SpaCEM3: a software for biological module detection when data is incomplete, high dimensional and dependent

Vignes, Matthieu; Blanchet, Juliette; Leroux, Damien; Forbes, Florence

doi:10.1093/bioinformatics/btr034

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…Solutions exist in such cases. Some procedures using the EM approach are implemented in the SpacEM 3 software (Vignes et al, 2011) available at http://spacem3.gforge.inria.fr. Finally, since image analysis is a vast domain in terms of both methodology and applications, many important contributions are not cited or mentioned in this chapter.…”

Section: Discussionmentioning

confidence: 99%

Mixture Models for Image Analysis

Forbes¹

2019

Handbook of Mixture Analysis

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Section: Discussionmentioning

confidence: 99%

Mixture Models for Image Analysis

Forbes¹

2019

Handbook of Mixture Analysis

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“…The third category is based on statistical models 36 . Ambroise et al 36 proposed a spatial clustering method based on Markov random field, which was first used for image segmentation 43 proposed a software SpaCEM3 in which spatial clustering using Markov models is implemented. This software applied the model for gene clustering based on the Markov models proposed by 44 .…”

Section: Related Workmentioning

confidence: 99%

A raster-based spatial clustering method with robustness to spatial outliers

Wang,

Song,

Wang

et al. 2024

Sci Rep

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Spatial clustering is an essential method for the comprehensive understanding of a region. Spatial clustering divides all spatial units into different clusters. The attributes of each cluster of the spatial units are similar, and simultaneously, they are as continuous as spatially possible. In spatial clustering, the handling of spatial outliers is important. It is necessary to improve spatial integration so that each cluster is connected as much as possible, while protecting spatial outliers can help avoid the excessive masking of attribute differences This paper proposes a new spatial clustering method for raster data robust to spatial outliers. The method employs a sliding window to scan the entire region to determine spatial outliers. Additionally, a mechanism based on the range and standard deviation of the spatial units in each window is designed to judge whether the spatial integration should be further improved or the spatial outliers should be protected. To demonstrate the usefulness of the proposed method, we applied it in two case study areas, namely, Changping District and Pinggu District in Beijing. The results show that the proposed method can retain the spatial outliers while ensuring that the clusters are roughly contiguous. This method can be used as a simple but powerful and easy-to-interpret alternative to existing geographical spatial clustering methods.

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“…The Markov random field was simulated using the SpaceEM 3 software (Vignes et al . 2011). Simulation relies on 100 Gibbs sampling iterations.…”

Section: Assessment Of β Estimates Yielded By Vbemmentioning

confidence: 99%

“…For each set of parameters, 50 simulated data sets are generated, yielding an estimate ˆ for each of them. The Markov random field was simulated using the SpaceEM 3 software (Vignes et al 2011). Simulation relies on 100 Gibbs sampling iterations.…”

Section: Assessment Of Estimates Yielded By Vbemmentioning

confidence: 99%

Bayesian non‐parametric spatial prior for traffic crash risk mapping: A case study of Victoria, Australia

Durand

Forbes

Phan

et al. 2022

Aus NZ J of Statistics

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Summary We develop a Bayesian non‐parametric (BNP) model coupled with Markov random fields (MRFs) for risk mapping, to infer homogeneous spatial regions in terms of risks. In contrast to most existing methods, the proposed approach does not require an arbitrary commitment to a specified number of risk classes and determines their risk levels automatically. We consider settings in which the relevant information are counts and propose a so‐called BNP hidden MRF (BNP‐HMRF) model that is able to handle such data. The model inference is carried out using a variational Bayes expectation–maximisation algorithm and the approach is illustrated on traffic crash data in the state of Victoria, Australia. The obtained results corroborate well with the traffic safety literature. More generally, the model presented here for risk mapping offers an effective, convenient and fast way to conduct partition of spatially localised count data.

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SpaCEM3: a software for biological module detection when data is incomplete, high dimensional and dependent

Cited by 3 publications

References 9 publications

Mixture Models for Image Analysis

Mixture Models for Image Analysis

A raster-based spatial clustering method with robustness to spatial outliers

Bayesian non‐parametric spatial prior for traffic crash risk mapping: A case study of Victoria, Australia

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