Precise lineage or evolution path determination play a crucial role in discerning the dynamic developmental or temporal progression patterns observed in single cell RNA-Seq data. In this work, we present a novel computational approach for progression pattern inference of normal or tumor cell populations that are actively progressing along a dynamic pathway in single cell resolution. This is achieved via ordering the cellular transcriptional profiles identifying the progression of cell populations along differentiation, signaling, or tumor evolution paths. Here, we developed a seriation-based progression pattern inference method using optimally reordered hierarchies and provide advanced principal-curves-based visualization of the inferred paths in three dimensional latent space representation of scRNA-Seq data. Additionally, we present novel metrics for evaluating the reconstructed order and identified pathways and evaluate our approach using real single cell transcriptomics datasets.