Interpretable trajectory inference with single-cell linear adaptive negative-binomial expression (scLANE) testing

Abstract The rapid proliferation of trajectory inference methods for single-cell RNA-seq data has allowed researchers to investigate complex biological processes by examining underlying gene expression dynamics. After estimating a latent cell ordering, statistical models are used to identify genes exhibiting changes in expression significantly associated with progression through the trajectory. While a few techniques for trajectory differential expression exist, most rely on generalized additive models to account for the inherent nonlinearity of gene expression dynamics. As such, the results can be difficult to interpret, and biological conclusions rely on subjective visual inspections. To address this challenge, we propose single-cell linear adaptive negative-binomial expression (scLANE) testing, which is built around an interpretable generalized linear model and handles nonlinearity with basis splines chosen empirically for each gene. In addition, extensions to estimating equations and mixed models allow for reliable trajectory testing under complex experimental designs. After validating the accuracy of scLANE under several simulation scenarios, we applied it to a set of diverse biological datasets and demonstrated its ability to provide novel biological information when used downstream of both pseudotime and RNA velocity estimation methods. scLANE is freely available as an R package through Bioconductor at https://bioconductor.org/packages/scLANE/, and is also accessible via a web server leveraging high-performance computing resources at https://sclane.rc.ufl.edu/.