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首页> 外文会议>International symposium on integrative bioinformatics, 8th annual meeting >Transcription Network Analysis by a Sparse Binary Factor Analysis Algorithm
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Transcription Network Analysis by a Sparse Binary Factor Analysis Algorithm

机译：稀疏二元分析算法的转录网络分析

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摘要

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 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.
机译：转录因子活性（TFA）而非表达水平可控制基因表达，并为研究TFgene调控提供有价值的信息。 TFA的潜在双峰或开关样模式可能在基因调控中发挥重要作用。网络成分分析（NCA）是一种从微阵列数据中推断TFA和TF基因控制强度的流行方法。但是，它不直接检查TFA的双峰性，并且需要先验已知的TF基因连接拓扑。在本文中，我们通过二元因子分析（BFA）修改了NCA，以模拟基因表达调控，该因子直接捕获TFA的开关样模式。此外，在BFA的混合矩阵上采用了稀疏技术，因此，在贝叶斯盈阳（BYY）学习框架下开发的稀疏BYY-BFA算法不仅可以发现Latet-t TEA配置文件的开关状模式，而且还能够自动关闭不必要的连接。仿真研究证明了BYY-BFA的有效性，并将其初步应用于啤酒酵母细胞周期数据和大肠杆菌碳源迁移数据表明BYY-BFA重构的TFA的二元模式与NCA的TFA的起伏一致，并且当网络拓扑未知时，BYY-BFA也可以很好地工作。

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  • 会议地点 Hangzhou(CN)
  • 作者

    Shikui Tu; Runsheng Chen; Lei Xu;

  • 作者单位

    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, Chinese Academy of Sciences, Beijing 100101, China;

    Department of Computer Science and Engineering, The Chinese University of Hong Kong,Hong Kong, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物信息论;
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