School of Mathematics, South China University of Technology, Guangzhou 510640, China.
School of mathematics and big data, Foshan University, Foshan 528225, China.
Brief Bioinform. 2022 Sep 20;23(5). doi: 10.1093/bib/bbac177.
The dynamics of complex diseases are not always smooth; they are occasionally abrupt, i.e. there is a critical state transition or tipping point at which the disease undergoes a sudden qualitative shift. There are generally a few significant differences in the critical state in terms of gene expressions or other static measurements, which may lead to the failure of traditional differential expression-based biomarkers to identify such a tipping point. In this study, we propose a computational method, the direct interaction network-based divergence, to detect the critical state of complex diseases by exploiting the dynamic changes in multivariable distributions inferred from observable samples and local biomolecular direct interaction networks. Such a method is model-free and applicable to both bulk and single-cell expression data. Our approach was validated by successfully identifying the tipping point just before the occurrence of a critical transition for both a simulated data set and seven real data sets, including those from The Cancer Genome Atlas and two single-cell RNA-sequencing data sets of cell differentiation. Functional and pathway enrichment analyses also validated the computational results from the perspectives of both molecules and networks.
复杂疾病的动态变化并不总是平稳的；它们偶尔会突然发生，即疾病会经历一个关键的状态转变或临界点，发生突然的定性转变。在关键状态方面，通常在基因表达或其他静态测量方面存在一些显著差异，这可能导致传统基于差异表达的生物标志物无法识别这样的临界点。在这项研究中，我们提出了一种计算方法，即基于直接相互作用网络的散度，通过利用从可观察样本和局部生物分子直接相互作用网络推断出的多变量分布的动态变化来检测复杂疾病的关键状态。这种方法是无模型的，适用于批量和单细胞表达数据。我们的方法通过成功识别模拟数据集和七个真实数据集（包括来自癌症基因组图谱和两个单细胞 RNA-seq 数据集的细胞分化）在关键转变发生之前的临界点得到了验证。功能和途径富集分析也从分子和网络的角度验证了计算结果。
