multiHiCcompare

multiHiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets with more than 2 groups and multiple samples per group.

Key Features:

• Joint normalization: Implements cyclic loess and fast loess (fastlo) methods adapted to jointly normalize Hi-C data across multiple datasets.
• Distance-dependent difference detection: Provides a general linear model (GLM) framework adapting the edgeR package to detect differences in Hi-C data in a distance dependent manner.
• Multi-group support: Extends the original HiCcompare package to allow for Hi-C experiments with more than 2 groups and multiple samples per group.
• Sparse matrix input: Operates on processed Hi-C data in the form of sparse upper triangular matrices.
• Tab-separated text file input: Accepts four column (chromosome, region1, region2, IF) tab-separated text files storing chromatin interaction matrices.

Scientific Applications:

• Comparative Hi-C analysis: Enables comparative analysis of multiple Hi-C datasets to detect chromatin interaction differences between experimental conditions.
• Epigenetic signature recovery: Applied to auxin-treated versus untreated experiments and CTCF depletion experiments to recover expected epigenetic and gene expression signatures of loss of chromatin interactions.
• Novel insight generation: Reveals novel insights from jointly normalized datasets through distance-dependent comparative analysis.

Methodology:

multiHiCcompare uses cyclic loess regression-based joint normalization to remove biases across multiple Hi-C datasets, properly handling the Hi-C-specific decay of chromatin interaction frequencies with increasing distance between interacting regions. The general linear model framework for comparative analysis is adapted for the Hi-C-specific decay of chromatin interaction frequencies.