Gene-level alignment of single cell trajectories

Abstract

AbstractSingle-cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation. To compare these dynamics between two conditions, trajectory alignment via dynamic programming (DP) optimization is frequently used, but is limited by assumptions such as a definite existence of a match. Here we describeGenes2Genes, a Bayesian information-theoretic DP framework for aligning single-cell trajectories.Genes2Genesovercomes current limitations and is able to capture sequential matches and mismatches between a reference and a query at single gene resolution, highlighting distinct clusters of genes with varying patterns of expression dynamics. Across both real world and simulated datasets,Genes2Genesaccurately captured different alignment patterns, demonstrated its utility in disease cell state trajectory analysis, and revealed that T cells differentiatedin vitromatched to an immaturein vivostate while lacking expression of genes associated with TNFɑ signaling. This use case demonstrates that precise trajectory alignment can pinpoint divergence from thein vivosystem, thus guiding the optimization ofin vitroculture conditions.