c-CSN: Single-cell RNA Sequencing Data Analysis by Conditional Cell-specific Network

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 data-sets 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.