HCMB

HCMB normalizes highly sparse Hi-C contact matrices to remove systematic technical biases (mappability, GC content, restriction fragment length) and enable downstream chromosomal interaction analyses such as P(s) curves, topologically associated domains (TADs), and A/B compartments.

Key Features:

• Sparse Hi-C normalization: Normalizes highly sparse Hi-C contact matrices to correct for uneven contact counts.
• Bias correction: Removes systematic and technical biases arising from mappability, GC content, and restriction fragment lengths.
• Algorithmic approach: Employs an iterative solution of equations combined with a linear search and projection strategy to achieve matrix balancing.
• Robustness to sparsity: Preserves essential biological signals under extreme sparsity, including P(s) curves, TADs, and A/B compartments.
• Comparison to existing methods: Addresses limitations of the Knight-Ruiz (KR) algorithm on highly sparse matrices.
• Implementation: Implemented in Python.
• Validation: Validated on both simulated and experimental Hi-C datasets.

Scientific Applications:

• P(s) curve analysis: Enables computation and interpretation of distance-dependent contact probability (P(s)) from sparse Hi-C data.
• TAD detection: Facilitates identification and analysis of topologically associated domains (TADs) from normalized contact matrices.
• A/B compartment analysis: Supports detection of A/B compartments and large-scale compartmentalization signals.
• Preprocessing for chromosomal interaction studies: Serves as a preprocessing step for downstream analyses of genome organization from chromosome conformation capture data.

Methodology:

Normalization is performed via an iterative solution of equations that integrates a linear search and projection strategy; methods were validated on simulated and experimental Hi-C datasets and the software is implemented in Python.