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

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 uncertainties in 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 framework for eva

SUBMITTER: Dong X 

PROVIDER: S-EPMC10769271 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature