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A new FCA-based method for identifying biclusters in gene expression data

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Abstract

Biclustering has been very relevant within the field of gene expression data analysis. In fact, its main thrust stands in its ability to identify groups of genes that behave in the same way under a subset of samples (conditions). However, the pioneering algorithms of the literature has shown some limits in terms of the quality of unveiled biclusters. In this paper, we introduce a new algorithm, called BiFCA+, for biclustering microarray data. BiFCA+ heavily relies on the mathematical background of the formal concept analysis, in order to extract the set of biclusters. In addition, the Bond correlation measure is of use to filter out the overlapping biclusters. The extensive experiments, carried out on real-life datasets, shed light on BiFCA+’s ability to identify statistically and biologically significant biclusters.

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Notes

  1. We use a separator-free abbreviated form for the sets; e.g., \(\{I_{1}I_{2}I_{3}\}\) stands for the set of items \(\{I_{1}, I_{2}, I_{3}\}\).

  2. This may be either monotone increasing, monotone decreasing, up–down or down–up, etc.

  3. Available at https://github.com/mehdi-kaytoue/trimax.

  4. Available at http://arep.med.harvard.edu/biclustering/.

  5. Available at http://www.tik.ethz.ch/sop/bimax/.

  6. Available at http://arep.med.harvard.edu/biclustering/.

  7. The human B-cell lymphoma dataset version that we have does not contain the names of genes to perform other tests.

  8. Available at http://llama.mshri.on.ca/funcassociate/

  9. http://geneontology.org/

  10. Available at http://db.yeastgenome.org/cgi-bin/GO/goTermFinder

  11. The adjusted significance scores assess genes in each bicluster, which indicates how well they match with the different GO categories.

  12. http://geneontology.org/

References

  1. Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, Powell JI, Yang L, Marti GE, Moore T, Hudson JJ, Lu L, Lewis DB, Tibshirani R, Sherlock G, Chan WC, Greiner TC, Weisenburger DD, Armitage JO, Warnke R, Levy R, Wilson W, Grever MR, Byrd JC, Botstein D, Brown PO, Staudt LM (2000) Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling. Nature 403(6769):503–511

    Article  Google Scholar 

  2. Aswanikumar C, Srinivas S (2010) Concept lattice reduction using fuzzy k-means clustering. Expert Syst Appl 37(3):2696–2704. https://doi.org/10.1016/j.eswa.2009.09.026

    Article  Google Scholar 

  3. Ayadi W (2011) Algorithmes systematiques et stochastiques de biregroupement pour l’analyse des donnees biopuces. Ph.D. thesis, University of Angers, France

  4. Ayadi W, Elloumi M, Hao JK (2009) A biclustering algorithm based on a bicluster enumeration tree: application to DNA microarray data. BioData Mining 2:9

    Article  Google Scholar 

  5. Ayadi W, Elloumi M, Hao JK (2010) Iterated local search for biclustering of microarray data. In: pattern recognition in bioinformatics–5th IAPR international conference, PRIB 2010, Nijmegen, The Netherlands, September 22-24, 2010. Proceedings, pp. 219–229

  6. Ayadi W, Elloumi M, Hao JK (2012) Bicfinder: a biclustering algorithm for microarray data analysis. Knowl Inf Syst 30(2):341–358

    Article  Google Scholar 

  7. Ayadi W, Elloumi M, Hao JK (2012) Bimine+: an efficient algorithm for discovering relevant biclusters of DNA microarray data. Knowl Based Syst 35:224–234

    Article  Google Scholar 

  8. Barbut M, Monjardet B (1970) Ordre et classification: algèbre et combinatoire. Classiques Hachette. Hachette. https://books.google.fr/books?id=n3BpSgAACAAJ. Accessed Jan 2014

  9. Ben-Dor A, Chor B, Karp RM, Yakhini Z (2003) Discovering local structure in gene expression data: the order-preserving submatrix problem. J Comput Biol 10(3/4):373–384

    Article  Google Scholar 

  10. Bergmann S, Ihmels J, Barkai N (2004) Defining transcription modules using large-scale gene expression data. Bioinformatics 20(13):1993–2003

    Article  Google Scholar 

  11. Berriz GF, King OD, Bryant B, Sander C, Roth FP (2003) Characterizing gene sets with funcassociate. Bioinformatics 19:2502–2504

    Article  Google Scholar 

  12. Bleuler S, Prelic A, Zitzler E (2004) An EA framework for biclustering of gene expression data. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2004, 19-23 June 2004, Portland, OR, USA, pp. 166–173. https://doi.org/10.1109/CEC.2004.1330853

  13. Boyle EI, Weng S, Gollub J, Jin H, Botstein D, Cherry JM, Sherlock G (2004) GO: : Termfinder-open source software for accessing gene ontology information and finding significantly enriched gene ontology terms associated with a list of genes. Bioinformatics 20(18):3710–3715. https://doi.org/10.1093/bioinformatics/bth456

    Article  Google Scholar 

  14. Madeira SC, Oliveira AL (2004) Biclustering algorithms for biological data analysis: a survey. IEEE Trans Comput Biol Bioinform 1:24–45

    Article  Google Scholar 

  15. Cheng K, Law N, Chan Y, Siu W (2014) A joint framework for missing values estimation and biclusters detection in gene expression data. IJBRA 10(6):574–586. https://doi.org/10.1504/IJBRA.2014.065243

    Article  Google Scholar 

  16. Cheng K, Law N, Siu W (2013) Use of biclustering for missing value imputation in gene expression data. Artif Intell Res 2(2):96–108. https://doi.org/10.5430/air.v2n2p96

    Article  Google Scholar 

  17. Cheng KO, Law NF, Siu WC, Liew AWC (2008) Identification of coherent patterns in gene expression data using an efficient biclustering algorithm and parallel coordinate visualization. BMC Bioinform 9:210

    Article  Google Scholar 

  18. Cheng Y, Church GM (2000) Biclustering of expression data. In: proc of ISMB, UC San Diego, California, pp 93–103

  19. Cheng Y, Church GM (2006) Biclustering of expression data. Tech. rep., supplementary information

  20. Das S, Idicula SM (2010) Application of cardinality based grasp to the biclustering of gene expression data. Int J Comput Appl 1:44–53

    Google Scholar 

  21. Divina F, Aguilar-Ruiz JS (2007) A multi-objective approach to discover biclusters in microarray data. In: genetic and evolutionary computation conference, GECCO 2007, proceedings, London, England, UK, July 7–11, 2007, pp 385–392. https://doi.org/10.1145/1276958.1277038

  22. Divina F, AguilarRuiz JS (2006) Biclustering of expression data with evolutionary computation. IEEE Trans Knowl Data Eng 18(5):590–602

    Article  Google Scholar 

  23. Eren K, Deveci M, Küçüktunç O, Çatalyürek ÜV (2013) A comparative analysis of biclustering algorithms for gene expression data. Brief Bioinform 14(3):279–292. https://doi.org/10.1093/bib/bbs032

    Article  Google Scholar 

  24. Fisher RA (1922) On the interpretation of \(\chi ^{\mathit{2}}\) from contingency tables, and the calculation of P. J R Stat Soc 85(1):87–94. https://doi.org/10.2307/2340521

    Article  Google Scholar 

  25. Freitas A, Ayadi W, Elloumi M, Oliveira LJ, Hao JK (2013) Survey on biclustering of gene expression data. In: Elloumi M, Zomaya AY (eds) Biological knowledge discovery handbook: preprocessing, mining, and postprocessing of biological data. Wiley, Hoboken, New Jersey, pp 591–608

    Chapter  Google Scholar 

  26. Gallo CA, Carballido JA, Ponzoni I (2009) Microarray biclustering: a novel memetic approach based on the pisa platform. In: Pizzuti C, Ritchie MD, Giacobini M (eds) Evolutionary computation, machine learning and data mining in bioinformatics. EvoBIO 2009. Springer, Berlin, Heidelberg, pp 44–55

    Chapter  Google Scholar 

  27. Ganter B, Wille R (1999) Formal concept analysis–mathematical foundations. Springer

  28. Gasch AP, Eisen MB (2002) Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biol. https://doi.org/10.1186/gb-2002-3-11-research0059

    Article  Google Scholar 

  29. Henriques R, Antunes C, Madeira SC (2013) Methods for the efficient discovery of large item-indexable sequential patterns. In: New frontiers in mining complex patterns–second international workshop, NFMCP 2013, Held in Conjunction with ECML-PKDD 2013, Prague, Czech Republic, September 27, 2013, Revised Selected Papers, pp 100–116. https://doi.org/10.1007/978-3-319-08407-7_7

    Google Scholar 

  30. Henriques R, Antunes C, Madeira SC (2015) A structured view on pattern mining-based biclustering. Pattern Recognit 48(12):3941–3958. https://doi.org/10.1016/j.patcog.2015.06.018

    Article  Google Scholar 

  31. Henriques R, Madeira SC (2014) Bicpam: pattern-based biclustering for biomedical data analysis. Algorithm Mol Biol 9:27. https://doi.org/10.1186/s13015-014-0027-z

    Article  Google Scholar 

  32. Henriques R, Madeira SC (2014) Bicspam: flexible biclustering using sequential patterns. BMC Bioinform 15:130. https://doi.org/10.1186/1471-2105-15-130

    Article  Google Scholar 

  33. Henriques R, Madeira SC (2016) Bic2pam: constraint-guided biclustering for biological data analysis with domain knowledge. Algorithm Mol Biol 11:23. https://doi.org/10.1186/s13015-016-0085-5

    Article  Google Scholar 

  34. Henriques R, Madeira SC (2016) Bicnet: flexible module discovery in large-scale biological networks using biclustering. Algorithm Mol Biol 11:14. https://doi.org/10.1186/s13015-016-0074-8

    Article  Google Scholar 

  35. Hochreiter S, Bodenhofer U, Heusel M, Mayr A, Mitterecker A, Kasim A, Khamiakova T, Sanden SV, Lin D, Talloen W, Bijnens L, Göhlmann HWH, Shkedy Z, Clevert D (2010) FABIA: factor analysis for bicluster acquisition. Bioinformatics 26(12):1520–1527. https://doi.org/10.1093/bioinformatics/btq227

    Article  Google Scholar 

  36. Ignatov DI, Gnatyshak DV, Kuznetsov SO, Mirkin BG (2015) Triadic formal concept analysis and triclustering: searching for optimal patterns. Mach Learning 101(1–3):271–302. https://doi.org/10.1007/s10994-015-5487-y

    Article  MathSciNet  MATH  Google Scholar 

  37. Ihmels J, Bergmann S, Barkai N (2004) Defining transcription modules using large-scale gene expression data. Bioinformatics 20:1993–2003

    Article  Google Scholar 

  38. Kaytoue M, Kuznetsov SO, Macko J, Napoli A (2014) Biclustering meets triadic concept analysis. Ann Math Artif Intell 70(1–2):55–79. https://doi.org/10.1007/s10472-013-9379-1

    Article  MathSciNet  MATH  Google Scholar 

  39. Kaytoue M, Kuznetsov SO, Napoli A (2011) Biclustering numerical data in formal concept analysis. In: proc of ICFCA, Leuven, Belgium, pp 135–150

    Chapter  Google Scholar 

  40. Kaytoue M, Kuznetsov SO, Napoli A, Duplessis S (2011) Mining gene expression data with pattern structures in formal concept analysis. Inf Sci 181(10):1989–2001. https://doi.org/10.1016/j.ins.2010.07.007

    Article  MathSciNet  Google Scholar 

  41. Király A, Laiho A, Abonyi J, Gyenesei A (2014) Novel techniques and an efficient algorithm for closed pattern mining. Expert Syst Appl 41(11):5105–5114. https://doi.org/10.1016/j.eswa.2014.02.029

    Article  Google Scholar 

  42. Kumar CA (2012) Fuzzy clustering-based formal concept analysis for association rules mining. Appl Artif Intell 26(3):274–301

    Article  Google Scholar 

  43. Lehmann F, Wille R (1995) A triadic approach to formal concept analysis. In: Conceptual structures: applications, implementation and theory, third international conference on conceptual structures, ICCS ’95, Santa Cruz, California, USA, August 14–18, 1995, proceedings, pp 32–43. https://doi.org/10.1007/3-540-60161-9_27

    Chapter  Google Scholar 

  44. Li J, Kumar CA, Mei C, Wang X (2017) Comparison of reduction in formal decision contexts. Int J Approx Reason 80:100–122. https://doi.org/10.1016/j.ijar.2016.08.007

    Article  MathSciNet  MATH  Google Scholar 

  45. Li X, Shao MW, Zhao XM (2016) Constructing lattice based on irreducible concepts. Int J Mach Learning Cybern. https://doi.org/10.1007/s13042-016-0587-y

    Article  Google Scholar 

  46. Liu J, Li Z, Hu X, Chen Y (2009) Biclustering of microarray data with MOSPO based on crowding distance. BMC Bioinform. https://doi.org/10.1186/1471-2105-10-S4-S9

    Article  Google Scholar 

  47. Liu J, Li Z, Liu F, Chen Y (2008) Multi-objective particle swarm optimization biclustering of microarray data. In: 2008 IEEE international conference on bioinformatics and biomedicine, BIBM 2008, 3–5 November 2008, Philadephia, Pennsylvania, USA, pp 363–366. https://doi.org/10.1109/BIBM.2008.17

  48. Luan Y, Li H (2003) Clustering of time-course gene expression data using a mixed-effects model with b-splines. Bioinformatics 19(4):474–482

    Article  Google Scholar 

  49. Martínez R, Pasquier N, Pasquier C (2008) Genminer: mining non-redundant association rules from integrated gene expression data and annotations. Bioinformatics 24(22):2643–2644. https://doi.org/10.1093/bioinformatics/btn490

    Article  Google Scholar 

  50. Mitra S, Banka H (2006) Multi-objective evolutionary biclustering of gene expression data. Pattern Recognit 39:2464–2477

    Article  Google Scholar 

  51. Mondal KC, Pasquier N (2014) Galois closure based association rule mining from biological data. In: Elloumi M, Zomaya AY (eds) Biological knowledge discovery handbook: preprocessing, mining, and postprocessing of biological data. Wiley, Hoboken, New Jersey, pp 761–802

    Google Scholar 

  52. Mondal KC, Pasquier N, Mukhopadhyay A, Maulik U, Bandyopadhyay S (2012) A new approach for association rule mining and bi-clustering using formal concept analysis. In: proc of machine learning and data mining in pattern recognition (MLDM), Berlin, Germany, pp 86–101

  53. Mouakher A, Ben Yahia S (2016) Qualitycover: efficient binary relation coverage guided by induced knowledge quality. Inf Sci 355:58–73

    Article  Google Scholar 

  54. Nepomuceno JA, Lora AT, Nepomuceno-Chamorro IA, Aguilar-Ruiz JS (2015) Integrating biological knowledge based on functional annotations for biclustering of gene expression data. Comput Method Progr Biomed 119(3):163–180. https://doi.org/10.1016/j.cmpb.2015.02.010

    Article  Google Scholar 

  55. Omiecinski ER (2003) Alternative interest measures for mining associations in databases. IEEE Trans Knowl Data Eng 15:57–69

    Article  Google Scholar 

  56. Orzechowski P (2013) Proximity measures and results validation in biclustering–a survey. In: Artificial intelligence and soft computing–12th international conference, ICAISC 2013, Zakopane, Poland, June 9–13, 2013, proceedings, part II, pp 206–217. https://doi.org/10.1007/978-3-642-38610-7_20

    Chapter  Google Scholar 

  57. Pasquier N, Bastide Y, Taouil R, Lakhal L (1999) Discovering frequent closed itemsets for association rules. In: Beeri C, Buneman P (eds) ICDT. Springer, Berlin, Heidelberg, pp 398–416

    Google Scholar 

  58. Peddada S, Lobenhofer E, Li L, Afshari C, Weinberg C (2003) Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference. Bioinformatics 19:834–841

    Article  Google Scholar 

  59. Pensa RG, Besson J, Boulicaut JF (2004) A methodology for biologically relevant pattern discovery from gene expression data. In: proc of discovery science, pp 230–241

    Chapter  Google Scholar 

  60. Prelic A, Bleuler S, Zimmermann P, Wille A, Buhlmann P, Gruissem W, Hennig L, Thiele L, Zitzler E (2006) A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22(9):1122–1129

    Article  Google Scholar 

  61. Tanay A, Sharan R, Shamir R (2002) Discovering statistically significant biclusters in gene expression data. Bioinformatics 18:S136–S144

    Article  Google Scholar 

  62. Tavazoieand S, Hughes JD, Campbell MJ, Cho RJ, Church GM (1999) Systematic determination of genetic network architecturegenetics. Nat Genet 22:281–285

    Article  Google Scholar 

  63. Teng L, Chan L (2008) Discovering biclusters by iteratively sorting with weighted correlation coefficient in gene expression data. J Signal Process Syst 50:267–280

    Article  Google Scholar 

  64. Trabelsi C, Jelassi N, Ben Yahia S (2012) Scalable mining of frequent tri-concepts from folksonomies. In: Advances in knowledge discovery and data mining–16th Pacific-Asia conference, PAKDD 2012, Kuala Lumpur, Malaysia, May 29–June 1, 2012, proceedings, part II, pp 231–242. Springer-Verlag. https://doi.org/10.1007/978-3-642-30220-6_20

    Chapter  Google Scholar 

  65. Uno T, Asai T, Uchida Y, Arimura H (2004) An efficient algorithm for enumerating closed patterns in transaction databases. In: Discovery science, 7th international conference, DS 2004, Padova, Italy, October 2–5, 2004, proceedings, pp 16–31. https://doi.org/10.1007/978-3-540-30214-8_2

    Chapter  Google Scholar 

  66. Wang H, Wang W, Yang J, Yu PS (2002) Clustering by pattern similarity in large data sets. In: Proceedings of the 2002 ACM SIGMOD international conference on management of data, Madison, Wisconsin, June 3–6, 2002, pp 394–405. https://doi.org/10.1145/564691.564737

  67. Wei J, Wang S, Yuan X (2010) Ensemble rough hypercuboid approach for classifying cancers. IEEE Trans Knowl Data Eng 22(3):381–391. https://doi.org/10.1109/TKDE.2009.114

    Article  Google Scholar 

  68. Wille R (1982) Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival I (ed) Ordered Sets. Reidel, Dordrecht/Boston, pp 445–470

    Chapter  Google Scholar 

  69. Zhang Y, Zha H, Chu CH (2005) A time-series biclustering algorithm for revealing co-regulated genes. Proc 5th Int Conf Inf Technol 1:32–37

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Authors and Affiliations

  1. Faculty of Sciences of Tunis, University of Tunis El Manar, LIPAH-LR11ES14, 2092, Tunis, Tunisia

    Amina Houari & Sadok Ben Yahia

  2. National Higher Engineering School of Tunis, University of Tunis, LaTICE-LR11ES04, 1008, Tunis, Tunisia

    Wassim Ayadi

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  1. Amina Houari
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Houari, A., Ayadi, W. & Ben Yahia, S. A new FCA-based method for identifying biclusters in gene expression data. Int. J. Mach. Learn. & Cyber. 9, 1879–1893 (2018). https://doi.org/10.1007/s13042-018-0794-9

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