An improved ant-based algorithm based on heaps merging and fuzzy c-means for clustering cancer gene expression data

Abstract

The microarray technology enables the analysis of the gene expression data and the understanding of the important biological processes in an efficient way. We have developed an efficient clustering scheme for microarray gene expression data based on correlation-based feature selection, ant-based clustering, fuzzy c-means algorithm and a novel heaps merging heuristic. The algorithm utilizes the feature selection algorithm to overcome the high-dimensionality problem encountered in bioinformatics domain. Based on extensive empirical analysis on microarray data, clustering quality of the ant-based clustering algorithm is enhanced with the use of fuzzy c-means algorithm and heaps merging heuristic. The performance of the proposed clustering scheme is compared with k-means, PAM algorithm, CLARA, self-organizing map, hierarchical clustering, divisive analysis clustering, self-organizing tree algorithm, hybrid hierarchical clustering, consensus clustering, AntClass algorithm and fuzzy c-means clustering algorithms. The experimental results indicate that the proposed clustering scheme yields better performance in clustering cancer gene expression data.

This is a preview of subscription content, log in to check access.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

References

  1. 1

    Dalton L, Ballarin V and Brun M 2009 Clustering algorithms: on learning, validation, performance and applications to genomics. Current Genomics 10: 430–445

    Google Scholar 

  2. 2

    Daxin J, Tang C and Zhang A 2004 Cluster analysis for gene expression data: a survey. IEEE Transactions on Knowledge and Data Engineering 16(11):1370–1386

    Google Scholar 

  3. 3

    De Souto M C P, Costa I G, De Araujo D S A, Ludermir T B and Schliep A 2008 Clustering cancer gene expression data: a comparative study. BMC Bioinformatics 9: 497

    Google Scholar 

  4. 4

    Hasan M J A and Ramakrishnan S 2011 A survey: hybrid evolutionary algorithms for cluster analysis. Artificial Intelligence Review 36(3): 179–204

    Google Scholar 

  5. 5

    Alon U, Barkai N and Notterman D A 1999 Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proceedings of the National Academy of Sciences of the United States of America 96: 6745–6750

    Google Scholar 

  6. 6

    Golub T R, Slonim D K and Tamayo P 1999 Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286: 531–537

    Google Scholar 

  7. 7

    Alizadeh A A, Eisesn M B and Davis R E 2000 Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403: 503–511

    Google Scholar 

  8. 8

    Dudoit S and Fridlyand J 2002 A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biology 3(7):1–21

    Google Scholar 

  9. 9

    Datta S and Datta S 2003 Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics 19(4): 459–466

    Google Scholar 

  10. 10

    Costa I G, de Carvalho F A T and de Souto M C P 2004 Comparative analysis of clustering methods for gene expression time course data. Genetics and Molecular Biology 27(4): 623–631

    Google Scholar 

  11. 11

    Iam-on N and Boongoen T 2012 A new locally weighted k-means for cancer-aided microarray data analysis. Journal of Medical Systems 36: 43–49

    Google Scholar 

  12. 12

    Castellanos-Garzon J A and Diaz F 2013 An evolutionary computational model applied to cluster analysis of DNA microarray data. Expert Systems with Applications 40(7): 2575–2591

    Google Scholar 

  13. 13

    Binu D 2015 Cluster analysis using optimization algorithms with newly designed objective functions. Expert Syst Appl 42(14): 5848–5859

    Google Scholar 

  14. 14

    Liu J and Pham T 2011 Fuzzy clustering for microarray data analysis: a review. Current Bioinformatics 6(4): 427–443

    Google Scholar 

  15. 15

    Bhattacharya A, Chowdhury N and De R K 2012 Comparative analysis of clustering and biclustering algorithms for grouping of genes: co-function and co-regulation. Current Bioinformatics 7: 63–76

    Google Scholar 

  16. 16

    Datta S and Mukhopadhyay S 2013 An in silico identification of human promoters: a soft computing based approach. Current Bioinformatics 8(3): 362–368

    Google Scholar 

  17. 17

    Bhattacharya A and De R K 2008 Divisive correlation clustering algorithm (DCCA) for grouping of genes: detecting varying patterns in expression profiles. Bioinformatics 24(11):1359–1366.

    Google Scholar 

  18. 18

    Bhattacharya A and De R K 2009 Bi-correlation clustering algorithm for determining a set of co-regulated genes. Bioinformatics 25(21):2795–2801

    Google Scholar 

  19. 19

    Bhattacharya A and De R K 2010 Average correlation clustering algorithm (ACCA) for grouping of co-regulated genes with similar pattern of variation in their expression values. Journal of Biomedical Informatics 43:560–568

    Google Scholar 

  20. 20

    Turner H, Bailey T and Krzanowski W 2005 Improved biclustering of microarray data demonstrated through systematic performance tests. Computational Statistics and Data Analysis 48(2):235–254.

    MathSciNet  MATH  Google Scholar 

  21. 21

    Santamaria R, Quintales L and Theron R 2007 Methods to bicluster validation and comparison in microarray data. In: Proceedings of 8th International Conference Intelligent Data Engineering and Automated Learning 780–789

  22. 22

    Filippone M, Masulli F and Rovetta S 2008 Stability and performances in biclustering algırithms. In: Proceedings of the International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics 91–101

  23. 23

    Ayadi W, Elloumi M and Hao J-K 2012 Bicfinder: a biclustering algorithm for microarray data analysis. Knowledge and Information Systems 30(2):341–358

    Google Scholar 

  24. 24

    Saber H B and Elloumi M 2015 A novel biclustering algorithm of binary microarray data: BiBincons and Bibinalter. BioData Mining 38:1–14

    Google Scholar 

  25. 25

    Eren K, Deveci M, Küçüktunc O and Çatalyürek Ü V 2013 A comparative analysis of biclustering algorithms for gene expression data. Brief Bioinformatics 14(3):279–292

    Google Scholar 

  26. 26

    Monmarche N, Slimane N and Venturini G 1999 AntClass: discovery of clusters in in numerical data by an hybridization of an ant colony with the Kmeans algorithm. Internal Report, Universite de Tours

  27. 27

    Monmarche N, Slimane N and Venturini G 1999 On improving clustering in numerical databases with artificial ants. Lecture Notes in Computer Science 1674: 626–635

    Google Scholar 

  28. 28

    Chandrashekar G and Sahin F 2014 A survey on feature selection methods. Computers and Electrical Engineering 40: 16–28

    Google Scholar 

  29. 29

    Glaab E 2011 Analysing functional genomics data using novel ensemble, consensus and data fusion techniques. Unpublished PhD Thesis, University of Nottingham, Nottingham, UK

  30. 30

    Loennstedt I and Speed T P 2002 Replicated microarray data. Statistica Sinica 12: 31–46

    MathSciNet  MATH  Google Scholar 

  31. 31

    Symth G K 2004 Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3(1): 1–25

    MathSciNet  Google Scholar 

  32. 32

    Boulesteix A and Strimmer K 2007 Partial least squares: a versatile tool for the analysis of high dimensional genomic data. Briefings in Bioinformatics 8: 32–44

    Google Scholar 

  33. 33

    Breiman L 2001 Random forests. Machine Learning 45(1): 5–32

    MATH  Google Scholar 

  34. 34

    Tusher V, Tibshirani R and Chu G 2001 Significance analysis of microarrays applied to ioinizing radiation response. Proceedings of the National Academy of Sciences of the United States of America 98: 5116–5121

    MATH  Google Scholar 

  35. 35

    Hall M A 1999 Correlation-based feature selection for machine learning. Unpublished PhD Thesis, University of Waikato, Hamilton, New Zealand

  36. 36

    Daxin J, Tang C and Zhang A 2004 Cluster analysis for gene expression data: a survey. IEEE Transactions on Knowledge and Data Engineering 16(11): 1370-1386

    Google Scholar 

  37. 37

    Xu R and Wunsch D 2005 Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3): 654–678

    Google Scholar 

  38. 38

    Han J and Kamber M 2006 Data mining concepts and techniques. San Francisco, Morgan Kaufmann

    Google Scholar 

  39. 39

    Jain A K 2010 Data clustering: 50 years beyond k-means. Pattern Recognition Letters 31: 651–656

    Google Scholar 

  40. 40

    Kaufman L and Rousseeuw P J 1990 Finding groups in data: an introduction to cluster analysis. New Jersey, John Wiley & Sons

  41. 41

    Park H S and Jun C H 2009 A simple and fast algorithm for k-medoids clustering. Expert Systems with Applications 36. 3336–3341

    Google Scholar 

  42. 42

    Aggarwal C C and Reddy C K 2013 Data clustering: algorithms and applications, San Francisco, CRC

    Google Scholar 

  43. 43

    Johnson R A and Wichern D W 2007 Applied multivariate statistical analysis. New Jersey, Prentice Hall

    Google Scholar 

  44. 44

    Herrero J, Valencia A, Dopazo J 2005 A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics 17:126–136

    Google Scholar 

  45. 45

    Chipman H and Tibschirani R 2006 Hybrid hierarchical clustering with applications to microarray data. Biostatistics 7(3): 286–301

    MATH  Google Scholar 

  46. 46

    Onan A 2013 A study of hybrid evolutionary algorithms for cluster analysis. Unpublished Master thesis, Ege University, Izmir, Turkey

  47. 47

    Onan A, Bulut H and Korukoğlu S 2017 An improved ant algorithm with LDA-based representation for text document clustering. Journal of Information Science 43(2): 275-292

    Google Scholar 

  48. 48

    Chandra E and Anuradha VP 2011 A survey on clustering algorithms for data in spatial database management systems. International Journal of Computer Applications 24(9): 19–26

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Hasan Bulut.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bulut, H., Onan, A. & Korukoğlu, S. An improved ant-based algorithm based on heaps merging and fuzzy c-means for clustering cancer gene expression data. Sādhanā 45, 160 (2020). https://doi.org/10.1007/s12046-020-01399-x

Download citation

Keywords

  • Gene expression data
  • gene selection
  • clustering
  • ant-based clustering
  • correlation-based feature selection
  • hybrid algorithms