Empirical Bayes Shrinkage and False Discovery Rate Estimation, Allowing For Unwanted Variation

Understand

We combine two important ideas in the analysis of large-scale genomics experiments (e.g.

• experiments that aim to identify genes that are differentially expressed between two conditions).
• The first is use of Empirical Bayes (EB) methods to handle the large number of potentially-sparse effects, and estimate false discovery rates and related quantities.
• The second is use of factor analysis methods to deal with sources of unwanted variation such as batch effects and unmeasured confounders.