Comparison of normalization methods for Hi-C data
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 import...
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Taylor & Francis Group
2020-02-01
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| Series: | BioTechniques |
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| Online Access: | https://www.future-science.com/doi/10.2144/btn-2019-0105 |
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| author | Hongqiang Lyu Erhu Liu Zhifang Wu |
| author_facet | Hongqiang Lyu Erhu Liu Zhifang Wu |
| author_sort | Hongqiang Lyu |
| collection | DOAJ |
| description | 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 |
| format | Article |
| id | doaj-art-2e3cbc65ac07428ebaa6849c1735f53b |
| institution | OA Journals |
| issn | 0736-6205 1940-9818 |
| language | English |
| publishDate | 2020-02-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | BioTechniques |
| spelling | doaj-art-2e3cbc65ac07428ebaa6849c1735f53b2025-08-20T02:25:51ZengTaylor & Francis GroupBioTechniques0736-62051940-98182020-02-01682566410.2144/btn-2019-0105Comparison of normalization methods for Hi-C dataHongqiang Lyu0Erhu Liu1Zhifang Wu21School of Electronic & Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China1School of Electronic & Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China1School of Electronic & Information Engineering, Xi'an Jiaotong University, Xi'an 710049, ChinaHi-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/NormComparehttps://www.future-science.com/doi/10.2144/btn-2019-0105comprehensive comparisonHi-C datanormalization methods |
| spellingShingle | Hongqiang Lyu Erhu Liu Zhifang Wu Comparison of normalization methods for Hi-C data BioTechniques comprehensive comparison Hi-C data normalization methods |
| title | Comparison of normalization methods for Hi-C data |
| title_full | Comparison of normalization methods for Hi-C data |
| title_fullStr | Comparison of normalization methods for Hi-C data |
| title_full_unstemmed | Comparison of normalization methods for Hi-C data |
| title_short | Comparison of normalization methods for Hi-C data |
| title_sort | comparison of normalization methods for hi c data |
| topic | comprehensive comparison Hi-C data normalization methods |
| url | https://www.future-science.com/doi/10.2144/btn-2019-0105 |
| work_keys_str_mv | AT hongqianglyu comparisonofnormalizationmethodsforhicdata AT erhuliu comparisonofnormalizationmethodsforhicdata AT zhifangwu comparisonofnormalizationmethodsforhicdata |