Binary Segmentation Methods for Identifying Boundaries of Spatial Domains

Raveendran, Nishanthi; Sofronov, Georgy

doi:10.15439/2017f206

Cited by 6 publications

(5 citation statements)

References 33 publications

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“…In our problem, the intensity of interaction along the row and column of the interaction matrix changes abruptly on domain boundaries, where the change point locations are the indices that are used to make horizontal and vertical boundaries of such domains. To solve this two-dimensional segmentation problem, we use a modified version of the binary segmentation algorithm proposed by Raveendran & Sofronov (2017), where the algorithm is used to detect homogeneous domains in two-dimensional lattice data. We use this algorithm to detect homogeneous domains along the diagonal of matrix X with the model defined in Section 2.…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we approach this problem as a two-dimensional (2D) spatial segmentation problem where detecting domains of diagonal blocks in a symmetric matrix can be seen as a particular 2D segmentation task. Spatial segmentation algorithms are widely used in many areas, including spatial epidemiology (Gangnon & Clayton (2000)), ecology (see, for example, Beckage et al (2007), López et al (2010), Raveendran & Sofronov (2017)), climatology (Tripathi & Govindaraju (2009)) and economic applications (Arbia et al (2008), Cai et al (2016)). For example, similar spatial segmentation problems were recently studied by Raveendran & Sofronov (2019, 2021 to identify homogeneous spatial domains in lattice data.…”

Section: Introductionmentioning

confidence: 99%

“…This method originates from the work of Scott & Knott (1974) and Sen & Srivastava (1975) and it was further studied and proposed as circular binary segmentation for analysing the DNA copy number data by Olshen et al (2004) and as a wild binary segmentation approach by Fryzlewicz (2014). This method is also used in two-dimensional segmentation problems, for example, see Raveendran & Sofronov (2017) in which the binary segmentation method was used to segment spatial binary data observed over a two-dimensional lattice. This method offers several benefits, including simplicity and ease of implementation, while also resulting in significant computational cost savings.…”

Section: Introductionmentioning

confidence: 99%

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A binary segmentation method for detecting topological domains in Hi-C data

2023

MODSIM2023, 25th International Congress on Modelling and Simulation.

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The three-dimensional (3D) architecture of chromosomes in nuclear space plays an important role in studying gene expression and regulation in cell biology. In particular, chromosome conformation capture (3C) techniques are used to study the spatial structure of chromosomes. Many such methods have been developed in the last two decades. Among them, Hi-C is a technology that uses a deep sequencing approach to detect the 3D spatial organization of a genome. That is, Hi-C allows us to evaluate spatial proximity between any pair of loci along the genome. This results in an interaction matrix with the frequency of interactions between genomic loci that physically interact in the nucleus. Highly self-interacting regions appear in the contact map of such interaction matrices. These regions are called topological domains and they play an important role in regulating gene expression and other genomic functions. Thus detecting such topological domains will provide new insights on chromosomal conformation in better understanding of cell functioning and various diseases.The topological domains centered along a diagonal region in contact maps are more likely to exist and prominent in data. In this study, we focus on detecting such domains, and we approach this problem as a twodimensional segmentation problem. To solve this segmentation problem, we propose an algorithm based on the binary segmentation method, a well-known recursive partitioning technique used in change point detection problems. Our numerical experiments illustrate the usefulness of this approach. We obtain estimates for the number of diagonal blocks and their boundaries in an artificially generated data matrix and compare the results of these estimates to those obtained with the HiCSeg R package. We conclude that binary segmentation method works well in identifying such domains with easy implementation and a low computational cost.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A binary segmentation method for detecting topological domains in Hi-C data

2023

MODSIM2023, 25th International Congress on Modelling and Simulation.

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“…In [6], the authors apply a change-point detection methodology to detect plant patches. An algorithm based on a binary segmentation method within the changepoint detection framework in order to identify homogeneous domains has recently been developed in [7]. Climate change studies is another area that commonly uses spatial segmentation methods.…”

Section: Introductionmentioning

confidence: 99%

A Markov Chain Monte Carlo Algorithm for Spatial Segmentation

Raveendran

Sofronov

2021

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Spatial data are very often heterogeneous, which indicates that there may not be a unique simple statistical model describing the data. To overcome this issue, the data can be segmented into a number of homogeneous regions (or domains). Identifying these domains is one of the important problems in spatial data analysis. Spatial segmentation is used in many different fields including epidemiology, criminology, ecology, and economics. To solve this clustering problem, we propose to use the change-point methodology. In this paper, we develop a new spatial segmentation algorithm within the framework of the generalized Gibbs sampler. We estimate the average surface profile of binary spatial data observed over a two-dimensional regular lattice. We illustrate the performance of the proposed algorithm with examples using artificially generated and real data sets.

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“…A change-point happens when the statistical properties, for example, mean or variance, of observations suddenly change, which may be due to an external stimulus or an inherent mechanism. Change-point detection plays an important role in modelling time series in wide range of scientific endeavours, such as financial time series analysis, econometrics (see Priyadarshana and Sofronov (2012)), signal processing (see Sofronov et al (2012)), genomics (see Sofronov et al (2009)), geology data analysis (see Furlan (2010)) and environmental applications (see, for example, Raveendran and Sofronov (2017)). In general, there are two classes of change-point models: retrospective and sequential (or prospective).…”

Section: Introductionmentioning

confidence: 99%

Change-point detection in time series data via the Cross-Entropy method

Sofronov

2017

Syme, G., Hatton MacDonald, D., Fulton, B. And Piantadosi, J. (Eds) MODSIM2017, 22nd International Congress on Modelling and Si

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In many applications data are collected over time and very often the statistical properties, such as mean or variance, of the data will change along data. In recent years, there has been an increasing interest to the problem, which is known as a change-point problem, where it is necessary to detect the number and locations of change-points of time series processes. The change-point detection problem, which may also be called a segmentation or break-point problem, can be found in a wide range of applications, such as financial time series analysis (e.g. changing volatility), signal processing (e.g. structural analysis of EEG signals), geology data analysis (e.g. analysis of volcanic eruption series) and environmental applications (e.g. detecting changes in ecological systems due to climatic conditions crossing some critical thresholds). For example, many macroeconomic variables such as inflation may be subject to changes in government policy that may cause structural breaks in the data and make highly persistent impacts. An awareness of those changes can assist people to eliminate and manage unnecessary risk and further improve the decision-making. The main concern of change-point problem is the complexity to build a flexible and effective model to estimate the unknown number and locations of break points in time series. In this paper, we develop an innovative methodology to tackle this problem. We compare the statistical performance of a number of computational methods for estimating unknown parameters of autoregressive data with structural breaks. Specifically, we consider the Cross Entropy method for modelling break points using minimum description length (MDL) information criterion to estimate change-points as well as parameters of the process on each segment. Numerical experiments illustrate the robustness of this approach. We obtain estimates for the locations of change-points in artificially generated sequences and compare the accuracy of these estimates to those obtained with other methods. Finally, we use the proposed method to detect the potential location of change-points in the Australian annual inflation rate data from 1960 to 2016.

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Binary Segmentation Methods for Identifying Boundaries of Spatial Domains

Cited by 6 publications

References 33 publications

A binary segmentation method for detecting topological domains in Hi-C data

A binary segmentation method for detecting topological domains in Hi-C data

A Markov Chain Monte Carlo Algorithm for Spatial Segmentation

Change-point detection in time series data via the Cross-Entropy method

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