Abstract:
:Hi-C is a powerful technology for studying genome-wide chromatin interactions. However, current methods for assessing Hi-C data reproducibility can produce misleading results because they ignore spatial features in Hi-C data, such as domain structure and distance dependence. We present HiCRep, a framework for assessing the reproducibility of Hi-C data that systematically accounts for these features. In particular, we introduce a novel similarity measure, the stratum adjusted correlation coefficient (SCC), for quantifying the similarity between Hi-C interaction matrices. Not only does it provide a statistically sound and reliable evaluation of reproducibility, SCC can also be used to quantify differences between Hi-C contact matrices and to determine the optimal sequencing depth for a desired resolution. The measure consistently shows higher accuracy than existing approaches in distinguishing subtle differences in reproducibility and depicting interrelationships of cell lineages. The proposed measure is straightforward to interpret and easy to compute, making it well-suited for providing standardized, interpretable, automatable, and scalable quality control. The freely available R package HiCRep implements our approach.
journal_name
Genome Resjournal_title
Genome researchauthors
Yang T,Zhang F,Yardımcı GG,Song F,Hardison RC,Noble WS,Yue F,Li Qdoi
10.1101/gr.220640.117subject
Has Abstractpub_date
2017-11-01 00:00:00pages
1939-1949issue
11eissn
1088-9051issn
1549-5469pii
gr.220640.117journal_volume
27pub_type
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