Transcription Network Analysis by a Sparse Binary Factor Analysis Algorithm
Transcription factor activities (TFAs), rather than expression levels, control gene expres-sion and provide valuable information for investigating TFgene regulations. The underly-ing bimodal or switchlike 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 bimodal-ity 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 laten-t TEA profiles 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.
Shikui Tu Runsheng Chen Lei Xu
Department of Computer Science and Engineering, The Chinese University of Hong Kong,Hong Kong, China Bioinformatics Laboratory and National Laboratory of Biomacromolecules, Institute of Biophysics, Chi
国际会议
杭州
英文
17-26
2012-04-02（万方平台首次上网日期，不代表论文的发表时间）