References
- Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Methodol 1995;57:289-300.
- Storey JD. A direct approach to false discovery rates. J R Stat Soc Series B Stat Methodol 2002;64:479-498. https://doi.org/10.1111/1467-9868.00346
- Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci U S A 2003;100:9440-9445. https://doi.org/10.1073/pnas.1530509100
- Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Series B Methodol 1996;58:267-288.
- Wu TT, Chen YF, Hastie T, Sobel E, Lange K. Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics 2009;25:714-721. https://doi.org/10.1093/bioinformatics/btp041
- Alexander DH, Lange K. Stability selection for genome-wide association. Genet Epidemiol 2011;35:722-728. https://doi.org/10.1002/gepi.20623
- Yuan M, Lin Y. Model selection and estimation in regression with grouped variables. J R Stat Soc Series B Stat Methodol 2006;68:49-67. https://doi.org/10.1111/j.1467-9868.2005.00532.x
- Ma S, Song X, Huang J. Supervised group Lasso with applications to microarray data analysis. BMC Bioinformatics 2007;8:60. https://doi.org/10.1186/1471-2105-8-60
- Jolliffe IT. Springer Series in Statistics. Principal Component Analysis. New York: Springer-Verlag, 2002.
- Chen M, Cho J, Zhao H. Incorporating biological pathways via a Markov random field model in genome-wide association studies. PLoS Genet 2011;7:e1001353. https://doi.org/10.1371/journal.pgen.1001353
- Lee S, Epstein MP, Duncan R, Lin X. Sparse principal component analysis for identifying ancestry-informative markers in genome-wide association studies. Genet Epidemiol 2012;36:293-302. https://doi.org/10.1002/gepi.21621
- Sun H, Wang S. Network-based regularization for matched case-control analysis of high-dimensional DNA methylation data. Stat Med 2013;32:2127-2139. https://doi.org/10.1002/sim.5694
-
Lu Y, Liu PY, Xiao P, Deng HW. Hotelling's
$T^2$ multivariate profiling for detecting differential expression in microarrays. Bioinformatics 2005;21:3105-3113. https://doi.org/10.1093/bioinformatics/bti496 - Kong SW, Pu WT, Park PJ. A multivariate approach for integrating genome-wide expression data and biological knowledge. Bioinformatics 2006;22:2373-2380. https://doi.org/10.1093/bioinformatics/btl401
- Cheung YH, Wang G, Leal SM, Wang S. A fast and noise-resilient approach to detect rare-variant associations with deep sequencing data for complex disorders. Genet Epidemiol 2012;36:675-685. https://doi.org/10.1002/gepi.21662
- Park H, Niida A, Miyano S, Imoto S. Sparse overlapping group lasso for integrative multi-omics analysis. J Comput Biol 2015; 22:73-84. https://doi.org/10.1089/cmb.2014.0197
- Whittaker J. Graphical Models in Applied Multivariate Statistics. New York: John Wiley & Sons, 1990.
- Peng J, Wang P, Zhou N, Zhu J. Partial correlation estimation by joint sparse regression models. J Am Stat Assoc 2009;104:735-746. https://doi.org/10.1198/jasa.2009.0126
- Teschendorff AE, Menon U, Gentry-Maharaj A, Ramus SJ, Weisenberger DJ, Shen H, et al. Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res 2010;20:440-446. https://doi.org/10.1101/gr.103606.109
- Sun H, Wang S. Penalized logistic regression for high-dimensional DNA methylation data with case-control studies. Bioinformatics 2012;28:1368-1375. https://doi.org/10.1093/bioinformatics/bts145
- Chen Y, Ning Y, Hong C, Wang S. Semiparametric tests for identifying differentially methylated loci with case-control designs using Illumina arrays. Genet Epidemiol 2014;38:42-50. https://doi.org/10.1002/gepi.21774