HiCcompare: an R-package for joint normalization and comparison of HI-C datasets

作者：Stansfield John C; Cresswell Kellen G; Vladimirov Vladimir I; Dozmorov Mikhail G^*

来源：BMC Bioinformatics, 2018, 19(1): 279.

DOI：10.1186/s12859-018-2288-x

• Hi-C
• Chromosome conformation capture
• Normalization
• Comparison
• Differential analysis
• HiCcompare

Background: Changes in spatial chromatin interactions are now emerging as a unifying mechanism orchestrating the regulation of gene expression. Hi-C sequencing technology allows insight into chromatin interactions on a genome-wide scale. However, Hi-C data contains many DNA sequence-and technology-driven biases. These biases prevent effective comparison of chromatin interactions aimed at identifying genomic regions differentially interacting between, e.g., disease-normal states or different cell types. Several methods have been developed for normalizing individual Hi-C datasets. However, they fail to account for biases between two or more Hi-C datasets, hindering comparative analysis of chromatin interactions.
Results: We developed a simple and effective method, HiCcompare, for the joint normalization and differential analysis of multiple Hi-C datasets. The method introduces a distance-centric analysis and visualization of the differences between two Hi-C datasets on a single plot that allows for a data-driven normalization of biases using locally weighted linear regression (loess). HiCcompare outperforms methods for normalizing individual Hi-C datasets and methods for differential analysis (diffHiC, FIND) in detecting a priori known chromatin interaction differences while preserving the detection of genomic structures, such as A/B compartments.
Conclusions: HiCcompare is able to remove between-dataset bias present in Hi-C matrices. It also provides a userfriendly tool to allow the scientific community to perform direct comparisons between the growing number of preprocessed Hi-C datasets available at online repositories. HiCcompare is freely available as a Bioconductor R package https://bioconductor.org/packages/HiCcompare/.

• 出版日期2018-7-31