Improving Hierarchical Models Using Historical Data with Applications in High-Throughput Genomics Data Analysis.

ABSTRACT: Modern high-throughput biotechnologies such as microarray and next generation sequencing produce a massive amount of information for each sample assayed. However, in a typical high-throughput experiment, only limited amount of data are observed for each individual feature, thus the classical 'large p, small n' problem. Bayesian hierarchical model, capable of borrowing strength across features within the same dataset, has been recognized as an effective tool in analyzing such data. However, the shrinkage effect, the most prominent feature of hierarchical features, can lead to undesirable over-correction for some features. In this work, we discuss possible causes of the over-correction problem and propose several alternative solutions. Our strategy is rooted in the fact that in

SUBMITTER: Li B 

PROVIDER: S-EPMC5599104 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature