Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference

Abstract

Abstract Single-cell RNA sequencing (scRNA-seq) experiments have become instrumental in developmental and differentiation studies, enabling the profiling of cells at a single or multiple time-points to uncover subtle variations in expression profiles reflecting underlying biological processes. Benchmarking studies have compared many of the computational methods used to reconstruct cellular dynamics; however, researchers still encounter challenges in their analysis due to uncertainty with respect to selecting the most appropriate methods and parameters. Even among universal data processing steps used by trajectory inference methods such as feature selection and dimension reduction, trajectory methods’ performances are highly dataset-specific. To address these challenges, we developed Escort, a novel framework for evaluating a dataset’s suitability for trajectory inference and quantifying trajectory properties influenced by analysis decisions. Escort evaluates the suitability of trajectory analysis and the combined effects of processing choices using trajectory-specific metrics. Escort navigates single-cell trajectory analysis through these data-driven assessments, reducing uncertainty and much of the decision burden inherent to trajectory inference analyses. Escort is implemented in an accessible R package and R/Shiny application, providing researchers with the necessary tools to make informed decisions during trajectory analysis and enabling new insights into dynamic biological processes at single-cell resolution.