HyperChIP: identification of hypervariable signals across ChIP-seq or ATAC-seq samples

Abstract

Identifying genomic regions with hypervariable ChIP-seq or ATAC-seq signals across given samples is essential for large-scale epigenetic studies. In particular, the hypervariable regions across tumors from different patients indicate their heterogeneity and can contribute to revealing potential cancer subtypes and the associated epigenetic markers. We present HyperChIP as the first complete statistical tool for the task. HyperChIP uses scaled variances that account for the mean-variance dependence to rank genomic regions, and it increases the statistical power by diminishing the influence of true hypervariable regions on model fitting. A pan-cancer case study illustrates the practical utility of HyperChIP.