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.
Gene Expression Omnibus
This study was supported by the Israel Science Foundation (ISF) grants 1365/12 and the Applebaum Foundation. We thank Y. Ofran, M. Drukker, N. Kaplan, K. Brennand, and members of the Shen-Orr lab for fruitful discussions, and D. Alpert for assistance in algorithmic design and technical support.
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