A single-sample network (SSN) is a biological molecular network constructed from single-sample data given a reference dataset and can provide insights into the mechanisms of individual diseases and aid in the development of personalized medicine. In this study, we proposed a computational method, a partial correlation-based single-sample network (P-SSN), which not only infers a network from each single-sample data given a reference dataset but also retains the direct interactions by excluding indirect interactions (https://github.com/hyhRise/P-SSN). By applying P-SSN to analyze tumor data from the Cancer Genome Atlas and single cell data, we validated the effectiveness of P-SSN in predicting driver mutation genes (DMGs), producing network distance, identifying subtypes and further classifying single cells. In particular, P-SSN is highly effective in predicting DMGs based on single-sample data. P-SSN is also efficient for subtyping complex diseases and for clustering single cells by introducing network distance between any two samples.

中文翻译：

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使用基于部分相关的样本特定网络进行疾病表征。

单样本网络（SSN）是由给定参考数据集的单样本数据构建的生物分子网络，可以深入了解个体疾病的机制并有助于个性化医疗的发展。在这项研究中，我们提出了一种计算方法，一种基于偏相关的单样本网络（P-SSN），它不仅可以从给定参考数据集的每个单样本数据中推断出一个网络，而且还通过排除间接影响来保留直接相互作用。互动（https://github.com/hyhRise/P-SSN）。通过应用 P-SSN 分析来自癌症基因组图谱和单细胞数据的肿瘤数据，我们验证了 P-SSN 在预测驱动突变基因 (DMG)、产生网络距离、识别亚型和进一步分类单细胞方面的有效性。特别是，P-SSN 在基于单样本数据预测 DMG 方面非常有效。P-SSN 通过在任意两个样本之间引入网络距离，还可有效地对复杂疾病进行亚型分型和对单个细胞进行聚类。