Co-expression networks are essential tools to infer biological associations between gene products and predict gene annotation. Global networks can be analyzed at the transcriptome-wide scale or after querying them with a set of guide genes to capture the transcriptional landscape of a given pathway in a process named Pathway Level Coexpression (PLC). A critical step in network construction remains the definition of gene co-expression. In the present work, we compared how Pearson Correlation Coefficient (PCC), Spearman Correlation Coefficient (SCC), their respective ranked values (Highest Reciprocal Rank (HRR)), Mutual Information (MI) and Partial Correlations (PC) performed on global networks and PLCs. This evaluation was conducted on the model plant Arabidopsis thaliana using microarray and differently pre-processed RNA-seq datasets. We particularly evaluated how dataset × distance measurement combinations performed in 5 PLCs corresponding to 4 well described plant metabolic pathways (phenylpropanoid, carbohydrate, fatty acid and terpene metabolisms) and the cytokinin signaling pathway. Our present work highlights how PCC ranked with HRR is better suited for global network construction and PLC with microarray and RNA-seq data than other distance methods, especially to cluster genes in partitions similar to biological subpathways.

中文翻译：

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对全基因组相关性测量进行排名可以改善基于微阵列和RNA-seq的全局和靶向共表达网络。

共表达网络是推断基因产物之间的生物学联系并预测基因注释的必不可少的工具。可以在整个转录组范围内分析全局网络，也可以在用一组指导基因查询它们后，在名为“途径水平共表达（PLC）”的过程中捕获给定途径的转录情况。网络构建的关键步骤仍然是基因共表达的定义。在当前的工作中，我们比较了皮尔逊相关系数（PCC），斯皮尔曼相关系数（SCC），它们各自的排名值（最高倒数排名（HRR）），互信息（MI）和偏相关（PC）在全球网络上的表现和PLC。使用微阵列和不同预处理的RNA-seq数据集对模型植物拟南芥进行了评估。我们特别评估了如何在5个PLC中执行数据集×距离测量组合，这些PLC对应于4个详细描述的植物代谢途径（苯丙烷，碳水化合物，脂肪酸和萜烯代谢）和细胞分裂素信号传导途径。我们目前的工作强调了与HRR相比，PCC如何比其他距离方法更适用于全球网络建设以及具有微阵列和RNA-seq数据的PLC，尤其是在与生物子途径相似的分区中聚集基因。