Ramachandran Parameswaran, Sánchez-Taltavull Daniel, Perkins Theodore J
Regenerative Medicine Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada K1H8L6.
Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, Ontario, Canada K1H8M5.
PLoS One. 2017 Aug 17;12(8):e0183103. doi: 10.1371/journal.pone.0183103. eCollection 2017.
Co-expression networks have long been used as a tool for investigating the molecular circuitry governing biological systems. However, most algorithms for constructing co-expression networks were developed in the microarray era, before high-throughput sequencing-with its unique statistical properties-became the norm for expression measurement. Here we develop Bayesian Relevance Networks, an algorithm that uses Bayesian reasoning about expression levels to account for the differing levels of uncertainty in expression measurements between highly- and lowly-expressed entities, and between samples with different sequencing depths. It combines data from groups of samples (e.g., replicates) to estimate group expression levels and confidence ranges. It then computes uncertainty-moderated estimates of cross-group correlations between entities, and uses permutation testing to assess their statistical significance. Using large scale miRNA data from The Cancer Genome Atlas, we show that our Bayesian update of the classical Relevance Networks algorithm provides improved reproducibility in co-expression estimates and lower false discovery rates in the resulting co-expression networks. Software is available at www.perkinslab.ca.
共表达网络长期以来一直被用作研究生物系统分子调控机制的工具。然而，大多数构建共表达网络的算法是在微阵列时代开发的，在高通量测序（具有独特的统计特性）成为表达量测量的标准方法之前。在这里，我们开发了贝叶斯相关网络，这是一种算法，它利用关于表达水平的贝叶斯推理来解释高表达和低表达实体之间以及具有不同测序深度的样本之间表达测量中不同程度的不确定性。它结合来自样本组（例如重复样本）的数据来估计组表达水平和置信范围。然后，它计算实体之间跨组相关性的不确定性调整估计，并使用置换检验来评估其统计显著性。使用来自癌症基因组图谱的大规模miRNA数据，我们表明我们对经典相关网络算法的贝叶斯更新在共表达估计中提供了更高的可重复性，并在所得的共表达网络中降低了错误发现率。软件可在www.perkinslab.ca获取。
