Identification of differentially expressed genes (DEGs) is a pivotal step in single-cell RNA sequencing (scRNA-seq) data analysis. The sparsity and multi-model distribution of scRNA-seq data decides that the traditional tools designed for bulk RNA-seq have several limitations when applied to single-cell data. On the other hand, tools specifically for DEGs analysis of scRNA-seq data normally does not consider the high dimensionality of the data. To this end, we present DEAPLOG, a method for differential expression analysis and pseudo-temporal locating and ordering of genes in single-cell transcriptomic data. We show that DEAPLOG has higher accurate and efficient in DEGs identification when compared with existing methods in both artificial and real datasets. Additionally, DEAPLOG can infer pseudo-time and embedding coordinates of genes, therefore is useful in identifying regulators in trajectory of cell fate decision.