scStripe framework detects chromatin stripes in sparse single-cell Hi-C data

Researchers have posted a preprint on bioRxiv describing scStripe, a computational framework designed to detect and quantify chromatin stripes — directional structural features of three-dimensional genome organisation — from the sparse contact maps generated by single-cell Hi-C (scHi-C) experiments.

Chromatin stripes arise at sites where architectural proteins extrude chromatin loops in one direction, and they are thought to reflect the spatial proximity of regulatory elements such as enhancers and promoters. Analysing them at single-cell resolution has been technically difficult because individual scHi-C maps contain very few detected contacts. scStripe addresses this by operating as a two-stage pipeline: stripes are first identified using aggregated contact maps from cell-type clusters, then quantified in raw individual-cell maps using per-cell stripe scores, avoiding imputation steps that can introduce artefacts.

Across three benchmark settings, scStripe achieved the highest F1 scores among compared methods and showed strong performance in detecting aggregate-defined stripes at the per-cell level. The tool is likely to be useful for researchers studying cell-to-cell variability in gene regulation, the relationship between chromatin architecture and transcriptional state, and the epigenomic underpinnings of development and disease. This is a preprint and has not yet been peer-reviewed.