1. Bayesian sparse reduced rank regression for high-dimensional genomic data with correlated outcomes
- Shi, Xingjie et al. 2019. “VIMCO: Variational Inference for Multiple Correlated Outcomes in Genome-Wide Association Studies” ed. Russell Schwartz. Bioinformatics 35(19): 3693–3700.
- Chen, Lisha, and Jianhua Z. Huang. 2012. “Sparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection.” Journal of the American Statistical Association 107(500): 1533–45.
- Hilafu, Haileab, Sandra E. Safo, and Lillian Haine. 2020. “Sparse Reduced-Rank Regression for Integrating Omics Data.” BMC Bioinformatics 21(1): 283.
- Chakraborty, Antik, Anirban Bhattacharya, and Bani K Mallick. 2020. “Bayesian Sparse Multiple Regression for Simultaneous Rank Reduction and Variable Selection.” Biometrika 107(1): 205–21.
-> They induce the sparsity on each row of C after estimating the coefficient matrix C = B*At.
- Goh, Gyuhyeong, Dipak K. Dey, and Kun Chen. 2017. “Bayesian Sparse Reduced Rank Multivariate Regression.” Journal of Multivariate Analysis 157: 14–28.
- Marttinen, Pekka et al. 2014. “Assessing Multivariate Gene-Metabolome Associations with Rare Variants Using Bayesian Reduced Rank Regression.” Bioinformatics 30(14): 2026–34.
- Wenming Zheng. 2014. “Multi-View Facial Expression Recognition Based on Group Sparse Reduced-Rank Regression.” IEEE Transactions on Affective Computing 5(1): 71–85.
- Yang, Dunfu, Gyuhyeong Goh, and Haiyan Wang. 2020. “A Fully Bayesian Approach to Sparse Reduced-Rank Multivariate Regression.” Statistical Modelling: 1471082X2094869.
- Zhu, Hongtu, Zakaria Khondker, Zhaohua Lu, and Joseph G. Ibrahim. 2014. “Bayesian Generalized Low Rank Regression Models for Neuroimaging Phenotypes and Genetic Markers.” Journal of the American Statistical Association 109(507): 977–90.
- Chen, Lisha, and Jianhua Z. Huang. 2012. “Sparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection.” Journal of the American Statistical Association 107(500): 1533–45.
- Hilafu H, Safo SE, Haine L. Sparse reduced-rank regression for integrating omics data. BMC Bioinformatics. 2020;21(1):283. doi:10.1186/s12859-020-03606-2
- Marttinen, Pekka et al. 2014. “Assessing Multivariate Gene-Metabolome Associations with Rare Variants Using Bayesian Reduced Rank Regression.” Bioinformatics 30(14): 2026–34.
- Goh, Gyuhyeong, Dipak K. Dey, and Kun Chen. 2017. “Bayesian Sparse Reduced Rank Multivariate Regression.” Journal of Multivariate Analysis 157: 14–28.
- Yang, Dunfu, Gyuhyeong Goh, and Haiyan Wang. 2020. “A Fully Bayesian Approach to Sparse Reduced-Rank Multivariate Regression.” Statistical Modelling: 1471082X2094869.
- Ray, P., Zheng, L., Lucas, J., & Carin, L. (2014). Bayesian joint analysis of heterogeneous genomics data. Bioinformatics, 30(10), 1370-1376.
- Clustering -- Raj, A., Stephens, M., & Pritchard, J. K. (2014). fastSTRUCTURE: variational inference of population structure in large SNP data sets. Genetics, 197(2), 573-589.
- Murua, A., Stanberry, L., & Stuetzle, W. (2008). On potts model clustering, kernel k-means and density estimation. Journal of Computational and Graphical Statistics, 17(3), 629-658.
- Zhou, X., & Schmidler, S. C. (2009). Bayesian parameter estimation in Ising and Potts models: A comparative study with applications to protein modeling. Department of Statistical Science, Duke University, Durham, NC.
- Li, F., Zhang, T., Wang, Q., Gonzalez, M. Z., Maresh, E. L., & Coan, J. A. (2015). Spatial Bayesian variable selection and grouping for high-dimensional scalar-on-image regression. The Annals of Applied Statistics, 9(2), 687-713.