Methods for the integration of multi-omics data: Mathematical aspects

BMC Bioinformatics

Volumn 17, Issue 2, 2016, Pages

  Bersanelli, Matteo ^a,b   Mosca, Ettore ^b   Remondini, Daniel ^a   Giampieri, Enrico ^a   Sala, Claudia ^a   Castellani, Gastone ^a   Milanesi, Luciano ^b  

^a UNIVERSITY OF BOLOGNA   (Italy)

^b INSTITUTE OF BIOMEDICAL TECHNOLOGIES   (Italy)

Author keywords

Data integration; Multi omics; Omics

Indexed keywords

LARGE DATASET;

BIOLOGICAL VARIABLES; INTEGRATIVE ANALYSIS; MATHEMATICAL ASPECTS; METHODOLOGICAL ASPECTS; MULTI-OMICS; NON TRIVIAL PROBLEMS; OMICS; TECHNOLOGICAL LIMIT;

DATA INTEGRATION;

ALGORITHM; BAYES THEOREM; BIOLOGICAL MODEL; GENOMICS; HUMAN; LEAST SQUARE ANALYSIS; PROCEDURES; SOFTWARE;

ALGORITHMS; BAYES THEOREM; GENOMICS; HUMANS; LEAST-SQUARES ANALYSIS; MODELS, GENETIC; SOFTWARE;

EID: 84954446439     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-015-0857-9     Document Type: Article

References (53)

1. Berger B, Peng J, Singh M. Computational solutions for omics data. Nat Rev Genet. 2013; 14(5):333-46. doi: 10.1038/nrg3433.
2. Kristensen VN, Lingjærde OC, Russnes HG, Vollan HKM, Frigessi A, Børresen-Dale A-L. Principles and methods of integrative genomic analyses in cancer. Nat Rev Cancer. 2014; 14(5):299-313. doi: 10.1038/nrc3721.
3. Lê Cao K-A, González I, Déjean S. Integromics: an r package to unravel relationships between two omics datasets. Bioinformatics. 2009; 25(21):2855-6.
4. Li W, Zhang S, Liu C-C, Zhou XJ. Identifying multi-layer gene regulatory modules from multi-dimensional genomic data.Bioinformatics. 2012; 28(19):2458-66. doi: 10.1093/bioinformatics/bts476.
5. Barabási A-L, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011; 12(1):56-68. doi: 10.1038/nrg2918.
6. Skilling J. Data analysis: a Bayesian tutorial. New York, USA: Oxford University Press; 2006.
7. Ferguson TS. A bayesian analysis of some nonparametric problems. Ann Stat. 1973; 1:209-30.
8. Heckerman D. A tutorial on learning with Bayesian networks, Learning in Graphical Models. Netherlands: Springer; 1998, pp. 301-354.
9. Gevaert O, De Smet F, Timmerman D, Moreau Y, De Moor B. Predicting the prognosis of breast cancer by integrating clinical and microarray data with bayesian networks.Bioinformatics. 2006; 22(14):184-90. doi: 10.1093/bioinformatics/btl230.
10. Friedman N, Linial M, Nachman I, Pe'er D. Using bayesian networks to analyze expression data. J Comput Biol. 2000; 7(3-4):601-20. doi: 10.1089/106652700750050961.
11. Akavia UD, Litvin O, Kim J, Sanchez-Garcia F, Kotliar D, Causton HC, et al. An integrated approach to uncover drivers of cancer. Cell. 2010; 143(6):1005-17. doi: 10.1016/j.cell.2010.11.013.
12. Shen R, Olshen AB, Ladanyi M. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics. 2009; 25(22):2906-12. doi: 10.1093/bioinformatics/btp543.
13. Chari R, Coe BP, Vucic EA, Lockwood WW, Lam WL. An integrative multi-dimensional genetic and epigenetic strategy to identify aberrant genes and pathways in cancer. BMC Syst Biol. 2010; 4(1):67.
14. Aure MR, Steinfeld I, Baumbusch LO, Liestøl K, Lipson D, Nyberg S, et al. Identifying in-trans process associated genes in breast cancer by integrated analysis of copy number and expression data. PLoS One. 2013; 8(1):53014. 10.1371/journal.pone.0053014.
15. Chen B-J, Causton HC, Mancenido D, Goddard NL, Perlstein EO, Pe'er D. Harnessing gene expression to identify the genetic basis of drug resistance. Mol Syst Biol. 2009; 5:310. doi: 10.1038/msb.2009.69.
16. Mosca E, Milanesi L. Network-based analysis of omics with multi-objective optimization. Mol Biosyst. 2013; 9(12):2971-80. doi: 10.1039/c3mb70327d.
17. Wang B, Mezlini AM, Demir F, Fiume M, Tu Z, Brudno M, et al. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods. 2014; 11(3):333-7. doi: 10.1038/nmeth.2810.
18. De Bie T, Tranchevent L-C, van Oeffelen LMM, Moreau Y. Kernel-based data fusion for gene prioritization. Bioinformatics. 2007; 23(13):125-32. doi: 10.1093/bioinformatics/btm187.
19. Louhimo R, Hautaniemi S. Cnamet: an r package for integrating copy number, methylation and expression data. Bioinformatics. 2011; 27(6):887-8.
20. Meng C, Kuster B, Culhane AC, Gholami AM. A multivariate approach to the integration of multi-omics datasets. BMC Bioinformatics. 2014; 15:162. doi: 10.1186/1471-2105-15-162.
21. Liu Y, Devescovi V, Chen S, Nardini C. Multilevel omic data integration in cancer cell lines: advanced annotation and emergent properties. BMC Syst Biol. 2013; 7:14. doi: 10.1186/1752-0509-7-14.
22. Trygg J, Wold S. Orthogonal projections to latent structures (o-pls). J Chemometrics. 2002; 16(3):119-28.
23. Rosipal R, Trejo LJ. Kernel partial least squares regression in reproducing kernel hilbert space. J Mach Learn Res. 2002; 2:97-123.
24. Bylesjö M, Eriksson D, Kusano M, Moritz T, Trygg J. Data integration in plant biology: the o2pls method for combined modeling of transcript and metabolite data. Plant J. 2007; 52(6):1181-91. doi: 10.1111/j.1365-313X.2007.03293.x.
25. James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning. New York, USA: Springer; 2013.
26. Antoniak CE. Mixtures of dirichlet processes with applications to bayesian nonparametric problems. Ann Stat. 1974; 2:1152-74.
27. Savage RS, Ghahramani Z, Griffin JE, de la Cruz BJ, Wild DL. Discovering transcriptional modules by bayesian data integration. Bioinformatics. 2010; 26(12):158-67. doi: 10.1093/bioinformatics/btq210.
28. Kirk P, Griffin JE, Savage RS, Ghahramani Z, Wild DL. Bayesian correlated clustering to integrate multiple datasets. Bioinformatics. 2012; 28(24):3290-7. doi: 10.1093/bioinformatics/bts595.
29. Yuan Y, Savage RS, Markowetz F. Patient-specific data fusion defines prognostic cancer subtypes. PLoS Comput Biol. 2011; 7(10):1002227. 10.1371/journal.pcbi.1002227.
30. Huttenhower C, Mutungu KT, Indik N, Yang W, Schroeder M, Forman JJ, et al. Detailing regulatory networks through large scale data integration. Bioinformatics. 2009; 25(24):3267-74. doi: 10.1093/bioinformatics/btp588.
31. Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the em algorithm. J R Stat Soc Ser B Methodol. 1977; 39:1-38.
32. Green PJ, Richardson S. Modelling heterogeneity with and without the dirichlet process. Scand J Stat. 2001; 28(2):355-75.
33. Tuncbag N, McCallum S, Huang S-SC, Fraenkel E. Steinernet: a web server for integrating 'omic' data to discover hidden components of response pathways. Nucleic Acids Res. 2012; 40(Web Server issue):505-9. doi: 10.1093/nar/gks445.
34. Cun Y, Fröhlich H. Netclass: an r-package for network based, integrative biomarker signature discovery. Bioinformatics. 2014; 30(9):1325-6. http://dx.doi.org/10.1093/bioinformatics/btu025.
35. Merelli I, Lió P, Milanesi L. Nuchart: an r package to study gene spatial neighbourhoods with multi-omics annotations. PLoS One. 2013; 8(9):75146. doi: 10.1371/journal.pone.0075146.
36. van Berkum NL, Lieberman-Aiden E, Williams L, Imakaev M, Gnirke A, Mirny LA, et al. Hi-c: a method to study the three-dimensional architecture of genomes. J Vis Exp. 2010;39. doi: 10.3791/1869.
37. Aerts S, Lambrechts D, Maity S, Van Loo P, Coessens B, De Smet F, et al. Gene prioritization through genomic data fusion. Nat Biotechnol. 2006; 24(5):537-44. doi: 10.1038/nbt1203.
38. Menichetti G, Remondini D, Panzarasa P, Mondragón RJ, Bianconi G. Weighted multiplex networks. PLoS One. 2014; 9(6):97857. doi: 10.1371/journal.pone.0097857.
39. Castellani G, Intrator N, Remondini D. Systems biology and brain activity in neuronal pathways by smart device and advanced signal processing. Front Genet. 2014; 5:1-20.
40. Menichetti G, Remondini D, Bianconi G. Correlations between weights and overlap in ensembles of weighted multiplex networks. Phys Rev E Stat Nonlin Soft Matter Phys. 2014; 90(6-1):062817.
41. Dong X, Frossard P, Vandergheynst P, Nefedov N. Clustering with multi-layer graphs: a spectral perspective. IEEE Trans Signal Process. 2012; 60(11):5820-31.
42. Wang X, Gulbahce N, Yu H. Network-based methods for human disease gene prediction. Brief Funct Genomics. 2011; 10(5):280-93. doi: 10.1093/bfgp/elr024.
43. Kondor RI, Lafferty J. Diffusion kernels on graphs and other discrete input spaces. In: ICML, vol. 2. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.: 2002. p. 315-22.
44. Hofree M, Shen JP, Carter H, Gross A, Ideker T. Network-based stratification of tumor mutations. Nat Methods. 2013; 10(11):1108-15. doi: 10.1038/nmeth.2651.
45. Vandin F, Upfal E, Raphael BJ. Algorithms for detecting significantly mutated pathways in cancer. J Comput Biol. 2011; 18(3):507-22. doi: 10.1089/cmb.2010.0265.
46. Qi Y, Suhail Y, Lin Y-y, Boeke JD, Bader JS. Finding friends and enemies in an enemies-only network: a graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions. Genome Res. 2008; 18(12):1991-2004.
47. Doyle PG, Snell JL. Random walks and electric networks. AMC. 1984; 10:12.
48. Suthram S, Beyer A, Karp RM, Eldar Y, Ideker T. Eqed: an efficient method for interpreting eqtl associations using protein networks. Mol Syst Biol. 2008; 4:162. doi: 10.1038/msb.2008.4.
49. Mirzaev I, Gunawardena J. Laplacian dynamics on general graphs. Bull Math Biol. 2013; 75(11):2118-49. doi: 10.1007/s11538-013-9884-8.
50. Li Y, Patra JC. Genome-wide inferring gene-phenotype relationship by walking on the heterogeneous network. Bioinformatics. 2010; 26(9):1219-24. doi: 10.1093/bioinformatics/btq108.
51. Vaske CJ, Benz SC, Sanborn JZ, Earl D, Szeto C, Zhu J, et al. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using paradigm. Bioinformatics. 2010; 26(12):237-45. doi: 10.1093/bioinformatics/btq182.
52. R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2008. ISBN 3-900051-07-0. http://www.R-project.org.
53. MATLAB. Version 7.10.0 (R2010a). Natick, Massachusetts: The MathWorks Inc; 2010.