Understanding spatial organizations of chromosomes via statistical analysis of Hi-C data

doi:10.1007/s40484-013-0016-0

Quantitative Biology

2013, Vol. 1

Issue (2): 156-174 https://doi.org/10.1007/s40484-013-0016-0

本期目录

Understanding spatial organizations of chromosomes via statistical analysis of Hi-C data

Ming Hu¹, Ke Deng^1,², Zhaohui Qin³, Jun S. Liu¹(

)

¹. Department of Statistics, Harvard University, Cambridge, MA 02138, USA
². Mathematical Sciences Center, Tsinghua University, Beijing 100084, China
³. Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA

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Abstract：

Understanding how chromosomes fold provides insights into the transcription regulation, hence, the functional state of the cell. Using the next generation sequencing technology, the recently developed Hi-C approach enables a global view of spatial chromatin organization in the nucleus, which substantially expands our knowledge about genome organization and function. However, due to multiple layers of biases, noises and uncertainties buried in the protocol of Hi-C experiments, analyzing and interpreting Hi-C data poses great challenges, and requires novel statistical methods to be developed. This article provides an overview of recent Hi-C studies and their impacts on biomedical research, describes major challenges in statistical analysis of Hi-C data, and discusses some perspectives for future research.

收稿日期: 2013-03-06 出版日期: 2013-06-05

Corresponding Author(s): Jun S. Liu

引用本文:

. [J]. Quantitative Biology, 2013, 1(2): 156-174.
Ming Hu, Ke Deng, Zhaohui Qin, Jun S. Liu. Understanding spatial organizations of chromosomes via statistical analysis of Hi-C data. Quant. Biol., 2013, 1(2): 156-174.

链接本文:

https://academic.hep.com.cn/qb/CN/10.1007/s40484-013-0016-0
https://academic.hep.com.cn/qb/CN/Y2013/V1/I2/156

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Topics	Methods	Features	Pros	Cons	References
	Yaffe and Tanay	Non-parametric bias correction	Effective bias reduction	Computationally intensive, difficult interpretation	[73]
Bias reduction	HiCNorm	Parametric bias correction	Computationally efficient, easy interpretation	Rely on parametric assumption	[87]
	ICE	Normalization method	No assumption on any specific systemic biases	Limited for equal-sized genome partition	[74]
	SCN	Normalization method	Effective removal of DNA circularization bias	Limited for equal-sized genome partition	[86]
	PCA	Chromosome-wide variance decomposition	Discovery of two compartments based on spatial proximity	Low resolution (several MBs) genome partition	[34]
	Dixon et al.	Hidden Markov model	Discovery of topological domain based on local chromatin interactions	Two-step procedure of bias reduction and genome partition	[45]
Genome partition	Sexton el al.	Local distance-scaling model	Combining Yaffe and Tanay's bias correction model [73] with genome partition	Computationally intensive	[47]
	Hou et al.	Poisson mixture model	Model intra-domain and inter-domain interactions via two Poisson distributions	Two-step procedure of bias reduction and genome partition	[48]
	GeSICA	Markov clustering algorithm	Exploration of hierarchical sub-domain structures	Lack of bias reduction	[93]
	Fractal globule model	Model chromatin as a knot-free configuration	NA*	NA*	[98,99]
	Equilibrium globule model	Model chromatin as a highly knotted configuration	NA*	NA*	[100]
Polymer model	Random loop model	Looping is formed by random interaction between monomers	NA*	NA*	[39]
	Dynamic loop	Looping is formed by diffusional motion of monomers	NA*	NA*	[40]
	Strings and binds switch	Looping is affected by concentrations of binding molecules	NA*	NA*	[41]
Inferring consensus 3D chromosomal structure	Optimization-based methods	Optimize a target function to measure the fitting of a 3D model	Use biophysical constraints in 3D model reconstruction	Local modes, fail to model experimental uncertainties	[33,38,43]
	MCMC5C	Gaussian model	First statistical model for Hi-C experimental uncertainties	No bias removal, Gaussian variance estimate is ad hoc	[42]
	BACH	Poisson model	Combing bias removal with 3D model reconstruction	Lack of biophysical constraints, computational intensive	[103]
Evaluating structural variation of chromatin via statistical model	Optimization-based methods	Use multiple parallel runs to measure chromatin structural variation	Explore all possible 3D models optimizing the target function	Computationally intensive, sensitive to initialization	[33,38,43]
	Kalhor et al.	Population-based approach	Direct link Hi-C data to presence or absence of chromatin interaction	Fail to model experimental uncertainties	[37]
	BACH-MIX	Poisson mixture model	Combing bias removal with evaluating chromatin structural variation	Limited for studying local chromatin structural variation	[103]

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