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arXiv · 2506.12626

Kernel Density Balancing

John Park·Ning Hao·Yue Selena Niu·Ming Hu
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Abstract

High-throughput chromatin conformation capture (Hi-C) data provide insights into the 3D structure of chromosomes, with normalization being a crucial pre-processing step. A common technique for normalization is matrix balancing, which rescales rows and columns of a Hi-C matrix to equalize their sums. Despite its popularity and convenience, matrix balancing lacks statistical justification. In this paper, we introduce a statistical model to analyze matrix balancing methods and propose a kernel-based estimator that leverages spatial structure. Under mild assumptions, we demonstrate that the kernel-based method is consistent, converges faster, and is more robust to data sparsity compared to existing approaches.

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John Park, Ning Hao, Yue Selena Niu, Ming Hu. 2025-06-14. Kernel Density Balancing. https://arxiv.org/abs/2506.12626

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