Department of Mathematics, University of Maryland, College Park, MD.
School of Public Health, University of Maryland, College Park, MD.
Bioinformatics. 2021 Aug 4;37(14):1997–2003. doi: 10.1093/bioinformatics/btab047. Epub 2021 Jan 28.
The analysis of gene co-expression network (GCN) is critical in examining the gene-gene interactions and learning the underlying complex yet highly organized gene regulatory mechanisms. Numerous clustering methods have been developed to detect communities of co-expressed genes in the large network. The assumed independent community structure, however, can be oversimplified and may not adequately characterize the complex biological processes.
We develop a new computational package to extract interconnected communities from gene co-expression network. We consider a pair of communities be interconnected if a subset of genes from one community is correlated with a subset of genes from another community. The interconnected community structure is more flexible and provides a better fit to the empirical co-expression matrix. To overcome the computational challenges, we develop efficient algorithms by leveraging advanced graph norm shrinkage approach. We validate and show the advantage of our method by extensive simulation studies. We then apply our interconnected community detection method to an RNA-seq data from The Cancer Genome Atlas (TCGA) Acute Myeloid Leukemia (AML) study and identify essential interacting biological pathways related to the immune evasion mechanism of tumor cells.
The software is available at Github: https://github.com/qwu1221/ICN and Figshare: https://figshare.com/articles/software/ICN-package/13229093.
Supplementary data are available at Bioinformatics online.
分析基因共表达网络（GCN）对于研究基因-基因相互作用和学习潜在的复杂但高度组织化的基因调控机制至关重要。已经开发了许多聚类方法来检测大型网络中共同表达基因的社区。然而，假设的独立社区结构可能过于简单化，并且可能无法充分描述复杂的生物学过程。
我们开发了一种新的计算程序来从基因共表达网络中提取相互关联的社区。我们认为，如果一个社区的一组基因与另一个社区的一组基因相关，则这两个社区是相互关联的。相互关联的社区结构更加灵活，并能更好地适应经验共表达矩阵。为了克服计算上的挑战，我们通过利用先进的图范数收缩方法开发了高效的算法。我们通过广泛的模拟研究验证并展示了我们方法的优势。然后，我们将我们的相互关联社区检测方法应用于来自癌症基因组图谱（TCGA）急性髓系白血病（AML）研究的 RNA-seq 数据，并确定与肿瘤细胞免疫逃逸机制相关的重要相互作用的生物学途径。
该软件可在 Github 上获得：https://github.com/qwu1221/ICN 和 Figshare：https://figshare.com/articles/software/ICN-package/13229093。
补充数据可在生物信息学在线获得。
