scGRN-Entropy: Inferring cell differentiation trajectories using single-cell data and gene regulation network-based transfer entropy

Research on cell differentiation facilitates a deeper understanding of the fundamental processes of life, elucidates the intrinsic mechanisms underlying diseases such as cancer, and advances the development of therapeutics and precision medicine. Existing methods for inferring cell differentiation trajectories from single-cell RNA sequencing (scRNA-seq) data primarily rely on static gene expression data to measure distances between cells and subsequently infer pseudotime trajectories. In this work, we introduce a novel method, scGRN-Entropy, for inferring cell differentiation trajectories and pseudotime from scRNA-seq data. Unlike existing approaches, scGRN-Entropy improves inference accuracy by incorporating dynamic changes in gene regulatory networks (GRN). In scGRN-Entropy, an undirected graph representing state transitions between cells is constructed by integrating both static relationships in gene expression space and dynamic relationships in the GRN space. The edges of the undirected graph are then refined using pseudotime inferred based on cell entropy in the GRN space. Finally, the Minimum Spanning Tree (MST) algorithm is applied to derive the cell differentiation trajectory. We validate the accuracy of scGRN-Entropy on eight different real scRNA-seq datasets, demonstrating its superior performance in inferring cell differentiation trajectories through comparative analysis with existing state-of-the-art methods.Author summary: It is very important to study cell differentiation because it can help us understand the fundamental processes of life, elucidates the intrinsic mechanisms underlying diseases such as cancer, and advances the development of therapeutics and precision medicine. However, the existed methods for this usually much more rely on static gene expression data. They ignore the dynamical behavior of it. In this paper, we introduce method named scGRN-Entropy for inferring cell differentiation trajectories and pseudotime from scRNA-seq data. Our method divides cellular differentiation relationships into static and dynamic types. Static relationships are calculated based on gene expression levels, while dynamic relationships are derived from the similarity of cellular GRNs. We obtain the GRN from ordinary differential equations of gene expression, reflecting the internal dynamic regulatory relationships within cells. Incorporating GRNs into trajectory inference considers both biological reality and real datasets, it shows that our method can infer the cell differentiation trajectories much more accurately.