Alignment of single-cell trajectories to compare cellular expression dynamics

• Published in: Nature methods Vol. 15; no. 4; pp. 267 - 270
• Main Authors: Alpert, Ayelet, Moore, Lindsay S, Dubovik, Tania, Shen-Orr, Shai S
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01-04-2018; Nature Publishing Group
• Subjects: 13/31; 38; 49/91; 631/114/2415; 631/136/142; 631/136/2086; 631/208/199; 631/208/514/1949; Algorithms; Bioinformatics; Biological activity; Biological Microscopy; Biological Techniques; Biomedical Engineering/Biotechnology; brief-communication; Cells; Computational biology; Cytometry; Embryonic development; Embryos; Flow cytometry; Gene expression; Gene sequencing; Genetic aspects; Genetic research; Life Sciences; Methods; Proteomics; Ribonucleic acid; RNA; RNA sequencing; Trajectories
• ISSN: 1548-7091; 1548-7105
• DOI: 10.1038/nmeth.4628

cellAlign enables quantitative comparisons of expression dynamics within and between single-cell trajectories based on single-cell RNA-seq or mass cytometry data.
Single-cell RNA sequencing and high-dimensional cytometry can be used to generate detailed trajectories of dynamic biological processes such as differentiation or development. Here we present cellAlign, a quantitative framework for comparing expression dynamics within and between single-cell trajectories. By applying cellAlign to mouse and human embryonic developmental trajectories, we systematically delineate differences in the temporal regulation of gene expression programs that would otherwise be masked.