Clustering DNA methylation expressions using nonparametric beta mixture model

Lin Zhang; Jia Meng; Hui Liu; Yufei Huang

doi:10.1109/gensips.2011.6169472

Clustering DNA methylation expressions using nonparametric beta mixture model

Lin Zhang
, Jia Meng
, Hui Liu
, Yufei Huang^*

^*Corresponding author for this work

China University of Mining and Technology
University of Texas at San Antonio
University of Texas Health Science Center at San Antonio

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

3 Citations (Scopus)

Abstract

The problem of defining the clustering structure in DNA methylation expressions is considered. A Dirichlet process beta mixture model (DPBMM) is proposed that models the DNA methylation data array. The model allows automatic learning of the cluster structure parameters such as the cluster mixing proportion, the models of each cluster, and especially the number of clusters. To enable the learning, we proposed a Gibbs sampling algorithm for computing the posterior distributions, hence the estimates of the parameters. We investigate the performance of the proposed clustering algorithm via simulation.

Original language	English
Title of host publication	Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11
Publisher	IEEE Computer Society
Pages	170-173
Number of pages	4
ISBN (Print)	9781467304900
DOIs	https://doi.org/10.1109/gensips.2011.6169472
Publication status	Published - 2011
Externally published	Yes
Event	2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11 - San Antonio, TX, United States Duration: 4 Dec 2011 → 6 Dec 2011

Publication series

Name	Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics
ISSN (Print)	2150-3001
ISSN (Electronic)	2150-301X

Conference

Conference	2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11
Country/Territory	United States
City	San Antonio, TX
Period	4/12/11 → 6/12/11

Keywords

Beta mixture model
DNA methylation microarray
Dirichlet process mixture (DPM)
Gibbs sampling

Access to Document

10.1109/gensips.2011.6169472

Cite this

Zhang, L., Meng, J., Liu, H., & Huang, Y. (2011). Clustering DNA methylation expressions using nonparametric beta mixture model. In Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11 (pp. 170-173). Article 6169472 (Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics). IEEE Computer Society. https://doi.org/10.1109/gensips.2011.6169472

@inproceedings{ecf223291bb2403ca8959a6d92d3fdb2,

title = "Clustering DNA methylation expressions using nonparametric beta mixture model",

abstract = "The problem of defining the clustering structure in DNA methylation expressions is considered. A Dirichlet process beta mixture model (DPBMM) is proposed that models the DNA methylation data array. The model allows automatic learning of the cluster structure parameters such as the cluster mixing proportion, the models of each cluster, and especially the number of clusters. To enable the learning, we proposed a Gibbs sampling algorithm for computing the posterior distributions, hence the estimates of the parameters. We investigate the performance of the proposed clustering algorithm via simulation.",

keywords = "Beta mixture model, DNA methylation microarray, Dirichlet process mixture (DPM), Gibbs sampling",

author = "Lin Zhang and Jia Meng and Hui Liu and Yufei Huang",

year = "2011",

doi = "10.1109/gensips.2011.6169472",

language = "English",

isbn = "9781467304900",

series = "Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics",

publisher = "IEEE Computer Society",

pages = "170--173",

booktitle = "Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11",

note = "2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11 ; Conference date: 04-12-2011 Through 06-12-2011",

}

Zhang, L, Meng, J, Liu, H & Huang, Y 2011, Clustering DNA methylation expressions using nonparametric beta mixture model. in Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11., 6169472, Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics, IEEE Computer Society, pp. 170-173, 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11, San Antonio, TX, United States, 4/12/11. https://doi.org/10.1109/gensips.2011.6169472

Clustering DNA methylation expressions using nonparametric beta mixture model. / Zhang, Lin; Meng, Jia; Liu, Hui et al.
Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11. IEEE Computer Society, 2011. p. 170-173 6169472 (Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Clustering DNA methylation expressions using nonparametric beta mixture model

AU - Zhang, Lin

AU - Meng, Jia

AU - Liu, Hui

AU - Huang, Yufei

PY - 2011

Y1 - 2011

N2 - The problem of defining the clustering structure in DNA methylation expressions is considered. A Dirichlet process beta mixture model (DPBMM) is proposed that models the DNA methylation data array. The model allows automatic learning of the cluster structure parameters such as the cluster mixing proportion, the models of each cluster, and especially the number of clusters. To enable the learning, we proposed a Gibbs sampling algorithm for computing the posterior distributions, hence the estimates of the parameters. We investigate the performance of the proposed clustering algorithm via simulation.

AB - The problem of defining the clustering structure in DNA methylation expressions is considered. A Dirichlet process beta mixture model (DPBMM) is proposed that models the DNA methylation data array. The model allows automatic learning of the cluster structure parameters such as the cluster mixing proportion, the models of each cluster, and especially the number of clusters. To enable the learning, we proposed a Gibbs sampling algorithm for computing the posterior distributions, hence the estimates of the parameters. We investigate the performance of the proposed clustering algorithm via simulation.

KW - Beta mixture model

KW - DNA methylation microarray

KW - Dirichlet process mixture (DPM)

KW - Gibbs sampling

UR - https://www.scopus.com/pages/publications/84863681549

U2 - 10.1109/gensips.2011.6169472

DO - 10.1109/gensips.2011.6169472

M3 - Conference Proceeding

AN - SCOPUS:84863681549

SN - 9781467304900

T3 - Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics

SP - 170

EP - 173

BT - Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11

PB - IEEE Computer Society

T2 - 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11

Y2 - 4 December 2011 through 6 December 2011

ER -

Zhang L, Meng J, Liu H, Huang Y. Clustering DNA methylation expressions using nonparametric beta mixture model. In Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11. IEEE Computer Society. 2011. p. 170-173. 6169472. (Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics). doi: 10.1109/gensips.2011.6169472

Clustering DNA methylation expressions using nonparametric beta mixture model

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this