essHi-C

essHi-C isolates the essential component of Hi-C contact matrices to separate specific chromosomal interaction signal from the non-specific, distance-dependent stochastic background and thereby improve analysis of genome folding.

Key Features:

• Essential-component extraction: Isolates the essential component of Hi-C matrices from the non-specific portion of the spectrum that resembles random matrices.
• Background separation: Addresses the high dynamic range of genomic distances probed in Hi-C assays and the associated stochastic background that convolves specific and non-specific interactions.
• Interaction-clarity improvement: Enhances the clarity of interaction patterns within Hi-C data.
• Robust TAD identification: Improves robustness of topologically associating domains (TADs) identification against variations in sequencing depth.
• Unsupervised clustering: Facilitates unsupervised clustering of experiments across different cell lines to compare genomic organization.
• Single-cell cell-cycle phasing: Recovers cell-cycle phasing of single cells based on Hi-C data.
• Feature extraction: Extracts significant biological and physical features from Hi-C matrices.

Scientific Applications:

• Genome folding analysis: Enables qualitative and quantitative studies of genome folding, including territorial organization, compartments, and topological domains.
• Comparative cell-line analysis: Supports clustering and comparison of Hi-C experiments across different cell lines without prior labels.
• TAD calling under variable depth: Improves detection of topological domains when sequencing depth varies across experiments.
• Single-cell cycle studies: Allows recovery of cell-cycle phasing from single-cell Hi-C datasets.
• Biophysical and biological inference: Facilitates extraction of biologically and physically relevant features from Hi-C contact matrices.

Methodology:

Separates the Hi-C spectral signal into an essential component and a non-specific, random-like portion, performs unsupervised clustering of experiments across cell lines, and recovers cell-cycle phasing from single-cell Hi-C data.