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为细胞异质性的复杂机制提供了新的视角。然而，与bulk RNA测序（RNA-seq）相比，单细胞RNA-seq（scRNA-seq）具有更高的噪声和更低的覆盖率，这带来了新的计算困难。基于统计独立性，细胞特异性网络（CSN）能够量化每个细胞基因之间的整体关联，但存在与间接影响相关的高估问题。为了克服这个问题，我们提出了 c-CSN 方法，该方法可以为每个小区构建条件小区特定网络（CCSN）。c-CSN 方法可以测量直接关联通过消除间接关联在基因之间。c-CSN 可用于基于单个小区的网络进行小区聚类和降维。直观地说，每个 CCSN 都可以被视为细胞中从不太“可靠”的基因表达到更“可靠”的基因-基因关联的转变。基于CCSN，我们进一步设计了网络流熵(NFE) 来估计单个细胞的分化潜能。许多 scRNA-seq 数据集被用来证明我们方法的优势。1）为一个小区生成一个直接关联网络。2) 大多数现有的为基因表达矩阵设计的 scRNA-seq 方法也适用于 c-CSN 转换的度数矩阵。3）基于CCSN的NFE通过量化每个细胞的效力来帮助解决分化轨迹的方向。c-CSN 在 https://github.com/LinLi-0909/c-CSN 上公开可用。