Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.

基因共表达网络捕获基因表达数据中具有生物学重要意义的模式，从而能够对基因进行功能分析、发现生物标志物以及解释遗传变异。迄今为止，大多数网络分析仅限于评估单个组织或一小组组织中总基因表达水平之间的相关性。在这里，我们建立了网络，该网络还捕获相对同种型丰度和剪接的调节，以及每种不同组织所特有的组织特异性连接。我们使用了 50 个组织和 449 个个体的基因型组织表达 (GTEx) 项目 v6 RNA 测序数据。首先，我们开发了一个称为转录组范围网络（TWN）的框架，用于将总表达和相对亚型水平组合到单个稀疏网络中，捕获剪接和转录调节之间的相互作用。我们为 16 个组织构建了 TWN，发现这些网络中的中枢高度富集剪接和 RNA 结合基因，证明了它们在揭示人类转录组剪接调节方面的实用性。接下来，我们使用贝叶斯双聚类模型来识别单个组织特有的网络边缘，以重建 26 个不同组织和 10 组相关组织的组织特异性网络 (TSN)。最后，我们发现了与网络中相邻节点对相关的遗传变异，支持了估计的网络结构，并识别了 20 个对转录和剪接具有远程调控影响的遗传变异。我们的网络可以更好地理解人类转录组跨组织的复杂关系。