Bayesian non-negative factor analysis for reconstructing transcriptional regulatory network

Jia Meng; Jianqiu Zhang; Yidong Chen; Yufei Huang

doi:10.1109/SSP.2011.5967704

Bayesian non-negative factor analysis for reconstructing transcriptional regulatory network

Jia Meng, Jianqiu Zhang, Yidong Chen, Yufei Huang^*

^*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

Transcriptional regulation by transcription factors (TFs) controls when and how much RNA is created. Due to technical limitations, the protein level expressions of TFs are usually unknown, making computational reconstruction of transcriptional network a difficult task. We proposed here a novel Bayesian non-negative factor approach, which is capable to estimate both the non-negative abundances of the transcription factors, their regulatory effects, and sample clustering information by integrating microarray data and existing knowledge regarding TFs regulated target genes. The results demonstrated its validity and effectiveness to reconstructing transcriptional networks by transcription factors through simulated systems and real data.

Original language	English
Title of host publication	2011 IEEE Statistical Signal Processing Workshop, SSP 2011
Pages	361-364
Number of pages	4
DOIs	https://doi.org/10.1109/SSP.2011.5967704
Publication status	Published - 2011
Externally published	Yes
Event	2011 IEEE Statistical Signal Processing Workshop, SSP 2011 - Nice, France Duration: 28 Jun 2011 → 30 Jun 2011

Publication series

Name	IEEE Workshop on Statistical Signal Processing Proceedings

Conference

Conference	2011 IEEE Statistical Signal Processing Workshop, SSP 2011
Country/Territory	France
City	Nice
Period	28/06/11 → 30/06/11

Keywords

Bayesian factor analysis
PCA
non-negative factor analysis
principle component analysis

Access to Document

10.1109/SSP.2011.5967704

Cite this

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title = "Bayesian non-negative factor analysis for reconstructing transcriptional regulatory network",

abstract = "Transcriptional regulation by transcription factors (TFs) controls when and how much RNA is created. Due to technical limitations, the protein level expressions of TFs are usually unknown, making computational reconstruction of transcriptional network a difficult task. We proposed here a novel Bayesian non-negative factor approach, which is capable to estimate both the non-negative abundances of the transcription factors, their regulatory effects, and sample clustering information by integrating microarray data and existing knowledge regarding TFs regulated target genes. The results demonstrated its validity and effectiveness to reconstructing transcriptional networks by transcription factors through simulated systems and real data.",

keywords = "Bayesian factor analysis, PCA, non-negative factor analysis, principle component analysis",

author = "Jia Meng and Jianqiu Zhang and Yidong Chen and Yufei Huang",

year = "2011",

doi = "10.1109/SSP.2011.5967704",

language = "English",

isbn = "9781457705700",

series = "IEEE Workshop on Statistical Signal Processing Proceedings",

pages = "361--364",

booktitle = "2011 IEEE Statistical Signal Processing Workshop, SSP 2011",

note = "2011 IEEE Statistical Signal Processing Workshop, SSP 2011 ; Conference date: 28-06-2011 Through 30-06-2011",

}

Meng, J, Zhang, J, Chen, Y & Huang, Y 2011, Bayesian non-negative factor analysis for reconstructing transcriptional regulatory network. in 2011 IEEE Statistical Signal Processing Workshop, SSP 2011., 5967704, IEEE Workshop on Statistical Signal Processing Proceedings, pp. 361-364, 2011 IEEE Statistical Signal Processing Workshop, SSP 2011, Nice, France, 28/06/11. https://doi.org/10.1109/SSP.2011.5967704

Bayesian non-negative factor analysis for reconstructing transcriptional regulatory network. / Meng, Jia; Zhang, Jianqiu; Chen, Yidong et al.
2011 IEEE Statistical Signal Processing Workshop, SSP 2011. 2011. p. 361-364 5967704 (IEEE Workshop on Statistical Signal Processing Proceedings).

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

TY - GEN

T1 - Bayesian non-negative factor analysis for reconstructing transcriptional regulatory network

AU - Meng, Jia

AU - Zhang, Jianqiu

AU - Chen, Yidong

AU - Huang, Yufei

PY - 2011

Y1 - 2011

N2 - Transcriptional regulation by transcription factors (TFs) controls when and how much RNA is created. Due to technical limitations, the protein level expressions of TFs are usually unknown, making computational reconstruction of transcriptional network a difficult task. We proposed here a novel Bayesian non-negative factor approach, which is capable to estimate both the non-negative abundances of the transcription factors, their regulatory effects, and sample clustering information by integrating microarray data and existing knowledge regarding TFs regulated target genes. The results demonstrated its validity and effectiveness to reconstructing transcriptional networks by transcription factors through simulated systems and real data.

AB - Transcriptional regulation by transcription factors (TFs) controls when and how much RNA is created. Due to technical limitations, the protein level expressions of TFs are usually unknown, making computational reconstruction of transcriptional network a difficult task. We proposed here a novel Bayesian non-negative factor approach, which is capable to estimate both the non-negative abundances of the transcription factors, their regulatory effects, and sample clustering information by integrating microarray data and existing knowledge regarding TFs regulated target genes. The results demonstrated its validity and effectiveness to reconstructing transcriptional networks by transcription factors through simulated systems and real data.

KW - Bayesian factor analysis

KW - PCA

KW - non-negative factor analysis

KW - principle component analysis

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U2 - 10.1109/SSP.2011.5967704

DO - 10.1109/SSP.2011.5967704

M3 - Conference Proceeding

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SN - 9781457705700

T3 - IEEE Workshop on Statistical Signal Processing Proceedings

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EP - 364

BT - 2011 IEEE Statistical Signal Processing Workshop, SSP 2011

T2 - 2011 IEEE Statistical Signal Processing Workshop, SSP 2011

Y2 - 28 June 2011 through 30 June 2011

ER -

Bayesian non-negative factor analysis for reconstructing transcriptional regulatory network

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Keywords

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