Guiding the design of well-powered Hi-C experiments to detect differential loops

Abstract

Abstract Motivation Three-dimensional chromatin structure plays an important role in gene regulation by connecting regulatory regions and gene promoters. The ability to detect the formation and loss of these loops in various cell types and conditions provides valuable information on the mechanisms driving these cell states and is critical for understanding long-range gene regulation. Hi-C is a powerful technique for characterizing 3D chromatin structure; however, Hi-C can quickly become costly and labor-intensive, and proper planning is required to ensure efficient use of time and resources while maintaining experimental rigor and well-powered results. Results To facilitate better planning and interpretation of human Hi-C experiments, we conducted a detailed evaluation of statistical power using publicly available Hi-C datasets, paying particular attention to the impact of loop size on Hi-C contacts and fold change compression. In addition, we have developed Hi-C Poweraid, a publicly hosted web application to investigate these findings. For experiments involving well-replicated cell lines, we recommend a total sequencing depth of at least 6 billion contacts per condition, split between at least two replicates to achieve the power to detect differences in the majority of loops. For experiments with higher variation, more replicates and deeper sequencing depths are required. Values for specific cases can be determined by using Hi-C Poweraid. This tool simplifies Hi-C power calculations, allowing for more efficient use of time and resources and more accurate interpretation of experimental results. Availability and implementation Hi-C Poweraid is available as an R Shiny application deployed at http://phanstiel-lab.med.unc.edu/poweraid/, with code available at https://github.com/sarmapar/poweraid.