Inference of Tumor Progression Patterns in Colon Cancer using Optimal Cell Order Analysis in Single Cell Resolution

Accurate determination of lineage or evolutionary pathways is critical for understanding the dynamic developmental and temporal progression patterns observed in single cell omics data. In this study, we present a computational approach designed to infer progression patterns in cell populations that are actively evolving, differentiating or progressing along dynamic pathways. Our method works at single cell resolution using single cell RNA-Seq data. We determine the optimal cell order based on the cellular transcriptional profiles to uncover the progression of cell populations along differentiation, signaling, or tumor evolution trajectories. To achieve this, we developed a seriation-based method for progression pattern inference, leveraging optimally reordered hierarchies. We provide advanced visualization tools using principal curves to represent the inferred pathways in a three-dimensional latent space derived from scRNA-Seq data. Furthermore, we propose new metrics for evaluating the accuracy of the reconstructed order and identified pathways. The effectiveness of our approach is demonstrated through applying the analysis to real human colon samples transcriptomics data that we generated specifically for this study.