@article{Applications:1383,
      recid = {1383},
      author = {Lu, Mengyin},
      title = {Generalized Adaptive Shrinkage Methods and Applications in  Genomics Studies},
      publisher = {University of Chicago},
      school = {Ph.D.},
      address = {2018-12},
      pages = {129},
      abstract = {Shrinkage procedures have played an important role in  helping improve estimation accuracy for a variety of  applications. In genomics studies, the gene-specific sample  statistics are usually noisy, especially when sample size  is limited. Hence some shrinkage methods (e.g. limma) have  been proposed to increase statistical power in identifying  differentially expressed genes. Motivated by the success of  shrinkage methods, Stephens (2016) proposed a novel  approach, Adaptive Shrinkage (ash) for large-scale  hypothesis testing including false discovery rate and  effect size estimation, based on the fundamental “unimodal  assumption” (UA) that the distribution of the actual  unobserved effects has a single mode.

Even though ash  primarily dealt with normal or student-t distributed  observations, the idea of UA can be widely applied to other  types of data. In this dissertation, we propose a general  flexible Bayesian shrinkage framework based on UA, which is  easily applicable to a wide range of settings. This  framework allows us to deal with data involving other noise  distributions (gamma, F, Poisson, binomial, etc.). We  illustrate its flexibility in a variety of genomics  applications including: differential gene expression  analysis on RNA-seq data; comparison between bulk RNA-seq  and single cell RNA-seq data; gene expression distribution  deconvolution for single cell RNA-seq data, etc.},
      url = {http://knowledge.uchicago.edu/record/1383},
      doi = {https://doi.org/10.6082/uchicago.1383},
}