The use of biological networks such as protein–protein interaction and transcriptional regulatory networks is becoming an integral part of genomics research. However, these networks are not static, and during phenotypic transitions like disease onset, they can acquire new “communities” (or highly interacting groups) of genes that carry out cellular processes. Disease communities can be detected by maximizing a modularity-based score, but since biological systems and network inference algorithms are inherently noisy, it remains a challenge to determine whether these changes represent real cellular responses or whether they appeared by random chance. Here, we introduce Constrained Random Alteration of Network Edges (CRANE), a method for randomizing networks with fixed node strengths. CRANE can be used to generate a null distribution of gene regulatory networks that can in turn be used to rank the most significant changes in candidate disease communities. Compared to other approaches, such as consensus clustering or commonly used generative models, CRANE emulates biologically realistic networks and recovers simulated disease modules with higher accuracy. When applied to breast and ovarian cancer networks, CRANE improves the identification of cancer-relevant GO terms while reducing the signal from non-specific housekeeping processes.

蛋白质-蛋白质相互作用和转录调控网络等生物网络的使用正在成为基因组学研究的一个组成部分。然而，这些网络不是静态的，在疾病发作等表型转变期间，它们可以获得执行细胞过程的新基因“社区”（或高度相互作用的群体）。可以通过最大化基于模块化的分数来检测疾病群落，但由于生物系统和网络推理算法本质上是有噪声的，因此确定这些变化是否代表真实的细胞反应或者它们是否是随机出现的仍然是一个挑战。在这里，我们介绍网络边缘的约束随机改变（CRANE），一种用于随机化具有固定节点强度的网络的方法。 CRANE 可用于生成基因调控网络的零分布，进而可用于对候选疾病群落中最显着的变化进行排名。与其他方法（例如共识聚类或常用的生成模型）相比，CRANE 模拟生物现实网络并以更高的准确度恢复模拟疾病模块。当应用于乳腺癌和卵巢癌网络时，CRANE 改进了癌症相关 GO 术语的识别，同时减少了来自非特定内务处理过程的信号。