Regulatory-scale stripe analysis from single-cell Hi-C with scStripe
Chromatin stripes are a directional architectural feature of three-dimensional genome organization, but their analysis in single-cell Hi-C (scHi-C) at regulatory resolution is hindered by extreme sparsity. We developed scStripe, an imputation-free two-stage statistical framework that detects stripes from sparse cell-type--aggregated scHi-C maps and directly quantifies the resulting…
Chromatin stripes, a directional architectural feature in the three-dimensional genome organization, pose challenges in single-cell Hi-C (scHi-C) analysis at regulatory resolution due to extreme sparsity. Researchers created scStripe, an imputation-free two-stage statistical framework, to detect stripes from sparse cell-type aggregated scHi-C maps and quantify the aggregate-defined stripes in raw individual-cell contact maps using per-cell stripe scores.
In three benchmark settings, scStripe demonstrated the highest F1 scores and exhibited strong aggregate stripe enrichment. The detected stripes mirrored characteristic structural and regulatory features found in bulk data. Furthermore, per-cell stripe scores identified recurrent configurations of the EBF1-anchored stripe, captured genome-wide variation linked to cell type, cell-cycle phase, and developmental stage, and identified gene-stripe pairs with pronounced cell-type specificity using matched scRNA-seq.
Overall, scStripe offers a practical and scalable framework for regulatory-scale stripe analysis across single cells without the need for single-cell enhancement or 3D reconstruction.
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