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Differential function analysis: identifying structure and activation variations in dysregulated pathways

差分功能分析:识别失调功能的网络结构和失调活性

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Abstract

Complex diseases are generally caused by the dysregulation of biological functions rather than individual molecules. Hence, a major challenge of the systematical study on complex diseases is how to capture the differentially regulated biological functions, e.g., pathways. The traditional differential expression analysis (DEA) usually considers the changed expression values of genes rather than functions. Meanwhile, the conventional function-based analysis (e.g., PEA: pathway enrichment analysis) mainly considers the varying activation of functions but disregards the structure change of genetic elements of functions. To achieve precision medicine against complex diseases, it is necessary to distinguish both the changes of functions and their elements from heterogeneous dysregulated pathways during the disease development and progression. In this work, in contrast to the traditional DEA, we developed a new computational framework, namely differential function analysis (DFA), to identify the changes of element-structure and expression-activation of biological functions, based on comparative non-negative matrix factorization (cNMF). To validate the effectiveness of our method, we tested DFA on various datasets, which shows that DFA is able to effectively recover the differential element-structure and differential activation-score of pre-set functional groups. In particular, the analysis of DFA on human gastric cancer dataset, not only capture the changed network-structure of pathways associated with gastric cancer, but also detect the differential activations of these pathways (i.e., significantly discriminating normal samples and disease samples), which is more effective than the state-of-the-art methods, such as GSVA and Pathifier. Totally, DFA is a general framework to capture the systematical changes of genes, networks and functions of complex diseases, which not only provides the new insight on the simultaneous alterations of pathway genes and pathway activations, but also opens a new way for the network-based functional analysis on heterogeneous diseases.

创新点

复杂疾病通常由生物功能, 而不是单个分子的失调造成的。因此, 系统性地研究复杂疾病的主要挑战是如何捕捉差异调节的生物功能。传统的差异表达分析(DEA), 通常考虑基因, 而不是功能的改变的表达值。同时, 传统的基于功能的分析(例如, PEA:功能途径富集分析)主要考虑功能活性的变化, 而忽略了功能内遗传基因之间的结构变化。在这个工作中, 我们开发了一个新的差分功能分析(DFA)算法, 它能够同时识别遗传基因之间的结构和功能活性的变化。为了验证我们方法的有效性, 我们在各种数据集上测试DFA, 结果表明DFA是能够有效地还原功能内遗传基因之间的结构变化和功能活性的失调。总之, DFA提供了一个系统性地窥视复杂疾病的功能, 网络, 基因变化的工具。

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References

  1. Jin L, Zuo X Y, Su W Y, et al. Pathway-based analysis tools for complex diseases: a review. Genom Proteom Bioinform, 2014, 12: 210–220

    Article  Google Scholar 

  2. Panoutsopoulou K, Zeggini E. Finding common susceptibility variants for complex disease: past, present and future. Brief Funct Genom Proteom, 2009, 8: 345–352

    Article  Google Scholar 

  3. Freimer N B, Sabatti C. Human genetics: variants in common diseases. Nature, 2007, 445: 828–830

    Article  Google Scholar 

  4. Thomas D. Gene-environment-wide association studies: emerging approaches. Nat Rev Genet, 2010, 11: 259–272

    Article  Google Scholar 

  5. Cordell H J. Detecting gene-gene interactions that underlie human diseases. Nat Rev Genet, 2009, 10: 392–404

    Article  Google Scholar 

  6. Ashburner M, Ball C A, Blake J A, et al. Gene Ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 2000, 25: 25–29

    Google Scholar 

  7. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res, 2000, 28: 27–30

    Article  Google Scholar 

  8. Holmans P, Green E K, Pahwa J S, et al. Gene ontology analysis of GWA study data sets provides insights into the biology of bipolar disorder. Am J Hum Genet, 2009, 85: 13–24

    Article  Google Scholar 

  9. Zhang C C, Liu J, Shi Q Q, et al. Identification of phenotypic networks based on whole transcriptome by comparative network decomposition. In: Proceedings of Bioinformatics and Biomedicine (BIBM), Washington, 2015. 189–194

    Google Scholar 

  10. Subramanian A, Tamayo P, Mootha V K, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Nat Acad Sci, 2005, 102: 15545–15550

    Article  Google Scholar 

  11. Wang J, Huang Q, Liu Z P, et al. NOA: a novel network ontology analysis method. Nucleic Acids Res, 2011, 39: e87

    Article  Google Scholar 

  12. Zhang C, Wang J, Hanspers K, et al. NOA: a cytoscape plugin for network ontology analysis. Bioinformatics, 2013, 29: 2066–2067

    Article  Google Scholar 

  13. Tarca A L, Draghici S, Khatri P, et al. A novel signaling pathway impact analysis. Bioinformatics, 2009, 25: 75–82

    Article  Google Scholar 

  14. Martini P, Sales G, Massa M S, et al. Along signal paths: an empirical gene set approach exploiting pathway topology. Nucleic Acids Res, 2013, 41: 218–225

    Article  Google Scholar 

  15. Drier Y, Sheffer M, Domany E. Pathway-based personalized analysis of cancer. Proc Nat Acad Sci, 2013, 110: 6388–6393

    Article  Google Scholar 

  16. Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinform, 2013, 14: 1–15

    Article  Google Scholar 

  17. Khatri P, Sirota M, Butte A J. Ten years of pathway analysis: current approaches and outstanding challenges. Plos Comput Biol, 2012, 8: 1454–1459

    Article  Google Scholar 

  18. Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization. Nature, 1999, 401: 788–791

    Article  Google Scholar 

  19. Lee D D, Seung H S. Algorithms for non-negative matrix factorization. Adv Neural Inform Proc Syst, 2001, 13: 556–562

    Google Scholar 

  20. Wang Y X, Zhang Y J. Nonnegative matrix factorization: a comprehensive review. IEEE Trans Knowl Data Eng, 2013, 25: 1336–1353

    Article  Google Scholar 

  21. Jia Z L, Zhang X, Guan N Y, et al. Gene ranking of RNA-seq data via discriminant non-negative matrix factorization. Plos One, 2015, 10: e0137782

    Article  Google Scholar 

  22. Zhang X, Guan N Y, Jia Z L, et al. Semi-supervised projective non-negative matrix factorization for cancer classification. Plos One, 2015, 10: e0138814

    Article  Google Scholar 

  23. Zhang S H, Li Q J, Liu J, et al. A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. Bioinformatics, 2011, 27: i401–i409

    Article  Google Scholar 

  24. Leo T, Bjorn N. A framework for regularized non-negative matrix factorization, with Application to the analysis of gene expression data. Plos One, 2012, 7: e46331

    Article  Google Scholar 

  25. Lee C M, Mudaliar M A V, Haggart D R, et al. Simultaneous non-negative matrix factorization for multiple large scale gene expression datasets in toxicology. Plos One, 2012, 7: 1411

    Google Scholar 

  26. Ma H, Jia M, Shi Y K, et al. Semi-supervised nonnegative matrix factorization for microblog clustering based on term correlation. Web Technol Appl, 2014, 8709: 511–516

    Google Scholar 

  27. Seichepine N, Essid S, Fevotte C, et al. Soft nonnegative matrix co-factorization. IEEE Trans Signal Process, 2014, 22: 5940–5949

    Article  MathSciNet  Google Scholar 

  28. Liu H F, Wu Z H, Li X L, et al. Constrained nonnegative matrix factorization for image representation. IEEE Trans Patt Anal Mach Intell, 2012, 34: 1299–1311

    Article  Google Scholar 

  29. Wu Q Y, Wang Z Y, Li C S, et al. Protein functional properties prediction in sparsely-label PPI networks through regularized non-negative matrix factorization. BMC Syst Biology, 2015, 9: 1–14

    Article  Google Scholar 

  30. Fogel P, Young S S, Hawkins D M, et al. Inferential, robust non-negative matrix factorization analysis of microarray data. Bioinformatics, 2007, 23: 44–49

    Article  Google Scholar 

  31. Zafeiriou S, Tefas A, Buciu I, et al. Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification. IEEE Trans Neural Netw, 2006, 17: 683–695

    Article  Google Scholar 

  32. Jiang J J, Zhang H B, Xue Y. Fast local learning regularized nonnegative matrix factorization. Adv Comput Environm Sci, 2012, 142: 67–75

    Article  Google Scholar 

  33. Gu Q Q, Zhou J. Local learning regularized nonnegative matrix factorization. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena, 2009. 1046–1051

    Google Scholar 

  34. Cai D, He X F, Wu X Y, et al. Non-negative matrix factorization on manifold. In: Proceedings of IEEE International Conference on Data Mining, Pisa, 2008. 63–72

    Google Scholar 

  35. Liu Y L, Du J L, Wang F. Non-negative matrix factorization with sparseness constraints for credit risk assessment. In: Proceedings of IEEE International Conference on Grey Systems and Intelligent Services, Macau, 2013. 211–214

    Google Scholar 

  36. Liu C L, Ma J W. Automatic non-negative matrix factorization clustering with competitive sparseness constraints. Intell Comput Methodol, 2014, 8589: 118–125

    Google Scholar 

  37. Hoyer P O. Non-negative matrix factorization with sparseness constraints. J Mach Learn Res, 2004, 5: 1457–1469

    MathSciNet  MATH  Google Scholar 

  38. Canadas-Quesada F J, Vera-Candeas P, Ruiz-Reyes N, et al. Percussive/harmonic sound separation by non-negative matrix factorization with smoothness/sparseness constraints. Eur J Audio Speech Music Proc, 2014, 2014: 1–17

    Article  Google Scholar 

  39. Zhang S, Liu C C, Li W, et al. Discovery of multi-dimensional modules by integrative analysis of cancer genomic data. Nucleic Acids Res, 2012, 40: 9379–9391

    Article  Google Scholar 

  40. Gao Y, Church G. Improving molecular cancer class discovery through sparse non-negative matrix factorization. Bioinformatics, 2005, 21: 3970–3975

    Article  Google Scholar 

  41. Kim H. Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis. Bioinformatics, 2007, 23: 1495–1502

    Article  Google Scholar 

  42. Peng C, Wong K C, Rockwood A, et al. Multiplicative algorithms for constrained non-negative matrix factorization. In: Proceedings of IEEE International Conference on Data Mining, Brussels, 2012. 1068–1073

    Google Scholar 

  43. Cui J, Li F, Wang G Q, et al. Gene-expression signatures can distinguish gastric cancer grades and stages. Plos One, 2011, 6: 1387

    Google Scholar 

  44. Frances N, Zeichner S B, Francavilla M, et al. Gastric small-cell carcinoma found on esophagogastroduodenoscopy: a case report and literature review. Case Rep Oncol Med, 2013, 2013: 475961

    Google Scholar 

  45. Hu K W, Chen F H. Identification of significant pathways in gastric cancer based on protein-protein interaction networks and cluster analysis. Genet Mol Biol, 2012, 35: 701–708

    Article  Google Scholar 

  46. Shimoda T, Matsutani T, Yoshida H, et al. A case of gastric cancer associated with systemic lupus erythematosus and nephrotic syndrome. Nihon Shokakibyo Gakkai Zasshi, 2013, 110: 1797–1803

    Google Scholar 

  47. Axon A T. Relationship between Helicobacter pylori gastritis, gastric cancer and gastric acid secretion. Adv Med Sci, 2007, 52: 55–60

    Google Scholar 

  48. Lee J, Jung K, Kim Y S, et al. Diosgenin inhibits melanogenesis through the activation of phosphatidylinositol-3-kinase pathway (PI3K) signaling. Life Sci, 2007, 81: 249–254

    Article  Google Scholar 

  49. Rappaport N, Nativ N, Stelzer G, et al. MalaCards: an integrated compendium for diseases and their annotation. Datab J Biolog Datab Curat, 2013, 2013: 1429–1438

    Google Scholar 

  50. Croft D, O’Kelly G, Wu G, et al. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res, 2011. 39(Database issue): D691–D697

    Article  Google Scholar 

  51. Zhao J, Zhou Y W, Zhang X J, et al. Part mutual information for quantifying direct associations in networks. Proc Nat Acad Sci, 2016, 113: 5130–5135

    Article  Google Scholar 

  52. Zhang X J, Liu K Q, Liu Z P, et al. NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference. Bioinformatics, 2013, 29: 106–113

    Article  Google Scholar 

  53. Chen L N, Liu R, Liu Z P, et al. Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Sci Rep, 2012, 2: 342

    Google Scholar 

  54. Liu R, Wang X D, Aihara K, et al. Early diagnosis of complex diseases by molecular biomarkers, network biomarkers, and dynamical network biomarkers. Med Res Rev, 2013, 34: 455–478

    Article  Google Scholar 

  55. Liu R, Chen P, Aihara K, et al. Identifying early-warning signals of critical transitions with strong noise by dynamical network markers. Sci Rep, 2015, 5: 17501

    Article  Google Scholar 

  56. Zeng T, Zhang C C, Zhang W W, et al. Deciphering early development of complex diseases by progressive module network. Methods, 2014, 67: 334–343

    Article  Google Scholar 

  57. Yu X T, Li G J, Chen L N. Prediction and early diagnosis of complex diseases by edge-network. Bioinformatics, 2014, 30: 852–859

    Article  Google Scholar 

  58. Yu X T, Zeng T, Wang X D, et al. Unravelling personalized dysfunctional gene network of complex diseases based on differential network model. J Transl Med, 2015, 13: 1–13

    Article  Google Scholar 

  59. Zeng T, Wang D C, Wang X D, et al. Prediction of dynamical drug sensitivity and resistance by module network rewiring-analysis based on transcriptional profiling. Drug Resist Update, 2014, 17: 64–76

    Article  Google Scholar 

  60. Zeng T, Zhang W W, Yu X T, et al. Big-data-based edge biomarkers: study on dynamical drug sensitivity and resistance in individuals. Brief Bioinform, 2015, 21: 863–874

    Google Scholar 

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Correspondence to Juan Liu, Tao Zeng or Luonan Chen.

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Zhang, C., Liu, J., Shi, Q. et al. Differential function analysis: identifying structure and activation variations in dysregulated pathways. Sci. China Inf. Sci. 60, 012108 (2017). https://doi.org/10.1007/s11432-016-0030-6

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