<p>Differential expression analysis constitutes a crucial step in the analysis of single-cell transcriptomic data. Numerous statistical methods have been developed to conduct differential expression analysis by addressing the sparsity or heterogeneity of gene expression. Nevertheless, these approaches often overlook other critical characteristics of single-cell transcriptomic data, such as the high dimensionality of gene expression at the cellular level, which may consequently lead to suboptimal performance. Furthermore, to date, there remains a significant gap in methodologies capable of locating and ordering genes along cell trajectories. Here, we integrate polynomial fitting with hypergeometric testing to develop a new tool, DEAPLOG (Differential Expression Analysis and Pseudo-temporal Locating and Ordering of Genes), leveraging the high-dimensional gene expression characteristics at the cellular level in single-cell transcriptomic data. Benchmarking analyses on synthetic single-cell datasets demonstrate that while DEAPLOG exhibits performance comparable to existing methods on datasets comprising only two cell clusters, it demonstrates superior performance in differential expression analysis when applied to datasets with multiple cell clusters. Furthermore, the applications of DEAPLOG to real single-cell and spatial transcriptomic dataset not only validate its superior performance in terms of accuracy but also computational efficiency. Notably, when applied to single-cell transcriptomic data from the developmental hematopoietic system, DEAPLOG demonstrate precise gene localization and accurate ordering along developmental trajectories. Collectively, these findings establish DEAPLOG as a robust and highly effective tool for single-cell transcriptomic data analysis.</p> Graphical Abstract <p></p>