Hi-C has been predominately used to study the genome-wide interactions of genomes. In Hi-C experiments, it is believed that biases originating from different systematic deviations lead to extraneous variability among raw samples, and affect the reliability of downstream interpretations. As an important pipeline in Hi-C analysis, normalization seeks to remove the unwanted systematic biases; thus, a comparison between Hi-C normalization methods benefits their choice and the downstream analysis. In this article, a comprehensive comparison is proposed to investigate six Hi-C normalization methods in terms of multiple considerations. In light of comparison results, it has been shown that a cross-sample approach significantly outperforms individual sample methods in most considerations. The differences between these methods are analyzed, some practical recommendations are given, and the results are summarized in a table to facilitate the choice of the six normalization methods. The source code for the implementation of these methods is available at https://github.com/lhqxinghun/bioinformatics/tree/master/Hi-C/NormCompare.

中文翻译：

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Hi-C数据标准化方法的比较。

Hi-C主要用于研究基因组范围内的全基因组相互作用。在Hi-C实验中，据信源自不同系统偏差的偏差会导致原始样品之间的外部变化，并影响下游解释的可靠性。作为Hi-C分析的重要管道，归一化旨在消除不必要的系统偏差。因此，Hi-C归一化方法之间的比较有利于它们的选择和下游分析。本文中，出于多种考虑，提出了一种全面的比较方法来研究六种Hi-C标准化方法。根据比较结果，已经表明，在大多数考虑下，交叉采样方法明显优于单个采样方法。分析了这两种方法之间的差异，给出了一些实用的建议，并将结果汇​​总在表格中，以方便选择六种标准化方法。可在https://github.com/lhqxinghun/bioinformatics/tree/master/Hi-C/NormCompare中找到实现这些方法的源代码。