Genetic Interaction Network Interpretation: A Tidy Data Science Perspective.

ABSTRACT

As practitioners, we aim to provide a consolidated introduction of tidy data science along with routine packages for relational data representation and interpretation, with the focus on analytics related to human genetic interactions. We describe three showcases (also made available at https//23verse.github.io/gini ), all done so via the R one-liner, in this chapter defined as a sequential pipeline of elementary functions chained together achieving a complex task. We guide the readers through step-by-step instructions on (case 1) performing network module analysis of genetic interactions, followed by visualization and interpretation; (case 2) implementing a practical strategy of how to identify and interpret tissue-specific genetic interactions; and (case 3) carrying out interaction-based tissue clustering and differential interaction analysis. All showcases demonstrate simplistic beauty and efficient nature of this analytics. We anticipate that mastering a dozen of one-liners to efficiently interpret genetic interactions is very timely now; opportunities for computational translational research are arising for data scientists to harness therapeutic potential of human genetic interaction data that are ever-increasingly available.