Genome-wide gene expression analysis are routinely used to gain a systems-level understanding of complex processes, including network connectivity. Network connectivity tends to be built on a small subset of extremely high co-expression signals that are deemed significant, but this overlooks the vast majority of pairwise signals. Here, we developed a computational pipeline to assign to every gene its pair-wise genome-wide co-expression distribution to one of 8 template distributions shapes varying between unimodal, bimodal, skewed, or symmetrical, representing different proportions of positive and negative correlations. We then used a hypergeometric test to determine if specific genes (regulators versus non-regulators) and properties (differentially expressed or not) are associated with a particular distribution shape. We applied our methodology to five publicly available RNA sequencing (RNA-seq) datasets from four organisms in different physiological conditions and tissues. Our results suggest that genes can be assigned consistently to pre-defined distribution shapes, regarding the enrichment of differential expression and regulatory genes, in situations involving contrasting phenotypes, time-series, or physiological baseline data. There is indeed a striking additional biological signal present in the genome-wide distribution of co-expression values which would be overlooked by currently adopted approaches. Our method can be applied to extract further information from transcriptomic data and help uncover the molecular mechanisms involved in the regulation of complex biological process and phenotypes.

中文翻译：

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全基因组共表达分布作为优先考虑功能重要性基因的指标

全基因组基因表达分析通常用于获得对复杂过程（包括网络连接）的系统级理解。网络连接往往建立在一小部分被认为很重要的极高共表达信号的基础上，但这忽略了绝大多数成对信号。在这里，我们开发了一个计算管道，将每个基因的成对全基因组共表达分布分配给 8 个模板分布形状之一，这些形状在单峰、双峰、倾斜或对称之间变化，代表不同比例的正相关和负相关。然后，我们使用超几何测试来确定特定基因（调节基因与非调节基因）和属性（差异表达或无差异表达）是否与特定分布形状相关。我们将我们的方法应用于来自不同生理条件和组织的四种生物体的五个公开的 RNA 测序 (RNA-seq) 数据集。我们的结果表明，在涉及对比表型、时间序列或生理基线数据的情况下，关于差异表达和调控基因的富集，基因可以一致地分配到预定义的分布形状。在共表达值的全基因组分布中确实存在一个引人注目的额外生物信号，而当前采用的方法可能会忽略这些信号。我们的方法可用于从转录组数据中提取更多信息，并帮助揭示复杂生物过程和表型调节所涉及的分子机制。