Abstract
Single 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 describe Genes2Genes, a Bayesian information-theoretic DP framework for aligning single-cell trajectories. Genes2Genes overcomes 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 gene expression dynamics. Across both real life and simulated datasets, Genes2Genes accurately captured different alignment patterns, and revealed that T cells differentiated in vitro matched to an immature in vivo state while lacking the final TNFα signaling. This use case demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus providing an opportunity to optimize in vitro culture conditions.
Competing Interest Statement
In the past three years, S.A.T. has received remuneration for Scientific Advisory Board Membership from Sanofi, GlaxoSmithKline, Foresite Labs and Qiagen. S.A.T. is a co-founder and holds equity in Transition Bio.
