Dirichlet process mixture model for correcting technical variation in single-cell gene expression data

33rd International Conference on Machine Learning, ICML 2016

Volumn 3, Issue , 2016, Pages 1691-1715

  Prabhakaran, Sandhya ^a   Azizi, Elham ^a   Carr, Ambrose ^a   Pe'er, Dana ^a  

^a Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CELLS; CYTOLOGY; GENE EXPRESSION; GENES; INFERENCE ENGINES; ITERATIVE METHODS; LEARNING SYSTEMS; MIXTURES;

DIRICHLET PROCESS MIXTURE MODEL; EMERGING TECHNOLOGIES; EXPERIMENTAL ERRORS; EXPRESSION MEASUREMENTS; GENE EXPRESSION DATA; GLOBAL NORMALIZATION; HIERARCHICAL BAYESIAN; INFERENCE ALGORITHM;

MOLECULAR BIOLOGY;

EID: 84998704868     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (42)

1. Amir, El-ad David, Davis, Kara L, Tadmor, Michelle D, Simonds, Erin F, Levine, Jacob H, Bendall, Sean C, Shenfeld, Daniel K, Krishnaswamy, Smita, Nolan, Garry P, and Pe'er, Dana, visne enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nature biotechnology, 31(6):545-552, 2013.
2. Anders, Simon and Huber, Wolfgang. Differential expression analysis for sequence count data. 2010.
3. Antoniak, Charles E. Mixtures of dirichlet processes with applications to bayesian nonparametric problems. The Annals of Statistics, pp. 1152-1174, 1974.
4. Bengio, Yoshua. Deep learning of representations: Looking forward. In Statistical language and speech processing, pp. 1-37. Springer, 2013.
5. Blei, David M. and Jordan, Michael I. Variational methods for the dirichlet process. In Brodley, Carla E. (ed.), Proceedings of the International Conference on Machine Learning (ICML 2004), volume 69. ACM International Conference Proceeding Series, 2004.
6. Brennecke, Philip, Anders, Simon, Kim, Jong Kyoung, Kolodziejczyk, Aleksandra A, Zhang, Xiuwei, Proserpio, Valentina, Baying, Bianka, Benes, Vladimir, Teichmann, Sarah A, Marioni, John C, et al. Accounting for technical noise in single-cell rna-seq experiments. Nature methods, 10(11):1093-1095, 2013.
7. Buettner, Florian, Natarajan, Kedar N, Casale, F Paolo, Proserpio, Valentina, Scialdone, Antonio, Theis, Fabian J, Teichmann, Sarah A, Marioni, John C, and Stegle, Oliver. Computational analysis of cell-to-cell heterogeneity in single-cell rna-sequencing data reveals hidden subpopulations of cells. Nature biotechnology, 33(2):155-160, 2015.
8. Di Palma, Serena and Bodenmiller, Bernd. Unraveling cell populations in tumors by single-cell mass cytometry. Current opinion in biotechnology, 31:122-129, 2015.
9. Diaz-Garcia, José A, Jáimez, Ramón Gutierrez, and Mardia, Kanti V. Wishart and pseudo-wishart distributions and some applications to shape theory. Journal of Multivariate Analysis, 63(1):73-87, 1997.
10. Fan, Jean, Salathia, Neeraj, Liu, Rui, Kaeser, Gwendolyn E, Yung, Yun C, Herman, Joseph L, Kaper, Fiona, Fan, Jian-Bing, Zhang, Kun, Chun, Jerold, et al. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nature Methods, 2016.
11. Gawad, Charles, Koh, Winston, and Quake, Stephen R. Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics. Proceedings of the National Academy of Sciences, 111(50):17947-17952, 2014.
12. Görür, Dilan and Rasmussen, Carl Edward. Dirichlet process gaussian mixture models: Choice of the base distribution. In Journal of Computer Science and Technology, pp. 653-664, 2010.
13. Hashimshony, Tamar, Wagner, Florian, Sher, Noa, and Yanai, Itai. Cel-seq: single-cell rna-seq by multiplexed linear amplification. Cell reports, 2(3):666-673, 2012.
14. Hollander, Myles and Wolfe, Douglas A. Nonparametric Statistical Methods. Wiley Interscience, 2nd edition, 1999.
15. Ishwaran, Hemant and James, Lancelot F. Gibbs sampling methods for stick-breaking priors. In Journal of the American Statistical Association, pp. 161-173, 2001.
16. Jaitin, Diego Adhemar, Kenigsberg, Ephraim, Keren-Shaul, Hadas, Elefant, Naama, Paul, Franziska, Zaretsky, Irina, Mildner, Alexander, Cohen, Nadav, Jung, Steffen, Tanay, Amos, et al. Massively parallel singlecell rna-seq for marker-free decomposition of tissues into cell types. Science, 343(6172):776-779, 2014.
17. Junker, Jan Philipp and van Oudenaarden, Alexander. Every cell is special: genome-wide studies add a new dimension to single-cell biology. Cell, 157(1):8-11, 2014.
18. Kharchenko, Peter V, Silberstein, Lev, and Scadden, David T. Bayesian approach to single-cell differential expression analysis. Nature methods, 11(7):740-742, 2014.
19. Klein, Allon M, Mazutis, Linas, Akartuna, Iike, Tallapragada, Naren, Veres, Adrian, Li, Victor, Peshkin, Leonid, Weitz, David A, and Kirschner, Marc W. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell, 161(5):1187-1201, 2015.
20. Korwar, Ramesh M and Hollander, Myles. Contributions to the theory of dirichlet processes. The Annals of Probability, pp. 705-711, 1973.
21. Kucukelbir, Alp and Blei, David M. Population empirical bayes.
22. Lamoureux, Christopher G and Lastrapes, William D. Heteroskedasticity in stock return data: volume versus garch effects. The Journal of Finance, 45(1):221-229, 1990.
23. Levine, Jacob H, Simonds, Erin F, Bendall, Sean C, Davis, Kara L, El-ad, D Amir, Tadmor, Michelle D, Litvin, Oren, Fienberg, Harris G, Jager, Astraea, Zunder, Eli R, et al. Data-driven phenotypic dissection of ami reveals progenitor-like cells that correlate with prognosis. Cell, 162(1):184-197, 2015.
24. Lilliefors, Hubert W. On the kolmogorov-smirnov test for normality with mean and variance unknown. Journal of the American Statistical Association, 62(318):399-402, 1967.
25. Macosko, Evan Z, Basu, Anindita, Satija, Rahul, Nemesh, James, Shekhar, Karthik, Goldman, Melissa, Tirosh, Itay, Bialas, Allison R, Kamitaki, Nolan, Martersteck, Emily M, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell, 161(5):1202-1214, 2015.
26. Navin, Nicholas E. Cancer genomics: one cell at a time. Genome Biol, 15:452, 2014.
27. Neal, Radford M. Markov chain sampling methods for dirichlet process mixture models. Journal of computational and graphical statistics, 9(2):249-265, 2000.
28. Ng, Andrew Y et al. On spectral clustering: Analysis and an algorithm. 2002.
29. Ohlssen, David I., Sharpies, Linda D., and Spiegelhalter, David J. Flexible random-effects models using bayesian semi-parametric models: applications to institutional comparisons. In Statistics in Medicine, volume 26(9), pp. 2088-2112. ACM International Conference Proceeding Series, 2007.
30. Oshlack, Alicia, Robinson, Mark D, Young, Matthew D, et al. From rna-seq reads to differential expression results. Genome biol, 11(12):220, 2010.
31. Paul, Franziska, Arkin, Yaara, Giladi, Amir, Jaitin, Diego Adhemar, Kenigsberg, Ephraim, Keren-Shaul, Hadas, Winter, Deborah, Lara-Astiaso, David, Gury, Meital, Weiner, Assaf, et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell, 163(7):1663-1677, 2015.
32. Rasmussen, Carl Edward. The infinite gaussian mixture model. 1999.
33. Satija, Rahul, Farrell, Jeffrey A, Gennert, David, Schier, Alexander F, and Regev, Aviv. Spatial reconstruction of single-cell gene expression data. Nature biotechnology, 33(5):495-502, 2015.
34. Schwartz, Lorraine. On bayes procedures. Zeitschrift fr Wahrscheinlichkeitstheorie und Verwandte Gebiete, 4(1):10-26, 1965. ISSN 0044-3719. doi: 10.1007/BF00535479.
35. Shalek, Alex K, Satija, Rahul, Adiconis, Xian, Gertner, Rona S, Gaublomme, Jellert T, Raychowdhury, Raktima, Schwartz, Schraga, Yosef, Nir, Malboeuf, Christine, Lu, Diana, et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature, 498(7453):236-240, 2013.
36. Stegle, Oliver. Computational and analytical challenges in single-cell transcriptomics. Nature Publishing Group, (January 2014): 133-145. ISSN 1471-0056. doi: 10. 1038/nrg3833.
37. Titterington, DM. Common structure of smoothing techniques in statistics. International Statistical Review/Revue Internationale de Statistique, pp. 141-170, 1985.
38. Vallejos, Catalina A., John C. Marioni and Richardson, Sylvia. Basics: Bayesian analysis of single-cell sequencing data. PLoS Computational Biology, 11(6):e1004333, 2015.
39. Van der Maaten, Laurens and Hinton, Geoffrey. Visualizing data using t-sne. Journal of Machine Learning Research, 9(2579-2605):85, 2008.
40. Wu, Yuefeng and Ghosal, Subhashis. The 11-consistency of dirichlet mixtures in multivariate bayesian density estimation. Journal of Multivariate Analysis, 101(10):2411-2419, 2010.
41. Yakowitz, Sidney J and Spragins, John D. On the identifiability of finite mixtures. The Annals of Mathematical Statistics, pp. 209-214, 1968.
42. Zeisel, Amit, Muñoz-Manchado, Ana B, Codeluppi, Simone, Lönnerberg, Peter, La Manno, Gioele, Jureus, Anna, Marques, Sueli, Munguba, Hermany, He, Liqun, Betsholtz, Christer, et al. Cell types in the mouse cortex and hippocampus revealed by single-cell rna-seq. Science, 347(6226):1138-1142, 2015.