Outcome after open repair of intact AAA across the United States is quite good. Older, sicker patients may benefit from nonoperative treatment or the potentially lower risk endovascular approaches.

There is a marked racial disparity in the treatment of patients with PAOD that may be caused in part by differences in the severity of disease or disease distribution.

The compartmental organization of mammalian genomes and its changes play important roles in distinct biological processes. Here, we introduce dcHiC, which utilizes a multivariate distance measure to identify significant changes in compartmentalization among multiple contact maps. Evaluating dcHiC on four collections of bulk and single-cell contact maps from in vitro mouse neural differentiation (n = 3), mouse hematopoiesis (n = 10), human LCLs (n = 20) and post-natal mouse brain development (n = 3 stages), we show its effectiveness and sensitivity in detecting biologically relevant changes, including those orthogonally validated. dcHiC reported regions with dynamically regulated genes associated with cell identity, along with correlated changes in chromatin states, subcompartments, replication timing and lamin association. With its efficient implementation, dcHiC enables high-resolution compartment analysis as well as standalone browser visualization, differential interaction identification and time-series clustering. dcHiC is an essential addition to the Hi-C analysis toolbox for the ever-growing number of bulk and single-cell contact maps. Available at: https://github.com/ay-lab/dcHiC.

Compartmental organization plays a role in important biological processes. However, its comparative analysis has been mainly limited to pairwise comparisons of contact maps with focus on finding compartment flips (e.g., A-to-B). Here, we introduce dcHiC, which utilizes Multiple Factor Analysis and a multivariate distance measure to systematically identify all compartmentalization differences among multiple contact maps. Evaluating dcHiC on three different collections of Hi-C data, we show its effectiveness and sensitivity in detecting biologically meaningful differences associated with cellular identity, gene expression, and lamin association. By providing a multivariate formulation, dcHiC immediately expands compartment analysis to new modalities in comparative genomics.