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Transcription Network Analysis by A Sparse Binary Factor Analysis Algorithm

Tu, Shikui ; Chen, Runsheng ; Xu, Lei

Journal of Integrative Bioinformatics - JIB (ISSN 1613-4516)

Transcription factor activities (TFAs), rather than expression levels, control gene expression and provide valuable information for investigating TF-gene regulations. The underlying bimodal or switch-like patterns of TFAs may play important roles in gene regulation. Network Component Analysis (NCA) is a popular method to deduce TFAs and TF-gene control strengths from microarray data. However, it does not directly examine the bimodality of TFAs and it needs TF-gene connection topology a priori known. In this paper, we modify NCA to model gene expression regulation by Binary Factor Analysis (BFA), which directly captures switch-like patterns of TFAs. Moreover, sparse technique is employed on the mixing matrix of BFA, and thus the proposed sparse BYY-BFA algorithm, developed under Bayesian Ying-Yang (BYY) learning framework, can not only uncover the latent TFA profile’s switch-like patterns, but also be capable of automatically shutting off the unnecessary connections. Simulation study demonstrates the effectiveness of BYY-BFA, and a preliminary application to Saccharomyces cerevisiae cell cycle data and Escherichia coli carbon source transition data shows that the reconstructed binary patterns of TFAs by BYY-BFA are consistent with the ups and downs of TFAs by NCA, and that BYY-BFA also works well when the network topology is unknown.

Beteiligte Einrichtung: Technische Fakultät, Arbeitsgruppen der Informatik
DDC-Sachgruppe: Datenverarbeitung, Informatik

Transcription Network Analysis by A Sparse Binary Factor Analysis Algorithm. Journal of Integrative Bioinformatics - JIB (ISSN 1613-4516), 9(2): Special Issue: 7th International Symposium on Integrative Bioinformatics, Hangzhou, China, 2012

Online-Journal: http://journal.imbio.de/article.php?aid=198
URL: http://biecoll.ub.uni-bielefeld.de/volltexte/2012/5225

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 Letzte Änderung: 15.2.2011
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