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Liu W, Yang Y, Abnousi A, Zhang Q, Kubo N, Beem JSM, Li Y, Hu M. MUNIn: A statistical framework for identifying long-range chromatin interactions from multiple samples. HGG ADVANCES 2021; 2. [PMID: 34485947 PMCID: PMC8415461 DOI: 10.1016/j.xhgg.2021.100036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Chromatin spatial organization (interactome) plays a critical role in genome function. Deep understanding of chromatin interactome can shed insights into transcriptional regulation mechanisms and human disease pathology. One essential task in the analysis of chromatin interactomic data is to identify long-range chromatin interactions. Existing approaches, such as HiCCUPS, FitHiC/FitHiC2, and FastHiC, are all designed for analyzing individual cell types or samples. None of them accounts for unbalanced sequencing depths and heterogeneity among multiple cell types or samples in a unified statistical framework. To fill in the gap, we have developed a novel statistical framework MUNIn (multiple-sample unifying long-range chromatin-interaction detector) for identifying long-range chromatin interactions from multiple samples. MUNIn adopts a hierarchical hidden Markov random field (H-HMRF) model, in which the status (peak or background) of each interacting chromatin loci pair depends not only on the status of loci pairs in its neighborhood region but also on the status of the same loci pair in other samples. To benchmark the performance of MUNIn, we performed comprehensive simulation studies and real data analysis and showed that MUNIn can achieve much lower false-positive rates for detecting sample-specific interactions (33.1%–36.2%), and much enhanced statistical power for detecting shared peaks (up to 74.3%), compared to uni-sample analysis. Our data demonstrated that MUNIn is a useful tool for the integrative analysis of interactomic data from multiple samples.
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Affiliation(s)
- Weifang Liu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.,These authors contributed equally
| | - Yuchen Yang
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.,Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.,McAllister Heart Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.,These authors contributed equally
| | - Armen Abnousi
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Qian Zhang
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Naoki Kubo
- Department of Cellular and Case Molecular Medicine, University of California San Diego School of Medicine, La Jolla, CA, USA
| | - Joshua S Martin Beem
- Duke Human Vaccine Institute, Duke University School of Medicine, Durham, NC 27710, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.,Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.,Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
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Perdikis T, Psarakis S, Castagliola P, Celano G. An EWMA-type chart based on signed ranks with exact run length properties. J STAT COMPUT SIM 2020. [DOI: 10.1080/00949655.2020.1828415] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- T. Perdikis
- Department of Statistics & Laboratory of Statistical Methodology, Athens University of Economics and Business, Athens, Greece
| | - S. Psarakis
- Department of Statistics & Laboratory of Statistical Methodology, Athens University of Economics and Business, Athens, Greece
| | | | - G. Celano
- Università di Catania, Catania, Italy
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