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Gene selection for microarray data classification via subspace learning and manifold regularization

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Abstract

With the rapid development of DNA microarray technology, large amount of genomic data has been generated. Classification of these microarray data is a challenge task since gene expression data are often with thousands of genes but a small number of samples. In this paper, an effective gene selection method is proposed to select the best subset of genes for microarray data with the irrelevant and redundant genes removed. Compared with original data, the selected gene subset can benefit the classification task. We formulate the gene selection task as a manifold regularized subspace learning problem. In detail, a projection matrix is used to project the original high dimensional microarray data into a lower dimensional subspace, with the constraint that the original genes can be well represented by the selected genes. Meanwhile, the local manifold structure of original data is preserved by a Laplacian graph regularization term on the low-dimensional data space. The projection matrix can serve as an importance indicator of different genes. An iterative update algorithm is developed for solving the problem. Experimental results on six publicly available microarray datasets and one clinical dataset demonstrate that the proposed method performs better when compared with other state-of-the-art methods in terms of microarray data classification.

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Notes

  1. CLL_SUB_111 and Lung can be downloaded from: http://featureselection.asu.edu/datasets.php; Breast nd GCM can be downloaded from: http://portals.broadinstitute.org/cgi-bin/cancer/datasets.cgi; Tumors-11 and SRBCT can be downloaded from:http://datam.i2r.a-star.edu.sg/datasets/krbd/index.html.

References

  1. Lj VTV, Dai H, Mj VDV, He YD, Hart AA, Mao M, Peterse HL, Van DKK, Marton MJ, Witteveen AT (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871):530–536

    Article  Google Scholar 

  2. Kolali KM, Bazrafkan M (2016) A novel sparse coding algorithm for classification of tumors based on gene expression data. Med Biol Eng Comput 54(6):869

    Article  Google Scholar 

  3. Kurgan LA, Cios KJ, Tadeusiewicz R, Ogiela M, Goodenday LS (2001) Knowledge discovery approach to automated cardiac spect diagnosis. Artif Intell Med 23(2):149–169

    Article  PubMed  CAS  Google Scholar 

  4. Liao JC, Boscolo R, Yang YL, Tran LM, Sabatti C, Roychowdhury VP (2003) Network component analysis: reconstruction of regulatory signals in biological systems. Proc Natl Acad Sci USA 100(26):15522–15527

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Guo S, Guo D, Chen L, Jiang Q (2017) A l1-regularized feature selection method for local dimension reduction on microarray data. Comput Biol Chem 67:92–101

    Article  PubMed  CAS  Google Scholar 

  6. Jiang X, Gao J, Hong X, Cai Z (2014) Gaussian processes autoencoder for dimensionality reduction. In: Pacific-asia conference on knowledge discovery and data mining, pp 62–73

  7. Jiang X, Song X, Gao J, Cai Z, Zhang D (2016) Nonparametrically guided autoencoder with laplace approximation for dimensionality reduction. In: International joint conference on neural networks, pp 3378–3384

  8. Ramos J, Castellanos-Garzón JA, González-Briones A, Paz JFD, Corchado JM (2017) An agent-based clustering approach for gene selection in gene expression microarray. Interdisciplinary Sci Comput Life Sci 9(1):1–13

    Article  CAS  Google Scholar 

  9. Wang WZ, Yang BP, Feng CL, Wang JG, Xiong GR, Zhao TT, Zhang SZ (2017) Efficient sugarcane transformation via bar gene selection. Trop Plant Biol 10:1–9

    Article  CAS  Google Scholar 

  10. Sharbaf FV, Mosafer S, Moattar MH (2016) A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization. Genomics 107(6):231

    Article  CAS  Google Scholar 

  11. Lv J, Peng Q, Chen X, Sun Z (2016) A multi-objective heuristic algorithm for gene expression microarray data classification. Expert Syst Appl Int J 59:13–19

    Article  Google Scholar 

  12. Wang H, Jing X, Niu B (2017) A discrete bacterial algorithm for feature selection in classification of microarray gene expression cancer data. Know-Based Syst 126:8–19

    Article  Google Scholar 

  13. Zhou LT, Cao YH, Lv LL, Ma KL, Chen PS, Ni HF, Lei XD, Liu BC Feature selection and classification of urinary mrna microarray data by iterative random forest to diagnose renal fibrosis: a two-stage study, Scientific Reports 7

  14. Duda RO, Hart PE, Stork DG (2001) Pattern Classification (2nd Edition). Wiley, New York

    Google Scholar 

  15. Dy JG, Brodley CE (2004) Feature selection for unsupervised learning. J Mach Learn Res 5:845–889

    Google Scholar 

  16. He X, Cai D, Niyogi P (2005) Laplacian score for feature selection. NIPS 18:507–514

    Google Scholar 

  17. Mitra P, Murthy C, Pal SK (2002) Unsupervised feature selection using feature similarity. IEEE Trans Pattern Anal Mach Intell 24(3):301–312

    Article  Google Scholar 

  18. Nie F, Xiang S, Jia Y, Zhang C, Yan S (2008) Trace ratio criterion for feature selection. In: NCAI, pp 671–676

  19. Oh IS, Lee JS, Moon BR (2004) Hybrid genetic algorithms for feature selection. IEEE Trans Pattern Anal Mach Intell 26(11):1424–37

    Article  PubMed  Google Scholar 

  20. Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A, Benítez JM, Herrera F (2014) A review of microarray datasets and applied feature selection methods. Inf Sci 282(5):111–135

    Article  Google Scholar 

  21. Cai D, Zhang C, He X (2010) Unsupervised feature selection for multi-cluster data. In: SIGKDD, pp 333–342

  22. Zhao Z, Wang L, Liu H et al (2010) Efficient spectral feature selection with minimum redundancy. In: AAAI, pp 673–678

  23. Zhao Z, Liu H (2007) Spectral feature selection for supervised and unsupervised learning. In: ICML, pp 1151–1157

  24. Li Z, Yang Y, Liu J, Zhou X, Lu H (2012) Unsupervised feature selection using nonnegative spectral analysis. In: NCAI, pp 1026–1032

  25. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA (1999) Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Brain Res 501(2):205–14

    Google Scholar 

  26. Thomas JG, Olson JM, Tapscott SJ, Zhao LP (2001) An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles. Genome Res 11(7):1227

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Dudoit S, Yang YH, Callow MJ, Speed TP (2000) Statistical methods for identifying differentially expressed genes in replicated cdna microarray experiments. Stat sinica 12(1):111–139

    Google Scholar 

  28. Long AD, Mangalam HJ, Chan BY, Tolleri L, Hatfield GW, Baldi P (2001) Improved statistical inference from dna microarray data using analysis of variance and a bayesian statistical framework. analysis of global gene expression in escherichia coli k12. J Biol Chem 276(23):19937–44

    Article  PubMed  CAS  Google Scholar 

  29. Cai R, Hao Z, Yang X, Wen W (2009) An efficient gene selection algorithm based on mutual information. Neurocomputing 72(4-6):991–999

    Article  Google Scholar 

  30. Chuang LY, Yang CH, Li JC, Yang CH (2012) A hybrid bpso-cga approach for gene selection and classification of microarray data. J Comput Biol A J Comput Mol Cell Biol 19(1):68

    Article  CAS  Google Scholar 

  31. Wang Y, Tetko IV, Hall MA, Frank E, Facius A, Mayer KFX, Mewes HW (2005) Gene selection from microarray data for cancer classification-a machine learning approach. Comput Biol Chem 29(1):37–46

    Article  PubMed  CAS  Google Scholar 

  32. Gevaert O, Smet FD, Timmerman D, Moreau Y, Moor BD (2006) Predicting the prognosis of breast cancer by integrating clinical and microarray data with bayesian networks. Bioinformatics 22(14):e184—90

    Article  PubMed  Google Scholar 

  33. Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238

    Article  PubMed  Google Scholar 

  34. Battiti R (1994) Using mutual information for selecting features in supervised neural net learning. IEEE Trans Neural Netw 5(4):537–550

    Article  PubMed  CAS  Google Scholar 

  35. Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1):389–422

    Article  Google Scholar 

  36. Ghosh D, Chinnaiyan AM (2005) Classification and selection of biomarkers in genomic data using lasso. J Biomed Biotechnol 2005(2):147

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Wang YX, Liu JX, Gao YL, Zheng CH, Shang JL (2016) Differentially expressed genes selection via laplacian regularized low-rank representation method. Comput Biol Chem 65(1):185–192

    Article  PubMed  CAS  Google Scholar 

  38. Wang D, Liu JX, Gao YL, Yu J, Zheng CH, Xu Y (2016) An nmf-l2,1-norm constraint method for characteristic gene selection. Plos One 11(7):e0158494

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Zheng CH, Ng TY, Zhang D, Shiu CK (2011) Tumor classification based on non-negative matrix factorization using gene expression data. IEEE Trans Nanobioscience 10(2):86–93

    Article  PubMed  Google Scholar 

  40. Du S, Ma Y, Li S, Ma Y (2017) Robust unsupervised feature selection via matrix factorization. Neurocomputing 241:115–127

    Article  Google Scholar 

  41. Zhu P, Zuo W, Zhang L, Hu Q, Shiu SCK (2015) Unsupervised feature selection by regularized self-representation. Pattern Recogn 48(2):438–446

    Article  Google Scholar 

  42. Shang R, Zhang Z, Jiao L, Liu C, Li Y (2016) Self-representation based dual-graph regularized feature selection clustering. Neurocomputing 171(1):1242–1253

    Article  Google Scholar 

  43. Zhu P, Zhu W, Wang W, Zuo W, Hu Q (2017) Non-convex regularized self-representation for unsupervised feature selection. Image Vis Comput 60(1):22–29

    Article  Google Scholar 

  44. Liu Y, Liu K, Zhang C, Wang J, Wang X (2017) Unsupervised feature selection via diversity-induced self-representation. Neurocomputing 219:350–363

    Article  Google Scholar 

  45. Zhu X, Li X, Zhang S, Ju C, Wu X (2017) Robust joint graph sparse coding for unsupervised spectral feature selection. IEEE Trans Neural Netw Learn Syst 28(6):1263–1275

    Article  PubMed  Google Scholar 

  46. Lee DD, Seung HS (1999) Learning the parts of objects by non-negativ matrix factorization. Nature 401 (6755):788

    Article  PubMed  CAS  Google Scholar 

  47. Cai D, He X, Han J, Huang TS (2011) Graph regularized nonnegative matrix factorization for data representation. IEEE Trans Pattern Anal Mach Intell 33(8):1548–1560

    Article  PubMed  Google Scholar 

  48. Belkin M, Niyogi P (2002) Laplacian eigenmaps and spectral techniques for embedding and clustering. Adv Neural Inf Proces Syst 14(6):585–591

    Google Scholar 

  49. He X, Niyogi P (2003) Locality preserving projections. In: Advances in Neural Information Processing Systems, pp 186–197

  50. Hestenes MR (1969) Multiplier and gradient methods. J Optim Theory Appl 4(5):303–320

    Article  Google Scholar 

  51. Ito K, Kunisch K (2010) Lagrange multiplier approach to variational problems and applications. Society for Industrial and Applied Mathematics

  52. Tang C, Wang P, Zhang C, Li W (2017) Salient object detection via weighted low rank matrix recovery. IEEE Signal Process Lett 24(4):490–494

    Article  Google Scholar 

  53. Tang C, Cao L, Chen J, Zheng X (2017) Speckle noise reduction for optical coherence tomography images via non-local weighted group low-rank representation. Laser Phys Lett 14(5):056002

    Article  Google Scholar 

  54. Boyd S, Vandenberghe L (2004) Convex Optimization. Cambridge University Press, Cambridge

    Book  Google Scholar 

  55. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    Google Scholar 

  56. Platt JC (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers 10(4):61–74

    Google Scholar 

  57. Ho TK (2002) Random decision forests. In: International Conference on Document Analysis and Recognition, p 278

  58. Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832–844

    Article  Google Scholar 

  59. Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46 (3):175–185

    Google Scholar 

  60. Geisser S (1993) Predictive inference : an introduction. Chapman and Hall, London

    Book  Google Scholar 

  61. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: International Joint Conference on Artificial Intelligence, pp 1137–1143

  62. Devijver PA, Kittler J (1982) Pattern recognition: a statistical approach. Prentice/hall International, New Jersey

    Google Scholar 

  63. Cheng WC, Tsai ML, Chang CW, Huang CL, Chen CR, Shu WY, Lee YS, Wang TH, Hong JH, Li CY (2010) Microarray meta-analysis database (m(2)db): a uniformly pre-processed, quality controlled, and manually curated human clinical microarray database. Bmc Bioinformatics 11(1):421

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Guo S, Guo D, Chen L, Jiang Q (2016) A centroid-based gene selection method for microarray data classification. J Theor Biol 400:32–41

    Article  PubMed  CAS  Google Scholar 

  65. Chang CC, Lin CJ (2011) Libsvm: A library for support vector machines. ACM Trans Intell Syst Technol 2(27):1–27

    Article  Google Scholar 

  66. Zhou X, Tuck DP (2007) Msvm-rfe: extensions of svm-rfe for multiclass gene selection on dna microarray data. Bioinformatics 23(9):1106–1114

    Article  PubMed  CAS  Google Scholar 

  67. Cao KAL, Bonnet A, Gadat S (2009) Multiclass classification and gene selection with a stochastic algorithm. Comput Stat Data Anal 53(10):3601–3615

    Article  Google Scholar 

  68. Sun S, Peng Q, Shakoor A (2014) A kernel-based multivariate feature selection method for microarray data classification. Plos One 9(9):e102541

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Zhao G, Wu Y Feature subset selection for cancer classification using weight local modularity, Scientific Reports 6

  70. An S, Wang J, Wei J (2017) Local-nearest-neighbors-based feature weighting for gene selection. IEEE/ACM Trans Comput Biol Bioinform PP(99):1–1

    Article  Google Scholar 

  71. Chen KH, Wang KJ, Tsai ML, Wang KM, Adrian AM, Cheng WC, Yang TS, Teng NC, Tan KP, Chang KS (2014) Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm. Bmc Bioinform 15(1):49

    Article  Google Scholar 

  72. Li X, Li M, Yin M (2016) Multiobjective ranking binary artificial bee colony for gene selection problems using microarray datasets. IEEE/CAA J Automatica Sinica PP(99):1–16

    Google Scholar 

  73. Golub GH, Van Loan CF (1996) Matrix computations (3rd ed.) Johns Hopkins University Press, Baltimore

    Google Scholar 

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Acknowledgments

This research was supported by the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (No. CUG170654) and the National Natural Science Foundation of China (No. 61701451 and No. 61601261).

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    Authors and Affiliations

    1. School of Computer Science, China University of Geosciences, Wuhan, 430074, People’s Republic of China

      Chang Tang

    2. Institute of Cardiovascular Disease Research, Huai’an Second People’s Hospital Affiliated to Xuzhou Medical College, Huai’an, 223002, People’s Republic of China

      Lijuan Cao

    3. Department of Endocrinology and Metabolism, Puren Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, 430081, People’s Republic of China

      Xiao Zheng

    4. Department of Pharmacy, People’s Hospital of Lian’shui County, Huai’an, Jiangsu, 223300, People’s Republic of China

      Minhui Wang

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    1. Chang Tang
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    4. Minhui Wang
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    Correspondence to Minhui Wang.

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    Chang Tang and Lijuan Cao contributed equally to this work.

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    Tang, C., Cao, L., Zheng, X. et al. Gene selection for microarray data classification via subspace learning and manifold regularization. Med Biol Eng Comput 56, 1271–1284 (2018). https://doi.org/10.1007/s11517-017-1751-6

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