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Bayesian non-negative factor analysis for reconstructing transcription factor mediated regulatory networks
BACKGROUND: Transcriptional regulation by transcription factor (TF) controls the time and abundance of mRNA transcription. Due to the limitation of current proteomics technologies, large scale measurements of protein level activities of TFs is usually infeasible, making computational reconstruction...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3289087/ https://www.ncbi.nlm.nih.gov/pubmed/22166063 http://dx.doi.org/10.1186/1477-5956-9-S1-S9 |
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author | Meng, Jia Zhang, Jianqiu (Michelle) Chen, Yidong Huang, Yufei |
author_facet | Meng, Jia Zhang, Jianqiu (Michelle) Chen, Yidong Huang, Yufei |
author_sort | Meng, Jia |
collection | PubMed |
description | BACKGROUND: Transcriptional regulation by transcription factor (TF) controls the time and abundance of mRNA transcription. Due to the limitation of current proteomics technologies, large scale measurements of protein level activities of TFs is usually infeasible, making computational reconstruction of transcriptional regulatory network a difficult task. RESULTS: We proposed here a novel Bayesian non-negative factor model for TF mediated regulatory networks. Particularly, the non-negative TF activities and sample clustering effect are modeled as the factors from a Dirichlet process mixture of rectified Gaussian distributions, and the sparse regulatory coefficients are modeled as the loadings from a sparse distribution that constrains its sparsity using knowledge from database; meantime, a Gibbs sampling solution was developed to infer the underlying network structure and the unknown TF activities simultaneously. The developed approach has been applied to simulated system and breast cancer gene expression data. Result shows that, the proposed method was able to systematically uncover TF mediated transcriptional regulatory network structure, the regulatory coefficients, the TF protein level activities and the sample clustering effect. The regulation target prediction result is highly coordinated with the prior knowledge, and sample clustering result shows superior performance over previous molecular based clustering method. CONCLUSIONS: The results demonstrated the validity and effectiveness of the proposed approach in reconstructing transcriptional networks mediated by TFs through simulated systems and real data. |
format | Online Article Text |
id | pubmed-3289087 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-32890872012-02-29 Bayesian non-negative factor analysis for reconstructing transcription factor mediated regulatory networks Meng, Jia Zhang, Jianqiu (Michelle) Chen, Yidong Huang, Yufei Proteome Sci Proceedings BACKGROUND: Transcriptional regulation by transcription factor (TF) controls the time and abundance of mRNA transcription. Due to the limitation of current proteomics technologies, large scale measurements of protein level activities of TFs is usually infeasible, making computational reconstruction of transcriptional regulatory network a difficult task. RESULTS: We proposed here a novel Bayesian non-negative factor model for TF mediated regulatory networks. Particularly, the non-negative TF activities and sample clustering effect are modeled as the factors from a Dirichlet process mixture of rectified Gaussian distributions, and the sparse regulatory coefficients are modeled as the loadings from a sparse distribution that constrains its sparsity using knowledge from database; meantime, a Gibbs sampling solution was developed to infer the underlying network structure and the unknown TF activities simultaneously. The developed approach has been applied to simulated system and breast cancer gene expression data. Result shows that, the proposed method was able to systematically uncover TF mediated transcriptional regulatory network structure, the regulatory coefficients, the TF protein level activities and the sample clustering effect. The regulation target prediction result is highly coordinated with the prior knowledge, and sample clustering result shows superior performance over previous molecular based clustering method. CONCLUSIONS: The results demonstrated the validity and effectiveness of the proposed approach in reconstructing transcriptional networks mediated by TFs through simulated systems and real data. BioMed Central 2011-10-14 /pmc/articles/PMC3289087/ /pubmed/22166063 http://dx.doi.org/10.1186/1477-5956-9-S1-S9 Text en Copyright ©2011 Meng et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Proceedings Meng, Jia Zhang, Jianqiu (Michelle) Chen, Yidong Huang, Yufei Bayesian non-negative factor analysis for reconstructing transcription factor mediated regulatory networks |
title | Bayesian non-negative factor analysis for reconstructing transcription factor mediated regulatory networks |
title_full | Bayesian non-negative factor analysis for reconstructing transcription factor mediated regulatory networks |
title_fullStr | Bayesian non-negative factor analysis for reconstructing transcription factor mediated regulatory networks |
title_full_unstemmed | Bayesian non-negative factor analysis for reconstructing transcription factor mediated regulatory networks |
title_short | Bayesian non-negative factor analysis for reconstructing transcription factor mediated regulatory networks |
title_sort | bayesian non-negative factor analysis for reconstructing transcription factor mediated regulatory networks |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3289087/ https://www.ncbi.nlm.nih.gov/pubmed/22166063 http://dx.doi.org/10.1186/1477-5956-9-S1-S9 |
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