Bayesian inference with historical data-based informative priors improves detection of differentially expressed genes.

ABSTRACT:

Motivation

Modern high-throughput biotechnologies such as microarray are capable of producing a massive amount of information for each sample. However, in a typical high-throughput experiment, only limited number of samples were assayed, thus the classical 'large p, small n' problem. On the other hand, rapid propagation of these high-throughput technologies has resulted in a substantial collection of data, often carried out on the same platform and using the same protocol. It is highly desirable to utilize the existing data when performing analysis and inference on a new dataset.

Results

Utilizing existing data can be carried out in a straightforward fashion under the Bayesian framework in which the repository of historical data can be exploited to build informative priors a

SUBMITTER: Li B 

PROVIDER: S-EPMC4907396 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature