利用稀疏典型相关分析推断基因-基因相互作用和功能模块
Inferring gene-gene interactions and functional modules using sparse canonical correlation analysis
摘要：
网络渗透到许多科学学科中，用于分析具有交互组件的复杂系统。特别是，这个概念通常用于模拟基因之间的相互作用，并确定密切相关的基因形成功能模块。在本文中，我们专注于基因组的相互作用，并推断这些相互作用使用适当的偏相关基因之间，即，基因之间的条件依赖关系后，删除一组其他功能相关的基因的影响。我们介绍了一种新的方法，估计组间相互作用的稀疏典型相关分析（SCCA）加上重复随机分区和子采样的基因表达数据集。通过考虑不同的基因子集和分组方式，我们的相互作用度量可以被看作是不同阶数的偏相关的聚集估计。我们的方法是独特的，在评估条件依赖时，正确的依赖集是未知的或只有部分已知的。因此，可以使用相互作用度量作为边权重来构建基因网络，并且可以从网络中推断基因功能组为紧密连接的社区。与几种流行的方法，使用模拟和真实数据的比较表明，我们的程序提高了统计意义和生物可解释性的结果。除了实现相当低的假阳性率，我们的程序在检测重要的生物途径方面表现出更好的性能。
英文摘要：
Networks pervade many disciplines of science for analyzing complex systems with interacting components. In particular, this concept is commonly used to model interactions between genes and identify closely associated genes forming functional modules. In this paper, we focus on gene group interactions and infer these interactions using appropriate partial correlations between genes, that is, the conditional dependencies between genes after removing the influences of a set of other functionally related genes. We introduce a new method for estimating group interactions using sparse canonical correlation analysis (SCCA) coupled with repeated random partition and subsampling of the gene expression data set. By considering different subsets of genes and ways of grouping them, our interaction measure can be viewed as an aggregated estimate of partial correlations of different orders. Our approach is unique in evaluating conditional dependencies when the correct dependent sets are unknown or only partially known. As a result, a gene network can be constructed using the interaction measures as edge weights and gene functional groups can be inferred as tightly connected communities from the network. Comparisons with several popular approaches using simulated and real data show our procedure improves both the statistical significance and biological interpretability of the results. In addition to achieving considerably lower false positive rates, our procedure shows better performance in detecting important biological pathways.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
