title: Transcription network analysis by a sparse binary factor analysis algorithm.
abstract: 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&rsquo;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.
authors: Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.
authors: Shikui
authors: S
authors: Tu
authors: Runsheng
authors: R
authors: Chen
authors: Lei
authors: L
authors: Xu
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002244:Carbon / Q000378:metabolism; D003198:Computer Simulation; D004926:Escherichia coli / Q000378:metabolism; D005163:Factor Analysis, Statistical; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D012441:Saccharomyces cerevisiae / Q000502:physiology; D014157:Transcription Factors / Q000235:genetics* / Q000378:metabolism; D014158:Transcription, Genetic
chemical_list: D014157:Transcription Factors; D002244:Carbon