Transcription network analysis by a sparse binary factor analysis algorithm.
Publication in refereed journal

香港中文大學研究人員

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摘要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.
著者Tu S., Chen R., Xu L.
期刊名稱Journal of Integrative Bioinformatics
出版年份2012
月份10
日期25
卷號9
期次2
出版社Informationsmanagement in der Biotechnologie e.V. (IMBio e.V.)
出版地Germany
頁次198
國際標準期刊號1613-4516
語言英式英語

上次更新時間 2020-14-10 於 02:06