The rapid advancement of single-cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. However, compared to bulk RNA sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq) suffers from higher noise and lower coverage, which brings new computational difficulties. Based on statistical independence, cell-specific network (CSN) is able to quantify the overall associations between genes for each cell, yet suffering from a problem of overestimation related to indirect effects. To overcome this problem, we propose the c-CSN method, which can construct the conditional cell-specific network (CCSN) for each cell. c-CSN method can measure the direct associations between genes by eliminating the indirect associations. c-CSN can be used for cell clustering and dimension reduction on a network basis of single cells. Intuitively, each CCSN can be viewed as the transformation from less “reliable” gene expression to more “reliable” gene–gene associations in a cell. Based on CCSN, we further design network flow entropy (NFE) to estimate the differentiation potency of a single cell. A number of scRNA-seq datasets were used to demonstrate the advantages of our approach. 1) One direct association network is generated for one cell. 2) Most existing scRNA-seq methods designed for gene expression matrices are also applicable to c-CSN-transformed degree matrices. 3) CCSN-based NFE helps resolving the direction of differentiation trajectories by quantifying the potency of each cell. c-CSN is publicly available at https://github.com/LinLi-0909/c-CSN.
单细胞技术的快速发展使人们对细胞异质性的复杂机制有了新的认识。然而，与传统的bulk RNA测序(RNA-seq)相比，单细胞RNA-seq (scRNA-seq)的噪声更高，覆盖面更低。这些增大了生物信息学分析的困难。在之前的工作中，基于统计独立性构建的细胞特异性网络(cell-specific network, CSN)能够量化每个细胞的基因之间的整体关联，但CSN方法构建的网络存在间接相关，从而存在过估计的问题。为了克服这一问题，我们提出了c-CSN方法，该方法可以为每个细胞构建条件细胞特异性网络(CCSN)。c-CSN方法通过消除基因间的间接关联来测量基因间的直接关联。c-CSN可以在单个细胞的网络基础上进行细胞聚类和降维。直观地看，每个CCSN都可以看作是细胞内从不太“可靠”的基因表达到更“可靠”的基因-基因关联的转化。同时在CCSN的基础上，我们进一步设计了网络流熵(NFE)来评估单个细胞的分化能力。





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