8 Not mentioned
8	Wang and Gotoh (2009)	Rough set	Leukemia Lung Prostate Breast Leukemia dataset 2	100.00 100.00 91.18 84.21 93.33
9	Chopra et al (2010)	Gene doublets	Colon Leukemia CNS DLBCL Lung Prostate1 Prostate2 Prostate3 GCM	79.03 91.67 70.59 97.40 96.13 87.25 77.27 90.91 83.21
10	Liu et al (2010)	EGS	Breast CNS Leukemia Lung Lymphoma Prostate	93.81 98.33 100.00 89.58 100.00 97.06
11	Yao and Li (2010)	ANMM4CBR	Leukemia Colon SRBCT GCM	97.5 ± 1.7 86.7 ± 5.6 99.7 ± 0.3 63.3 ± 3.9
12	Chandra and Gupta (2011)	ERGS	ALL_AML Colon DLBCL Lung MLL Prostate	100.00 83.87 97.92 100.00 97.22 94.12
13	Dagliyan et al (2011)	HBE	Leukemia Prostate cancer Prostate outcome DLBCL Lymphoma SRBCT	100.00 96.08 95.24 96.10 97.87 100.00
14	Lee et al (2011)	BMI	MAQC-II Airway dataset	92.98 82.99

Table 1 Continue…..

S. No.	Reference	Methodology	Datasets used	Classification accuracy in percentage
15	Li et al (2011)	MIA	Colon Estrogen	100.00 100.00
16	Liu et al (2011)	RFA with LPPO	Leukemia Lymphoma Prostate Colon CNS Breast	99.9 99.5 96.90 91.10 94.00 85.90
17	Tsai et al (2011)	GMI	SRBCT Leukemia CNS Lung cancer	90.48 96.49 92.86 96.06
18	Wang and Simon (2011)	Single gene	Melanoma Breast Cancer 1 Brain Cancer Breast Cancer 2 Gastric Tumor Lung Cancer 1 Lung Cancer 2 Lymphoma Myeloma Pancreatic Cancer Prostate Cancer	97.00 69.00 80.00 58.00 89.00 98.00 93.00 74.00 68.00 90.00 89.00
19	Alonso et al (2012)	Combination of attribute selection and classification algorithm	ALL_AML ALL AML Bladder Brain Breast Breast-F Breast-W CNS Colon DLBCL Leukemia Lung MLL NCI60 Ovarian Pancreatic Prostate	100.00 94.18 77.20 96.67 55.56 70.96 100.00 92.56 75.49 88.41 98.67 68.33 99.33 98.21 84.34 100.00 92.50 93.08
20	Huang et al (2012)	iSELF	SRBCT DLBCL Brain Tumor	94.71 ± 0.46 91.33 ± 0.91 81.27 ± 0.72
21	Maji (2012)	Mutual information and supervised attribute clustering	Breast Cancer Leukemia Colon Cancer Rheumatoid Arthritis versus Osteoarthritis Rheumatoid Arthritis versus Healthy Controls	100.00 100.00 100.00 100.00 100.00
22	Sharma et al (2012a)	Gene subset	SRBCT MLL Prostate Tumor	100.00 100.00 100.00
23	Sharma et al (2012b)	Null space based feature selection	Acute Leukemia SRBCT MLL Lung	100.00 100.00 100.00 97.3
24	Arevalillo and Navarro (2013)	MPMR	Colon	90.68
25	Bhalla and Agrawal (2013)	Combination of feature selection method and classifier	Breast GCM Lung MLL Lymphoma Prostate	87.50 73.33 100.00 97.33 93.77 95.52

Table 1 Continue…….

S. No.	Reference	Methodology	Datasets used	Classification accuracy in percentage
26	Du et al (2013)	Multi-stage method for feature selection	Leukemia Prostate Colon Breast Nervous DLBCL	100.00 98.04 100.00 89.74 100.00 98.28
27	Song et al (2013)	Fast clustering-based feature selection algorithm	Colon B-cell1 B-cell2 B-cell3 Leukemia1 Leukemia2 GCM	95.08 100.00 96.63 98.22 100.00 100.00 70.90
28	Xu et al (2013)	Improved SOM clustering algorithm	8D5K AD400_10_10 Yeast Heart Class Breast Cancer	78.10 76.50 67.90 73.70 71.10 72.90
29	Bennet et al (2014)	DWT and MWT	Colon Ovarian CNS Leukemia Breast	100.00 100.00 100.00 100.00 100.00
30	Maulik and Chakraborty (2014)	FPRS with semi-supervised SVMs	Leukemia SRBCT MLL DLBCL Prostate ALL	97.22 ± 3.21 95.61 ± 2.78 89.16 ± 3.32 95.25 ± 3.23 91.56 ± 4.39 85.27 ± 3.96
31	Sharma et al (2014a)	Fixed-point algorithm	SRBCT MLL ALL	70.00 100.00 94.00
32	Sharma et al (2014b)	Improved regularized linear discriminant analysis	SRBCT MLL Leukemia	100.00 100.00 100.00
33	Wang et al (2014)	OFS	Colon	99.92

CONCLUSION:

Feature selection is an important problem in large volumes of data for classification. The special nature of microarray data (large number of genes but small number of samples) creates the need for significant gene selection. There are numerous studies on feature selection for diagnosing cancer using microarray gene expression data. This paper has reviewed and analyzed the current research literature on the various methods for selecting the important features from the microarray for cancer classification. Extensive analysis on the comparison of classification accuracy of different datasets is also presented in this work.

CONFLICT OF INTEREST:

The authors declare no conflict of interest.

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Received on 06.03.2017 Modified on 02.04.2017

Research J. Pharm. and Tech. 2017; 10(5): 1395-1401.

DOI: 10.5958/0974-360X.2017.00249.9