  Medical research institutions have generated massive amounts of biological
data by genetically profiling hundreds of cancer cell lines. In parallel,
academic biology labs have conducted genetic screens on small numbers of cancer
cell lines under custom experimental conditions. In order to share information
between these two approaches to scientific discovery, this article proposes a
"frequentist assisted by Bayes" (FAB) procedure for hypothesis testing that
allows historical information from massive genomics datasets to increase the
power of hypothesis tests in specialized studies. The exchange of information
takes place through a novel probability model for multimodal genomics data,
which distills historical information pertaining to cancer cell lines and genes
across a wide variety of experimental contexts. If the relevance of the
historical information for a given study is high, then the resulting FAB tests
can be more powerful than the corresponding classical tests. If the relevance
is low, then the FAB tests yield as many discoveries as the classical tests.
Simulations and practical investigations demonstrate that the FAB testing
procedure can increase the number of effects discovered in genomics studies
while still maintaining strict control of type I error and false discovery
rates.
