Abstract

Inference of single-cell developmental potential from scRNA-Seq data enables us to reconstruct the pseudo-temporal path of cell development, which is an important and challenging task for single-cell analysis. Single-cell transcriptional diversity (SCTD), measured by the number of expressed genes per cell, has been found to be negatively correlated with the development time, and thus can be considered as a hallmark of developmental potential. However, in some cases, the gene expression level of the cells in the early stages of development may be lower than that of the later stages, which may lead to incorrect estimation of differentiation states by gene diversity-based inference. Here we refer to the economic complexity theory and propose single-cell transcriptional complexity (SCTC) metrics as a measure of single-cell developmental potential, given the intrinsic similarities between biological and economic complex systems. We take into account not only the number of genes expressed by cells, but also the more sophisticated structure information of gene expression by treating the scRNA-seq count matrix as a bipartite network. We show that complexity metrics characterize the developmental potential more accurately than the diversity metrics. Especially, in the early stages of development, cells typically have lower gene expression level than that in the later stages, while their complexity in the early stages is significantly higher than that in the later stages. Based on the measurement of SCTC, we provide an unsupervised method for accurate, robust, and transferable inference of single-cell pseudotime. Our findings suggest that the complexity emerging from the interaction between cells and genes determines the developmental potential, which may bring new insights into the understanding of biological development from the perspective of the complexity theory.