基于单样本组学数据的生物分子网络构建理论和方法

一般说来复杂疾病的起因不是由于单个分子的功能缺失而是因为生物体在系统或网络层次上的功能失调而引起的，而生物系统的网络又会随着生物过程或疾病发生发展而变化。因此，从样本推断特定条件下的生物网络是在系统水平上阐明复杂疾病的分子机制的关键。然而，由于计算相关性等需要多样本数据，因此目前还没有一种可以仅通过单样本的表达谱数据构建个体特异性网络的有效方法。这里基于网络科学和统计学，我们建立一个全新的单样本生物分子网络的理论，开发了一种仅利用样本表达数据构建该样本特异性网络的方法。基于这种方法，我们可以实现网络标志物，不仅可以直接由网络进行疾病状态诊断和生物系统状态评估，应用于临床医学和网络生物学，而且可以构建单细胞生物分子网络，为单细胞测序分析提供了全新的方法。利用该方法对TCGA各种癌症数据等进行分析，发现新的个体特异的功能驱动基因和网络模式，由生物数据验证的单样本生物分子网络理论和方法。

Generally, a complex disease generally results not from malfunction of individual molecules but from dysfunction of the relevant system or network, which changes with states and conditions. Thus, estimating a condition-specific network from a sample is crucial to elucidating the molecular mechanisms of complex diseases at the system level. However, there is currently no effective way to construct such an individual-specific network by expression profiling of a single sample because of the requirement of multiple samples for computing correlations. In this project, we aim to develop a statistical method, i.e., a sample-specific network (SSN) method, which allows us to construct single-sample network (SSN) based on molecular expressions of a single sample. Using this method, we can characterize various human diseases at a network level. In particular, such sample-specific networks can lead to the identification of individual-specific disease modules as well as functional driver genes, even without gene sequencing information. We will validate this new method based on both numerical simulations.