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

Jack R Leary¹, Xiaoru Dong^1,2,3, Rhonda Bacher¹

¹Department of Biostatistics, University of Florida, 2004 Mowry Road, FL 32611, United States.

Nucleic Acids Research

|January 14, 2026

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Single-cell linear adaptive negative-binomial expression (scLANE) testing offers an interpretable approach for analyzing gene expression dynamics in single-cell RNA sequencing data. This method improves biological insights from trajectory inference, overcoming limitations of existing models.

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) enables gene expression dynamics analysis.
Trajectory inference methods identify cell differentiation paths.
Existing differential expression methods for trajectories are often nonlinear and difficult to interpret.

Purpose of the Study:

To develop a novel, interpretable statistical method for trajectory differential expression analysis in scRNA-seq data.
To address the interpretability challenges posed by nonlinear models in current trajectory analysis.

Main Methods:

Proposed single-cell linear adaptive negative-binomial expression (scLANE) testing.
Utilized an interpretable generalized linear model framework.
Incorporated basis splines for handling nonlinear gene expression dynamics.

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Extended methods for complex experimental designs using estimating equations and mixed models.

Main Results:

Validated scLANE's accuracy through various simulation scenarios.
Demonstrated scLANE's ability to extract novel biological information from diverse scRNA-seq datasets.
Showcased scLANE's utility with both pseudotime and RNA velocity estimation methods.

Conclusions:

scLANE provides a robust and interpretable framework for trajectory differential expression analysis.
The method enhances biological discovery from scRNA-seq data, particularly in complex differentiation processes.
scLANE is available as an R package and a web server for broader accessibility.