Integrating Trajectory Inference and Self-Explainable Predictive Models to Explore Cell State Transitions in Breast Cancer at Single- Cell Resolution

Breast cancer is characterized by a highly heterogeneous cellular environment composed of diverse malignant clones and components of the tumor microenvironment (TME) that collectively influence the progression of the disease. Single-cell RNA sequencing (scRNA-seq) offers a powerful tool to dissect this complexity, enabling high-resolution characterization of tumor heterogeneity and the functional interactions within the TME. Moreover, it supports the discovery of clinically relevant subpopulations and potential therapeutic targets. In this study, we present a novel scRNA-seq dataset from an infiltrating ductal breast cancer, profiling over 5,000 cells and identifying six distinct clusters spanning cancer and TME populations. To explore the molecular drivers of cell state transitions, we integrate pseudotime trajectory inference with interpretable, tree-based machine learning. This approach enables the identification of key genes and expression thresholds associated with dynamic phenotypic shifts. Unlike black-box models, our framework yields transparent, rule-based insights into transcriptional reprogramming during tumor evolution. The resulting dataset, along with an accessible and transparent analytical pipeline, offers a valuable resource for the breast cancer research community and lays the groundwork for future studies aimed at refining molecular classification and precision therapy development. Overall design: We analyzed single cell RNA-seq data from breast cancer tissue.