hicACT: an R package for improved detection of chromatin interactions from Hi-C Data via Aggregated Cauchy Test

Genome-wide chromatin conformation capture (3C) technologies such as Hi-C are commonly employed to study chromatin spatial organization. In particular, to identify statistically significant long-range chromatin interactions from Hi-C data, most existing methods such as Fit-Hi-C/FitHiC2 and HiCCUPS assume that all chromatin interactions are statistically independent. Such an independence assumption is reasonable at low resolution (e.g., 40Kb bin), but is invalid at high resolution (e.g., 5 or 10Kb bins) since spatial dependency of neighboring chromatin interactions is non-negligible at high resolution. Our previous hidden Markov model based methods accommodate spatial dependency, but are computationally intensive. HiC-ACT is an aggregated Cauchy test (ACT) based approach that post-processes results from methods assuming independence to improve the detection of chromatin interactions.

hicACT is a user-friendly R package that implements HiC-ACT. hicACT is flexible in application, only requiring the input of fragment/bin identifiers and corresponding raw p-value, and allows the user to specify a smoothing parameter based on the data resolution. Moreover, hicACT does not require any information about the underlying correlation structure in the data while being able to account for the inherent correlation between bin (loci) pairs.

The current version is a pre-release. For details, please refer to our hicACT readme on GitHub.

Comments and suggestions are welcome, please e-mail Taylor Lagler at tmlagler@live.unc.edu, Ming Hu at hum@ccf.org or Yun Li at yunli@med.unc.edu.