Trajectory-guided dimensionality reduction for multi-sample single-cell RNA-seq data reveals biologically relevant sample-level heterogeneity

The analysis of single-cell RNA-sequencing (scRNA-seq) data with multiple biological samples remains a pressing challenge. We present MUSTARD, a trajectory-guided dimension reduction method for multi-sample multi-condition scRNA-seq data. This all-in-one decomposition reveals major gene expression variation patterns along the trajectory and across multiple samples simultaneously, providing opportunities to discover sample endotypes along with associated genes and gene modules. In data-driven simulation, MUSTARD achieves high accuracy in distinguishing sample-level group differences that existing methods fail to capture. MUSTARD also demonstrates a robust ability to capture gene markers and pathways associated with phenotypes of interest across multiple real-world case studies.