Inferring the transcriptional modules using penalized matrix decomposition

Chun Hou Zheng; Lei Zhang; Vincent To Yee Ng; Chi Keung Simon Shiu; Shu Lin Wang

doi:10.1007/978-3-642-14932-0_5

Inferring the transcriptional modules using penalized matrix decomposition

Chun Hou Zheng, Lei Zhang, Vincent To Yee Ng, Chi Keung Simon Shiu, Shu Lin Wang

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

3 Citations (Scopus)

Abstract

This paper proposes to use the penalized matrix decomposition (PMD) to discover the transcriptional modules from microarray data. With the sparsity constraint on the decomposition factors, metagenes can be extracted from the gene expression data and they can well capture the intrinsic patterns of genes with the similar functions. Meanwhile, the PMD factors of each gene are good indicators of the cluster it belongs to. Compared with traditional methods, our method can cluster genes of the similar functions but without similar expression profiles. It can also assign a gene into different modules.

Original language	English
Title of host publication	Advanced Intelligent Computing Theories and Applications
Subtitle of host publication	With Aspects of Artificial Intelligence - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings
Pages	35-41
Number of pages	7
DOIs	https://doi.org/10.1007/978-3-642-14932-0_5
Publication status	Published - 6 Sep 2010
Event	6th International Conference on Intelligent Computing, ICIC 2010 - Changsha, China Duration: 18 Aug 2010 → 21 Aug 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6216 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Conference on Intelligent Computing, ICIC 2010
Country	China
City	Changsha
Period	18/08/10 → 21/08/10

Keywords

Clustering
Gene expression data
Penalized matrix decomposition
Transcriptional module

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

More information

10.1007/978-3-642-14932-0_5

Cite this

Zheng, C. H., Zhang, L., Ng, V. T. Y., Shiu, C. K. S., & Wang, S. L. (2010). Inferring the transcriptional modules using penalized matrix decomposition. In Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings (pp. 35-41). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6216 LNAI). https://doi.org/10.1007/978-3-642-14932-0_5

Zheng, Chun Hou ; Zhang, Lei ; Ng, Vincent To Yee ; Shiu, Chi Keung Simon ; Wang, Shu Lin. / Inferring the transcriptional modules using penalized matrix decomposition. Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings. 2010. pp. 35-41 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Inferring the transcriptional modules using penalized matrix decomposition",

abstract = "This paper proposes to use the penalized matrix decomposition (PMD) to discover the transcriptional modules from microarray data. With the sparsity constraint on the decomposition factors, metagenes can be extracted from the gene expression data and they can well capture the intrinsic patterns of genes with the similar functions. Meanwhile, the PMD factors of each gene are good indicators of the cluster it belongs to. Compared with traditional methods, our method can cluster genes of the similar functions but without similar expression profiles. It can also assign a gene into different modules.",

keywords = "Clustering, Gene expression data, Penalized matrix decomposition, Transcriptional module",

author = "Zheng, {Chun Hou} and Lei Zhang and Ng, {Vincent To Yee} and Shiu, {Chi Keung Simon} and Wang, {Shu Lin}",

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Zheng, CH, Zhang, L, Ng, VTY, Shiu, CKS & Wang, SL 2010, Inferring the transcriptional modules using penalized matrix decomposition. in Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6216 LNAI, pp. 35-41, 6th International Conference on Intelligent Computing, ICIC 2010, Changsha, China, 18/08/10. https://doi.org/10.1007/978-3-642-14932-0_5

Inferring the transcriptional modules using penalized matrix decomposition. / Zheng, Chun Hou; Zhang, Lei; Ng, Vincent To Yee; Shiu, Chi Keung Simon; Wang, Shu Lin.

Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings. 2010. p. 35-41 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6216 LNAI).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - This paper proposes to use the penalized matrix decomposition (PMD) to discover the transcriptional modules from microarray data. With the sparsity constraint on the decomposition factors, metagenes can be extracted from the gene expression data and they can well capture the intrinsic patterns of genes with the similar functions. Meanwhile, the PMD factors of each gene are good indicators of the cluster it belongs to. Compared with traditional methods, our method can cluster genes of the similar functions but without similar expression profiles. It can also assign a gene into different modules.

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Zheng CH, Zhang L, Ng VTY, Shiu CKS, Wang SL. Inferring the transcriptional modules using penalized matrix decomposition. In Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings. 2010. p. 35-41. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-14932-0_5