The Annals of Applied Statistics

Sparse integrative clustering of multiple omics data sets

Ronglai Shen, Sijian Wang, and Qianxing Mo

Full-text: Open access

Abstract

High resolution microarrays and second-generation sequencing platforms are powerful tools to investigate genome-wide alterations in DNA copy number, methylation and gene expression associated with a disease. An integrated genomic profiling approach measures multiple omics data types simultaneously in the same set of biological samples. Such approach renders an integrated data resolution that would not be available with any single data type. In this study, we use penalized latent variable regression methods for joint modeling of multiple omics data types to identify common latent variables that can be used to cluster patient samples into biologically and clinically relevant disease subtypes. We consider lasso [J. Roy. Statist. Soc. Ser. B 58 (1996) 267–288], elastic net [J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005) 301–320] and fused lasso [J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005) 91–108] methods to induce sparsity in the coefficient vectors, revealing important genomic features that have significant contributions to the latent variables. An iterative ridge regression is used to compute the sparse coefficient vectors. In model selection, a uniform design [Monographs on Statistics and Applied Probability (1994) Chapman & Hall] is used to seek “experimental” points that scattered uniformly across the search domain for efficient sampling of tuning parameter combinations. We compared our method to sparse singular value decomposition (SVD) and penalized Gaussian mixture model (GMM) using both real and simulated data sets. The proposed method is applied to integrate genomic, epigenomic and transcriptomic data for subtype analysis in breast and lung cancer data sets.

Article information

Source
Ann. Appl. Stat. Volume 7, Number 1 (2013), 269-294.

Dates
First available in Project Euclid: 9 April 2013

Permanent link to this document
http://projecteuclid.org/euclid.aoas/1365527199

Digital Object Identifier
doi:10.1214/12-AOAS578

Mathematical Reviews number (MathSciNet)
MR3086419

Zentralblatt MATH identifier
06171272

Keywords
Sparse integrative clustering latent variable approach penalized regression

Citation

Shen, Ronglai; Wang, Sijian; Mo, Qianxing. Sparse integrative clustering of multiple omics data sets. Ann. Appl. Stat. 7 (2013), no. 1, 269--294. doi:10.1214/12-AOAS578. http://projecteuclid.org/euclid.aoas/1365527199.


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References

  • Alizadeh, A. A., Eisen, M. B., Davis, E. E. et al. (2000). Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403 503–511.
  • Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehar, J., Kryukov, G. V., Sonkin, D., Reddy, A., Liu, M., Murray, L., Berger, M. F., Monahan, J. E., Morais, P., Meltzer, J., Korejwa, A., Jane-Valbuena, J., Mapa, F. A., Thibault, J., Bric-Furlong, E., Raman, P., Shipway, A., Engels, I. H., Cheng, J., Yu, G. K., Yu, J., Aspesi, P., de Silva, M., Jagtap, K., Jones, M. D., Wang, L., Hatton, C., Palescandolo, E., Gupta, S., Mahan, S., Sougnez, C., Onofrio, R. C., Liefeld, T., MacConaill, L., Winckler, W., Reich, M., Li, N., Mesirov, J. P., Gabriel, S. B., Getz, G., Ardlie, K., Chan, V., Myer, V. E. and Weber, B. L. (2012). The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483 603–307.
  • Beroukhim, R., Mermel, C. H., Porter, D., Wei, G., Raychaudhuri, S., Donovan, J., Barretina, J., Boehm, J. S., Dobson, J., Urashima, M., Mc Henry, K. T., Pinchback, R. M., Ligon, A. H., Cho, Y. J., Haery, L., Greulich, H., Reich, M., Winckler, W., Lawrence, M. S., Weir, B. A., Tanaka, K. E., Chiang, D. Y., Bass, A. J., Loo, A., Hoffman, C., Prensner, J., Liefeld, T., Gao, Q., Yecies, D., Signoretti, S., Maher, E., Kaye, F. J., Sasaki, H., Tepper, J. E., Fletcher, J. A., Tabernero, J., Baselga, J., Tsao, M., Demichelis, F., Rubin, M. A., Janne, P. A., Daly, M. J., Nucera, C., Levine, R. L., Ebert, B. L., Gabriel, S., Rustgi, A., Antonescu, C. R., Ladanyi, M., Letai, A., Garraway, L., Loda, M., Beer, D., True, L. D., Okamoto, A., Pomeroy, S. L., Singer, S., Golub, T. R., Lander, E. S., Getz, G. and Sellers, W. R. (2010). The landscape of somatic copy-number alteration across human cancers. Nature 463 899–905.
  • Cancer Genome Atlas Research Network (2008). Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455 1061–1068.
  • Carvalho, I., Milanezi, F., Martins, A., Reis, R. M. and Schmitt, F. (2005). Overexpression of platelet-derived growth factor receptor alpha in breast cancer is associated with tumour progression. Breast Cancer Res. 7 R788–R795.
  • Chen, H., Xing, H. and Zhang, N. R. (2011). Estimation of parent specific DNA copy number in tumors using high-density genotyping arrays. PLoS Comput. Biol. 7 e1001060, 15.
  • Chin, L. and Gray, J. W. (2008). Translating insights from the cancer genome into clinical practice. Nature 452 553–563.
  • Chitale, D., Gong, Y., Taylor, B. S., Broderick, S., Brennan, C., Somwar, R., Golas, B., Wang, L., Motoi, N., Szoke, J., Reinersman, J. M., Major, J., Sander, C., Seshan, V. E., Zakowski, M. F., Rusch, V., Pao, W., Gerald, W. and Ladanyi, M. (2009). An integrated genomic analysis of lung cancer reveals loss of DUSP4 in EGFR-mutant tumors. Nature 28 2773–2783.
  • Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. Ser. B 39 1–38.
  • Dudoit, S. and Fridlyand, J. (2002). A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biology 3 1–21.
  • Fan, J. and Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. J. Amer. Statist. Assoc. 96 1348–1360.
  • Fang, K. T. and Wang, Y. (1994). Number-Theoretic Methods in Statistics. Monographs on Statistics and Applied Probability 51. Chapman & Hall, London.
  • Feinberg, A. P. and Vogelstein, B. (1983). Hypomethylation distinguishes genes of some human cancers from their normal counterparts. Nature 301 89–92.
  • Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. Springer, New York.
  • Holliday, R. (1979). A new theory of carcinogenesis. Br. J. Cancer 40 513–522.
  • Holm, K., Hegardt, C., Staaf, J. et al. (2010). Molecular subtypes of breast cancer are associated with characteristic DNA methylation patterns. Breast Cancer Research 12 R36.
  • Hoshida, Y., Nijman, S. M., Kobayashi, M., Chan, J. A., Brunet, J. P., Chiang, D. Y., Villanueva, A., Newell, P., Ikeda, K., Hashimoto, M., Watanabe, G., Gabriel, S., Friedman, S. L., Kumada, H., Llovet, J. M. and Golub, T. R. (2003). Integrative transcriptome analysis reveals common molecular subclasses of human hepatocellular carcinoma. Cancer Research 69 7385–7392.
  • Irizarry, R. A., Ladd-Acosta, C., Carvalho, B., Wu, H., Brandenburg, S. A., Jeddeloh, J. A., Wen, B. and Feinberg, A. P. (2008). Comprehensive high-throughput arrays for relative methylation (CHARM). Genome Res. 18 780–790.
  • Jolliffe, I. T. (2002). Principal Component Analysis, 2nd ed. Springer, New York.
  • Kapp, A. V. and Tibshirani, R. (2007). Are clusters found in one dataset present in another dataset? Biostatistics 8 9–31.
  • Laird, P. W. (2003). The power and the promise of DNA methylation markers. Nat. Rev. Cancer 3 253–266.
  • Laird, P. W. (2010). Principles and challenges of genome-wide DNA methylation analysis. Nat. Rev. Genet. 11 191–203.
  • Lapointe, J., Li, C., Higgins, J. P., van de Rijn, M., Bair, E., Montgomery, K., Ferrari, M., Egevad, L., Rayford, W., Bergerheim, U., Ekman, P., DeMarzo, A. M., Tibshirani, R., Botstein, D., Brown, P. O., Brooks, J. D. and Pollack, J. R. (2003). Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proc. Natl. Acad. Sci. USA 101 811–816.
  • Le Cao, K. A., Martin, P. G., Robert-Granie, P. and Besse, P. (2009). Sparse canonical methods for biological data integration: Application to a cross-platform study. BMC Bioinformatics 26 34.
  • Ng, A. Y., Jordan, M. I. and Weiss, Y. (2002). On spectral clustering: Analysis and an algorithm. Adv. Neural Inf. Process. Syst. 2 849–856.
  • Olshen, A. B., Venkatraman, E. S., Lucito, R. and Wigler, M. (2004). Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5 557–572.
  • Olshen, A. B., Bengtsson, H., Neuvial, P., Spellman, P. T., Olshen, R. A. and Seshan, V. E. (2011). Parent-specific copy number in paired tumor-normal studies using circular binary segmentation. Bioinformatics 27 2038–2046.
  • Parkhomenko, E., Tritchler, D. and Beyene, J. (2009). Sparse canonical correlation analysis with application to genomic data integration. Stat. Appl. Genet. Mol. Biol. 8 Art. 1, 36.
  • Peng, J., Zhu, J., Bergamaschi, A., Han, W., Noh, D.-Y., Pollack, J. R. and Wang, P. (2010). Regularized multivariate regression for identifying master predictors with application to integrative genomics study of breast cancer. Ann. Appl. Stat. 4 53–77.
  • Perou, C. M., Jeffrey, S. S., van de Rijn, M. et al. (1999). Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc. Natl. Acad. Sci. USA 96 9212–9217.
  • Pollack, J. R., Sørlie, T., Perou, C. M. et al. (2002). Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors. Proc. Natl. Acad. Sci. USA 99 12963–12968.
  • Rohe, K., Chatterjee, S. and Yu, B. (2010). Spectral clustering and the high-dimensional stochastic block model. Available at arXiv:1007.1684.
  • Shen, R., Olshen, A. B. and Ladanyi, M. (2009). Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics 25 2906–2912.
  • Simon, R. (2010). Translational research in oncology: Key bottlenecks and new paradigms. Expert Reviews Molecular Medicine 12 e32.
  • Soneson, C., Lilljebjörn, H., Fioretos, T. and Fontes, M. (2010). Integrative analysis of gene expression and copy number alterations using canonical correlation analysis. BMC Bioinformatics 11 191.
  • Sorlie, T., Perou, C. M., Tibshirani, R. et al. (2001). Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci. USA 98 10869–10874.
  • TCGA Network (2011). Integrated genomic analyses of ovarian carcinoma. Nature 474 609–615.
  • Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc. Ser. B 58 267–288.
  • Tibshirani, R. and Walther, G. (2005). Cluster validation by prediction strength. J. Comput. Graph. Statist. 14 511–528.
  • Tibshirani, R. and Wang, P. (2008). Spatial smoothing and hot spot detection for CGH data using the fused lasso. Biostatistics 9 18–29.
  • Tibshirani, R., Saunders, M., Rosset, S., Zhu, J. and Knight, K. (2005). Sparsity and smoothness via the fused lasso. J. R. Stat. Soc. Ser. B Stat. Methodol. 67 91–108.
  • Tipping, M. E. and Bishop, C. M. (1999). Probabilistic principal component analysis. J. R. Stat. Soc. Ser. B Stat. Methodol. 61 611–622.
  • van Wieringen, W. N. and van de Wiel, M. A. (2009). Nonparametric testing for DNA copy number induced differential mRNA gene expression. Biometrics 65 19–29.
  • Vaske, C. J., Benz, S. C., Sanborn, J. Z., Earl, D., Szeto, C., Zhu, J., Haussler, D. and Stuart, J. M. (2010). Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 26 237–245.
  • Venkatraman, E. S. and Olshen, A. B. (2007). A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 23 657–663.
  • Waaijenborg, S., Verselewel de Witt Hamer, P. C. and Zwinderman, A. H. (2008). Quantifying the association between gene expressions and DNA-markers by penalized canonical correlaton analysis. Stat. Appl. Genet. Mol. Biol. 7 Art. 3, 29.
  • Wang, S. and Zhu, J. (2008). Variable selection for model-based high-dimensional clustering and its application to microarray data. Biometrics 64 440–448, 666.
  • Wang, S., Nan, B., Zhu, J. and Beer, D. G. (2008). Doubly penalized Buckley–James method for survival data with high-dimensional covariates. Biometrics 64 132–140, 323.
  • Wang, S., Nan, B., Zhou, N. and Zhu, J. (2009). Hierarchically penalized Cox regression with grouped variables. Biometrika 96 307–322.
  • Witten, D. M. and Tibshirani, R. J. (2009). Extensions of sparse canonical correlation analysis with applications to genomic data. Stat. Appl. Genet. Mol. Biol. 8 Art. 28, 29.
  • Witten, D. M., Tibshirani, R. and Hastie, T. (2009). A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10 515–534.
  • Yuan, M. and Lin, Y. (2006). Model selection and estimation in regression with grouped variables. J. R. Stat. Soc. Ser. B Stat. Methodol. 68 49–67.
  • Zhao, Y. and Simon, R. (2010). Development and validation of predictive indices for a continuous outcome using gene expression profiles. Cancer Inform. 9 105–114.
  • Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat. Methodol. 67 301–320.