Prioritizing transcriptomic and epigenomic experiments by using an optimization strategy that leverages imputed data

Abstract

Successful science often involves not only performing experiments well, but also choosing well among many possible experiments. In a hypothesis generation setting, choosing an experiment well means choosing an experiment whose results are interesting or novel. In this work, we formalize this selection procedure in the context of genomics and epigenomics data generation. Specifically, we consider the task faced by a scientific consortium such as the National Institutes of Health ENCODE Consortium, whose goal is to characterize all of the functional elements in the human genome. Given a list of possible cell types or tissue types (\"biosamples\") and a list of possible high throughput sequencing assays, we ask \"Which experiments should ENCODE perform next?\" We demonstrate how to represent this task as an optimization problem, where the goal is to maximize the information gained in each successive experiment. Compared with previous work that has addressed a similar problem, our approach has the advantage that it can use imputed data to tailor the selected list of experiments based on data collected previously by the consortium. We demonstrate the utility of our proposed method in simulations, and we provide a general software framework, named Kiwano, for selecting genomic and epigenomic experiments.