Enter the Matrix: Factorization Uncovers Knowledge from Omics

Trends in Genetics

Volumn 34, Issue 10, 2018, Pages 790-805

  Stein O'Brien, Genevieve L ^a,b   Arora, Raman ^c   Culhane, Aedin C ^d,e   Favorov, Alexander V ^a,f   Garmire, Lana X ^g   Greene, Casey S ^h,i   Goff, Loyal A ^b   Li, Yifeng ^j   Ngom, Aloune ^k   Ochs, Michael F ^l   Xu, Yanxun ^c   Fertig, Elana J ^a  

^a JOHNS HOPKINS UNIVERSITY   (United States)

^b JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c Johns Hopkins University   (United States)

^d DANA FARBER CANCER INSTITUTE   (United States)

^e HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^f VAVILOV INSTITUTE OF GENERAL GENETICS   (Russian Federation)

^g UNIVERSITY OF HAWAII CANCER CENTER   (United States)

^h UNIVERSITY OF PENNSYLVANIA   (United States)

ⁱ Alex's Lemonade Stand Foundation   (United States)

^j NATIONAL RESEARCH COUNCIL CANADA   (Canada)

^k UNIVERSITY OF WINDSOR   (Canada)

^l The College of New Jersey   (United States)

more..

Author keywords

deconvolution; dimension reduction; genomics; matrix factorization; single cell; unsupervised learning

Indexed keywords

AUTOPSY; BRAIN REGION; CLONAL VARIATION; DECOMPOSITION; GENE EXPRESSION; GENETIC BACKGROUND; GENOTYPE; INDEPENDENT COMPONENT ANALYSIS; MATRIX FACTORIZATION; METHODOLOGY; NON NEGATIVE MATRIX FACTORIZATION; OMICS; PHENOTYPE; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; REVIEW; WORKFLOW; ALGORITHM; GENOMICS; HUMAN; PROTEOMICS; STATISTICAL ANALYSIS; STATISTICS AND NUMERICAL DATA; SYSTEMS BIOLOGY;

ALGORITHMS; DATA INTERPRETATION, STATISTICAL; GENOMICS; HUMANS; PROTEOMICS; SYSTEMS BIOLOGY;

EID: 85051770269     PISSN: 01689525     EISSN: 13624555     Source Type: Journal    
DOI: 10.1016/j.tig.2018.07.003     Document Type: Review

References (127)

1. Bell, G., et al. Beyond the data deluge. Science 323 (2009), 1297–1298.
2. Sagoff, M., Data deluge and the human microbiome project. Issues Sci. Technol., 28, 2012 http://issues.org/28-4/sagoff-3/.
3. Alter, O., Discovery of principles of nature from mathematical modeling of DNA microarray data. Proc. Natl. Acad. Sci. U. S. A. 103 (2006), 16063–16064.
4. Heyn, P., et al. Introns and gene expression: cellular constraints, transcriptional regulation, and evolutionary consequences. Bioessays 37 (2015), 148–154.
5. Ochs, M.F., Fertig, E.J., Matrix factorization for transcriptional regulatory network inference. IEEE Symp. Comput. Intell. Bioinforma. Comput. Biol. Proc. 2012 (2012), 387–396.
6. Abdi, H., et al. Multiple factor analysis: principal component analysis for multitable and multiblock data sets. WIREs Comp. Stat. 5 (2013), 149–179.
7. Meng, C., et al. Dimension reduction techniques for the integrative analysis of multi-omics data. Brief. Bioinform. 17 (2016), 628–641.
8. Li, Y., et al. A review on machine learning principles for multi-view biological data integration. Brief. Bioinform. 19 (2016), 325–340.
9. Devarajan, K., Nonnegative matrix factorization: an analytical and interpretive tool in computational biology. PLoS Comput. Biol., 4, 2008, e1000029.
10. Ritchie, M.E., et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res., 43, 2015, e47.
11. Anders, S., Huber, W., Differential expression analysis for sequence count data. Genome Biol., 11, 2010, R106.
12. Xie, F., et al. BayCount: a Bayesian decomposition method for inferring tumor heterogeneity using RNA-Seq counts. bioRxiv, 2017, 10.1101/218511 Published online November 13, 2017.
13. Alexandrov, L.B., et al. Signatures of mutational processes in human cancer. Nature 500 (2013), 415–421.
14. Alexandrov, L.B., et al. Mutational signatures associated with tobacco smoking in human cancer. Science 354 (2016), 618–622.
15. Favorov, A.V., et al. A Markov chain Monte Carlo technique for identification of combinations of allelic variants underlying complex diseases in humans. Genetics 171 (2005), 2113–2121.
16. Zakeri, M., et al. Improved data-driven likelihood factorizations for transcript abundance estimation. Bioinformatics 33 (2017), i142–i151.
17. Bertagnolli, N.M., et al. SVD identifies transcript length distribution functions from DNA microarray data and reveals evolutionary forces globally affecting GBM metabolism. PLoS One, 8, 2013, e78913.
18. Peckner, R., et al. Specter: linear deconvolution as a new paradigm for targeted analysis of data-independent acquisition mass spectrometry proteomics. bioRxiv, 2017, 10.1101/152744 Published online September 8, 2017.
19. Yeung, K.Y., et al. Model-based clustering and data transformations for gene expression data. Bioinformatics 17 (2001), 977–987.
20. Jiang, D., et al. Cluster analysis for gene expression data: a survey. IEEE Trans. Knowl. Data Eng. 16 (2004), 1370–1386.
21. Venet, D., et al. Separation of samples into their constituents using gene expression data. Bioinformatics 17 (2001), S279–S287.
22. Abbas, A.R., et al. Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus. PLoS One, 4, 2009, e6098.
23. Erkkilä T., et al. Probabilistic analysis of gene expression measurements from heterogeneous tissues. Bioinformatics 26 (2010), 2571–2577.
24. Leek, J.T., Asymptotic conditional singular value decomposition for high-dimensional genomic data. Biometrics 67 (2011), 344–352.
25. Kelton, C.J., et al. The estimation of dimensionality in gene expression data using nonnegative matrix factorization. 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2015, 1642–1649.
26. Meng, C., et al. Dimension reduction techniques for the integrative analysis of multi-omics data. Brief. Bioinform. 17 (2016), 628–641.
27. Berry, M.W., et al. Algorithms and applications for approximate nonnegative matrix factorization. Comput. Stat. Data Anal. 52 (2007), 155–173.
28. Wang, Y.-X., Zhang, Y.-J., Nonnegative matrix factorization: a comprehensive review. IEEE Trans. Knowl. Data Eng. 25 (2013), 1336–1353.
29. Zhou, G., et al. Nonnegative matrix and tensor factorizations: an algorithmic perspective. IEEE Signal Process. Mag. 31 (2014), 54–65.
30. Lee, S.-I., Batzoglou, S., Application of independent component analysis to microarrays. Genome Biol., 4, 2003, R76.
31. Engreitz, J.M., et al. Independent component analysis: mining microarray data for fundamental human gene expression modules. J. Biomed. Bioinf. 43 (2010), 932–944.
32. Teschendorff, A.E., et al. Elucidating the altered transcriptional programs in breast cancer using independent component analysis. PLoS Comput. Biol., 3, 2007, e161.
33. Ochs, M.F., et al. A new method for spectral decomposition using a bilinear Bayesian approach. J. Magn. Reson. 137 (1999), 161–176.
34. Lee, D.D., Seung, H.S., Learning the parts of objects by non-negative matrix factorization. Nature 401 (1999), 788–791.
35. Moloshok, T.D., et al. Application of Bayesian decomposition for analysing microarray data. Bioinformatics 18 (2002), 566–575.
36. Kossenkov, A.V., et al. Determining transcription factor activity from microarray data using Bayesian Markov chain Monte Carlo sampling. Stud. Health Technol. Inform. 129 (2007), 1250–1254.
37. Mairal, J., et al. Online learning for matrix factorization and sparse coding. J. Mach. Learn. Res. 11 (2010), 19–60.
38. Wu, S., et al. Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks. Proc. Natl. Acad. Sci. U. S. A. 113 (2016), 4290–4295.
39. Hyvärinen, A., Oja, E., Independent component analysis: algorithms and applications. Neural Netw. 13 (2000), 411–430.
40. Fertig, E.J., et al. CoGAPS: an R/C++ package to identify patterns and biological process activity in transcriptomic data. Bioinformatics 26 (2010), 2792–2793.
41. Stein-O'Brien, G.L., et al. PatternMarkers & GWCoGAPS for novel data-driven biomarkers via whole transcriptome NMF. Bioinformatics 33 (2017), 1892–1894.
42. Dey, K.K., et al. Visualizing the structure of RNA-seq expression data using grade of membership models. PLoS Genet., 13, 2017, e1006599.
43. Biton, A., et al. Independent component analysis uncovers the landscape of the bladder tumor transcriptome and reveals insights into luminal and basal subtypes. Cell Rep. 9 (2014), 1235–1245.
44. Fertig, E.J., et al. Preferential activation of the hedgehog pathway by epigenetic modulations in HPV negative HNSCC identified with meta-pathway analysis. PLoS One, 8, 2013, e78127.
45. Bidaut, G., et al. Interpreting and comparing clustering experiments through graph visualization and ontology statistical enrichment with the ClutrFree Package. Ochs, M.F., (eds.) Biomedical Informatics for Cancer Research, 2010, Springer, 315–333.
46. Bidaut, G., et al. Determination of strongly overlapping signaling activity from microarray data. BMC Bioinformatics, 7, 2006, 99.
47. Xu, Y., et al. MAD Bayes for tumor heterogeneity – feature allocation with exponential family sampling. J. Am. Stat. Assoc. 110 (2015), 503–514.
48. Novembre, J., et al. Genes mirror geography within Europe. Nature 456 (2008), 98–101.
49. Engelhardt, B.E., Stephens, M., Analysis of population structure: a unifying framework and novel methods based on sparse factor analysis. PLoS Genet., 6, 2010, e1001117.
50. McCarthy, M.I., et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat. Rev. Genet. 9 (2008), 356–369.
51. Hackl, H., et al. Computational genomics tools for dissecting tumour-immune cell interactions. Nat. Rev. Genet. 17 (2016), 441–458.
52. Fertig, E.J., et al. Pattern identification in time-course gene expression data with the CoGAPS matrix factorization. Methods Mol. Biol. 1101 (2014), 87–112.
53. Nik-Zainal, S., et al. The life history of 21 breast cancers. Cell 149 (2012), 994–1007.
54. Roth, A., et al. PyClone: statistical inference of clonal population structure in cancer. Nat. Methods 11 (2014), 396–398.
55. Deshwar, A.G., et al. PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors. Genome Biol., 16, 2015, 35.
56. Lee, J., et al. Bayesian inference for intratumour heterogeneity in mutations and copy number variation. J. R. Stat. Soc. Ser. C. Appl. Stat. 65 (2016), 547–563.
57. Bar-Joseph, Z., et al. Studying and modelling dynamic biological processes using time-series gene expression data. Nat. Rev. Genet. 13 (2012), 552–564.
58. Liang, Y., Kelemen, A., Dynamic modeling and network approaches for omics time course data: overview of computational approaches and applications. Brief. Bioinform., 2017, 10.1093/bib/bbx036 Published online April 18, 2017.
59. Moloshok, T.D., et al. Application of Bayesian decomposition for analysing microarray data. Bioinformatics 18 (2002), 566–575.
60. Liebermeister, W., Linear modes of gene expression determined by independent component analysis. Bioinformatics 18 (2002), 51–60.
61. Ochs, M.F., et al. Detection of treatment-Induced changes in signaling pathways in gastrointestinal stromal tumors using transcriptomic data. Cancer Res. 69 (2009), 9125–9132.
62. Hill, S.M., et al. Inferring causal molecular networks: empirical assessment through a community-based effort. Nat. Methods 13 (2016), 310–318.
63. Stein-O'Brien, G., et al. Integrated time-course omics analysis distinguishes immediate therapeutic response from acquired resistance. bioRxiv, 2017, 10.1101/136564 Published online August 1, 2017.
64. Khatri, P., et al. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput. Biol., 8, 2012, e1002375.
65. Irizarry, R.A., et al. Gene set enrichment analysis made simple. Stat. Methods Med. Res. 18 (2009), 565–575.
66. Bauer-Mehren, A., et al. Pathway databases and tools for their exploitation: benefits, current limitations and challenges. Mol. Syst. Biol., 5, 2009, 290.
67. Tsui, I.F.L., et al. Public databases and software for the pathway analysis of cancer genomes. Cancer Inform. 3 (2007), 379–397.
68. The GTEx Consortium, The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348 (2015), 648–660.
69. Tan, J., et al. Unsupervised extraction of stable expression signatures from public compendia with an ensemble of neural networks. Cell Syst. 5 (2017), 63–71.
70. Kim, J.W., et al. Decomposing oncogenic transcriptional signatures to generate maps of divergent cellular states. Cell Syst., 5, 2017 105–18.e9.
71. Fertig, E.J., et al. Gene expression signatures modulated by epidermal growth factor receptor activation and their relationship to cetuximab resistance in head and neck squamous cell carcinoma. BMC Genomics, 13, 2012, 160.
72. Fertig, E.J., et al. Identifying context-specific transcription factor targets from prior knowledge and gene expression data. IEEE Trans. Nanobioscience 12 (2012), 142–149.
73. Segal, E., et al. A module map showing conditional activity of expression modules in cancer. Nat. Genet. 36 (2004), 1090–1098.
74. Subramanian, A., et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. 102 (2005), 15545–15550.
75. Zhu, X., et al. Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization. Peer J., 5, 2017, e2888.
76. DeTomaso, D., Yosef, N., FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data. BMC Bioinformatics, 17, 2016, 315.
77. Fertig, E.J., et al. Identifying context-specific transcription factor targets from prior knowledge and gene expression data. IEEE Trans. Nanobiosci. 12 (2013), 142–149.
78. Irizarry, R.A., et al. Multiple-laboratory comparison of microarray platforms. Nat. Methods 2 (2005), 345–350.
79. Fan, J., et al. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat. Methods 3 (2016), 241–244.
80. Trapnell, C., et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32 (2014), 381–386.
81. Townes, F.W., et al. Varying-censoring aware matrix factorization for single cell RNA-sequencing. bioRxiv, 2017, 10.1101/166736 Published online July 21, 2017.
82. Moon, K.R., et al. PHATE: a dimensionality reduction method for visualizing trajectory structures in high-dimensional biological data. bioRxiv, 2017, 10.1101/120378 Published online March 24, 2017.
83. Puram, S.V., et al. Single-vell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171 (2017), 1611–1624.
84. Hübschmann, D., et al. Deciphering programs of transcriptional regulation by combined deconvolution of multiple omics layers. bioRxiv, 2017, 10.1101/199547 Published online October 8, 2017.
85. Buettner, F., et al. f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq. Genome Biol., 18, 2017, 212.
86. Buettner, F., et al. Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects. bioRxiv, 2016, 10.1101/087775 Published online November 15, 2016.
87. van Dijk, D., et al. MAGIC: a diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data. bioRxiv, 2017, 10.1101/111591 Published online February 25, 2017.
88. Risso, D., et al. ZINB-WaVE: a general and flexible method for signal extraction from single-cell RNA-seq data. bioRxiv, 2017, 10.1101/125112 Published online November 2, 2017.
89. Pierson, E., Yau, C., ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol., 16, 2015, 241.
90. van der, M.L., Hinton, G., Visualizing data using t-SNE. J. Mach. Learn Res. 9 (2008), 2579–2605.
91. Alter, O., et al. Singular value decomposition for genome-wide expression data processing and modeling. Proc. Natl. Acad. Sci. U. S. A. 97 (2000), 10101–10106.
92. Fellenberg, K., et al. Correspondence analysis applied to microarray data. Proc. Natl. Acad. Sci. U. S. A. 98 (2001), 10781–10786.
93. Brunet, J.P., et al. Metagenes and molecular pattern discovery using matrix factorization. Proc. Natl. Acad. Sci. U. S. A. 101 (2004), 4164–4169.
94. Abdi, H., Williams, L.J., Principal component analysis. WIREs Comp. Stat. 2 (2010), 433–459.
95. Hyvärinen, A., et al. Independent Component Analysis. 2004, John Wiley & Sons.
96. Hardoon, D.R., et al. Canonical correlation analysis: an overview with application to learning methods. Neural. Comput. 16 (2004), 2639–2664.
97. Schölkopf, B., et al. Kernel principal component analysis. Schölkopf, B., (eds.) Advances In Kernel Methods – Support Vector Learning, 1999, MIT Press pp. 327–000.
98. Arora, R., Livescu, K., Kernel CCA for multi-view learning of acoustic features using articulatory measurements. Symposium on Machine Learning in Speech and Language Processing, 2012, MSLP.
99. Andrew, G., et al. Deep canonical correlation analysis. Proceedings of the 30th International Conference on Machine Learning, 2013, 1247–1255.
100. Ding, C., He, X., K-means clustering via principal component analysis. Proceedings of the 21st International Conference on Machine Learning, 2004, 29.
101. Arora, R., et al. Clustering by left-stochastic matrix factorization. Proceedings of the 28th International Conference on Machine Learning 28 (2011), 761–768.
102. Kulis, B., Metric learning: a survey. Found. Trends Mach. Learn. 5 (2013), 287–364.
103. Pritchard, J.K., et al. Inference of population structure using multilocus genotype data. Genetics 155 (2000), 945–959.
104. De la Torre, F., Black, M.J., A framework for robust subspace learning. Int. J. Comput. Vis. 54 (2003), 117–142.
105. Szeliski, R., Computer Vision: Algorithms and Applications. 2010 Published online September 3, 2010 http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf.
106. Candès, E.J., et al. Robust principal component analysis?. J. ACM, 58, 2011, 11.
107. Arora, R., et al. Stochastic optimization for PCA and PLS. 50th Annual Allerton Conference on Communication Control, and Computing, 2012, Allerton, 861–868.
108. Arora, R., et al. Stochastic optimization of PCA with capped MSG. Burges, C.J.C., (eds.) Advances in Neural Information Processing Systems, Vol. 26, 2013, Curran Associates, 1815–1823.
109. Goes, J. et al. (2014) Robust stochastic principal component analysis. In Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics (Kaski, S. and Corander, J., eds), pp. 266–274, PMLR.
110. Bickel, S., Scheffer, T., Multi-view clustering. Proceedings of the IEEE International Conference on Data Mining, 2004, 19–26.
111. Candès, E.J., Recht, B., Exact matrix completion via convex optimization. Found Comut. Math., 9, 2009, 717.
112. Argyriou, A., et al. Multi-task feature learning. Adv. Neural. Inf. Process. Syst. 19 (2007), 41–48.
113. Ando, R.K., Zhang, T., A framework for learning predictive structures from multiple tasks and unlabeled data. J. Mach. Learn. Res. 6 (2005), 1817–1853.
114. Cleary, B., Composite measurements and molecular compressed sensing for highly efficient transcriptomics. bioRxiv, 2017, 10.1101/091926 Published online January 2, 2017.
115. Aha, D.W., et al. Instance-based learning algorithms. Mach. Learn. 6 (1991), 37–66.
116. Arora, R., et al. Similarity-based clustering by left-stochastic matrix factorization. J. Mach. Learn. Res. 14 (2013), 1715–1746.
117. Liao, R., et al. CloudNMF: a MapReduce implementation of nonnegative matrix factorization for large-scale biological datasets. Genomics Proteomics Bioinf. 12 (2014), 48–51.
118. de Campos, C.P., et al. Discovering subgroups of patients from DNA copy number data using NMF on compacted matrices. PLoS One, 8, 2013, e79720.
119. Huang, S., et al. More is better: recent progress in multi-omics data integration methods. Front. Genet., 8, 2017, 84.
120. Hore, V., et al. Tensor decomposition for multiple-tissue gene expression experiments. Nat. Genet. 48 (2016), 1094–1100.
121. Durham, T.J., et al. PREDICTD parallel epigenomics data Imputation with cloud-based tensor decomposition. Nat. Commun., 9, 2018, 1402.
122. Zhu, Y., et al. Constructing 3D interaction maps from 1D epigenomes. Nat. Commun., 7, 2016, 10812.
123. Wang, M., et al. Three-way clustering of multi-tissue multi-individual gene expression data using constrained tensor decomposition. bioRxiv, 2017, 10.1101/229245 Published online December 5, 2017.
124. Kolda, T., Bader, B., Tensor decompositions and applications. SIAM Rev. 51 (2009), 455–500.
125. Mao, W., et al. Pathway-level information extractor (PLIER): a generative model for gene expression data. bioRxiv, 2017, 10.1101/116061 Published online December 16 2017.
126. Hofree, M., et al. Network-based stratification of tumor mutations. Nat. Methods, 10, 2013, 1108.
127. Liao, J.C., et al. Network component analysis: reconstruction of regulatory signals in biological systems. Proc. Natl. Acad. Sci. U. S. A. 100 (2003), 15522–15527.