Inferring disease associations of the long non-coding RNAs through non-negative matrix factorization

  • Ashis Kumer Biswas
  • Mingon Kang
  • Dong-Chul Kim
  • Chris H. Q. Ding
  • Baoju Zhang
  • Xiaoyong Wu
  • Jean X. Gao
Original Article


Long non-coding RNAs (lncRNAs) have been implicated in various biological processes, and are linked in many dysregulations. Over the past decade, researchers reported a large number of human disease associations with the lncRNAs, both intergenic lncRNAs (lincRNAs) and non-intergenic lncRNAs. Thanks to the next generation sequencing platform, RNA-seq, through which researchers also were able to quantify expression profiles of each of the lncRNAs in human tissue samples. In this article we adapted the non-negative matrix factorization method to develop a low-rank computational model that can describe the existing knowledge about both non-intergenic and intergenic lncRNA-disease associations represented in a two dimensional association matrix as well as convey a way of ranking disease causing lncRNAs. We proposed several NMF formulations for the problem and we found that the sparsity-constrained NMF obtained the best model among all the other models. By exploiting the inherent bi-clustering ability of the NMF models, we extracted several lncRNA groups and disease groups that possess biological significance. Moreover, we proposed an integrative NMF formulation where we incorporated along with the coding gene and lincRNA disease association data, prior knowledge about relationship networks among the coding genes and lincRNAs, and the RNA-seq expression profile data to identify potential lincRNA-coding gene co-modules with which we further enhanced the lincRNA-disease associations and untangled mysteries about functional chemistry of the intergenic lncRNAs. Experimental results show the superiority of our proposed method over two state-of-the-art clustering algorithms—k-means and hierarchical clustering.


Long non-coding RNAs Association inference Non-negative matrix factorization Bi-clustering Intergenic long non-coding RNA–gene co-modules 


Conflict of interest

The authors declare that they have no conflict of interest.

Research involving human participants and/or animals

This research neither involved any human participant nor animals. All the data-sets were collected from publicly accessible websites.

Informed consent

Not applicable to this research since no human participants, and animals were involved.


  1. Bauer-Mehren A, Rautschka M, Sanz F, Furlong LI (2010) DisGeNET: a Cytoscape plugin to visualize, integrate, search and analyze gene-disease networks. Bioinformatics 26(22):2924–2926CrossRefGoogle Scholar
  2. Berry MW, Browne M, Langville AN, Pauca VP, Plemmons RJ (2007) Algorithms and applications for approximate nonnegative matrix factorization. Computat Stat Data Anal 52(1):155–173CrossRefMathSciNetzbMATHGoogle Scholar
  3. Biswas AK, Gao JX, Zhang B, Wu X (2014) NMF-based LncRNA-disease association inference and bi-clustering. In: Proceedings of the IEEE international conference on Bioinformatics and Bioengineering (BIBE), pp 97–104Google Scholar
  4. Brunet JP, Tamayo P, Golub TR, Mesirov JP (2004) Metagenes and molecular pattern discovery using matrix factorization. Proc Natl Acad Sci 101(12):4164–4169CrossRefGoogle Scholar
  5. Cabili MN, Trapnell C, Goff L, Koziol M, Tazon-Vega B, Regev A, Rinn JL (2011) Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev 25(18):1915–1927CrossRefGoogle Scholar
  6. Cai D, He X, Wu X, Han J (2008) Non-negative matrix factorization on manifold. In: Data Mining. ICDM’08. Eighth IEEE International Conference on. IEEE, pp 63–72Google Scholar
  7. Chen G, Wang Z, Wang D, Qiu C, Liu M, Chen X, Zhang Q, Yan G, Cui Q (2013) LncRNADisease: a database for long-non-coding RNA-associated diseases. Nucl Acids Res 41(D1):D983–D986CrossRefGoogle Scholar
  8. Chen X, Yan GY (2013) Novel human lncRNA-disease association inference based on lncRNA expression profiles. Bioinformatics 29:2617–2624CrossRefGoogle Scholar
  9. Chung S, Nakagawa H, Uemura M, Piao L, Ashikawa K, Hosono N, Takata R, Akamatsu S, Kawaguchi T, Morizono T et al (2011) Association of a novel long non-coding RNA in 8q24 with prostate cancer susceptibility. Cancer Sci 102(1):245–252CrossRefGoogle Scholar
  10. Crick F et al (1970) Central dogma of molecular biology. Nature 227(5258):561–563CrossRefGoogle Scholar
  11. Devarajan K (2008) Nonnegative matrix factorization: an analytical and interpretive tool in computational biology. PLoS Comput Biol 4(7):e1000–1029CrossRefGoogle Scholar
  12. Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci 95(25):14863–14868CrossRefGoogle Scholar
  13. Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabási AL (2007) The human disease network. Proc Natl Acad Sci 104(21):8685–8690CrossRefGoogle Scholar
  14. Gu Q, Zhou J (2009) Local learning regularized nonnegative matrix factorization. In: Twenty-First International Joint Conference on Artificial IntelligenceGoogle Scholar
  15. Guo X, Gao L, Liao Q, Xiao H, Ma X, Yang X, Luo H, Zhao G, Bu D, Jiao F et al (2013) Long non-coding RNAs function annotation: a global prediction method based on bi-colored networks. Nucl Acids Res 41(2):e35–e35CrossRefGoogle Scholar
  16. Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA (2005) Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucl Acids Res 33(suppl 1):D514–D517Google Scholar
  17. Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53CrossRefGoogle Scholar
  18. Hoyer PO (2004) Non-negative matrix factorization with sparseness constraints. J Mach Learn Res 5:1457–1469MathSciNetzbMATHGoogle Scholar
  19. Hutchins LN, Murphy SM, Singh P, Graber JH (2008) Position-dependent motif characterization using non-negative matrix factorization. Bioinformatics 24(23):2684–2690CrossRefGoogle Scholar
  20. Kapranov P, Cheng J, Dike S, Nix DA, Duttagupta R, Willingham AT, Stadler PF, Hertel J, Hackermüller J, Hofacker IL et al (2007) RNA maps reveal new RNA classes and a possible function for pervasive transcription. Science 316(5830):1484–1488CrossRefGoogle Scholar
  21. Kim H, Park H (2007) Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis. Bioinformatics 23(12):1495–1502CrossRefGoogle Scholar
  22. Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791CrossRefGoogle Scholar
  23. Lee DD, Seung HS (2001) Algorithms for non-negative matrix factorization. In: Advances in neural information processing systems. pp 556–562Google Scholar
  24. Li JH, Liu S, Zhou H, Qu LH, Yang JH (2013) starBase v2. 0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucl Acids Res 42:D92–D97CrossRefGoogle Scholar
  25. Liao Q, Liu C, Yuan X, Kang S, Miao R, Xiao H, Zhao G, Luo H, Bu D, Zhao H et al (2011) Large-scale prediction of long non-coding RNA functions in a coding-non-coding gene co-expression network. Nucl Acids Res 39(9):3864–3878CrossRefGoogle Scholar
  26. Lin A, Wang RT, Ahn S, Park CC, Smith DJ (2010) A genome-wide map of human genetic interactions inferred from radiation hybrid genotypes. Genome Res 20(8):1122–1132CrossRefGoogle Scholar
  27. Machado-Lima A, del Portillo HA, Durham AM (2008) Computational methods in noncoding RNA research. J Math Biol 56(1–2):15–49CrossRefMathSciNetzbMATHGoogle Scholar
  28. Mercer TR, Dinger ME, Mattick JS (2009) Long non-coding RNAs: insights into functions. Nature Rev Genetics 10(3):155–159CrossRefGoogle Scholar
  29. Paatero P, Tapper U (1994) Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5(2):111–126CrossRefGoogle Scholar
  30. Pascual-Montano A, Carazo JM, Kochi K, Lehmann D, Pascual-Marqui RD (2006) Nonsmooth nonnegative matrix factorization (nsnmf). Pattern Anal Mach Intell IEEE Trans 28(3):403–415CrossRefGoogle Scholar
  31. Pauca VP, Piper J, Plemmons RJ (2006) Nonnegative matrix factorization for spectral data analysis. Linear Algebra Appl 416(1):29–47CrossRefMathSciNetzbMATHGoogle Scholar
  32. Prelić A, Bleuler S, Zimmermann P, Wille A, Bühlmann 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):1122–1129CrossRefGoogle Scholar
  33. Sacco LD, Baldassarre A, Masotti A (2011) Bioinformatics tools and novel challenges in long non-coding RNAs (lncRNAs) functional analysis. Int J Mol Sci 13(1):97–114CrossRefGoogle Scholar
  34. Taft RJ, Pang KC, Mercer TR, Dinger M, Mattick JS (2010) Non-coding RNAs: regulators of disease. J Pathol 220(2):126–139CrossRefGoogle Scholar
  35. Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander ES, Golub TR (1999) Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci 96(6):2907–2912CrossRefGoogle Scholar
  36. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L (2010) Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnol 28(5):511–515CrossRefGoogle Scholar
  37. Wilusz JE, Sunwoo H, Spector DL (2009) Long noncoding RNAs: functional surprises from the RNA world. Genes Dev 23(13):1494–1504CrossRefGoogle Scholar
  38. Yang X, Gao L, Guo X, Shi X, Wu H, Song F, Wang B (2014) A network based method for analysis of lncRNA-disease associations and prediction of lncRNAs implicated in diseases. PLOS One 9(1):e87–e797Google Scholar
  39. Yuan J, Wu W, Xie C, Zhao G, Zhao Y, Chen R (2014) NPInter v2. 0: an updated database of ncRNA interactions. Nucl Acids Res 42(D1):D104–D108CrossRefGoogle Scholar
  40. Zafeiriou S, Tefas A, Buciu I, Pitas I (2006) Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification. Neural Netw IEEE Trans 17(3):683–695CrossRefGoogle Scholar
  41. Zhang S, Li Q, Liu J, Zhou XJ (2011) A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. Bioinformatics 27(13):i401–i409CrossRefGoogle Scholar
  42. Zhang ZY, Li T, Ding C, Ren XW, Zhang XS (2010) Binary matrix factorization for analyzing gene expression data. Data Mining Knowl Discov 20(1):28–52CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Wien 2015

Authors and Affiliations

  • Ashis Kumer Biswas
    • 1
  • Mingon Kang
    • 1
  • Dong-Chul Kim
    • 2
  • Chris H. Q. Ding
    • 1
  • Baoju Zhang
    • 3
  • Xiaoyong Wu
    • 3
  • Jean X. Gao
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonUSA
  2. 2.Department of Computer ScienceUniversity of Texas Pan AmericanEdinburgUSA
  3. 3.College of Electronics and Communication EngineeringTianjin Normal UniversityTianjinChina

Personalised recommendations