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Updated: Dec 27, 2025

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Trajectory-based differential expression analysis for single-cell sequencing data.

Koen Van den Berge1,2,3, Hector Roux de Bézieux4,5, Kelly Street6,7

  • 1Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.

Nature Communications
|March 7, 2020
PubMed
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This summary is machine-generated.

We introduce tradeSeq, a novel generalized additive model for analyzing single-cell RNA sequencing data. This tool enhances the discovery of genes associated with cell lineages and differential gene expression, providing clearer biological insights.

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) enables studying dynamic gene expression changes.
  • Trajectory inference is crucial for understanding cell differentiation and development.
  • Identifying lineage-associated and differentially expressed genes is vital for biological discovery.

Purpose of the Study:

  • To develop a robust statistical framework for differential gene expression analysis following trajectory inference.
  • To address limitations of existing methods in exploiting continuous trajectory resolution and pinpointing differential expression types.
  • To provide a flexible model for both within-lineage and between-lineage gene expression comparisons.

Main Methods:

  • Introduced tradeSeq, a generalized additive model framework utilizing the negative binomial distribution.

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  • Incorporated observation-level weights to handle zero-inflation in scRNA-seq data.
  • Evaluated tradeSeq on simulated and real-world scRNA-seq datasets from droplet-based and full-length protocols.

    • Main Results:

    • tradeSeq enables flexible inference of within-lineage and between-lineage differential expression.
    • The model effectively accounts for zero inflation, a common issue in scRNA-seq data.
    • Demonstrated biological insights and clear data interpretation on diverse datasets.

    Conclusions:

    • tradeSeq offers a powerful and flexible approach for gene expression analysis in single-cell trajectory studies.
    • The method improves the discovery of biologically relevant genes by leveraging continuous trajectory information.
    • tradeSeq provides a valuable tool for advancing research in developmental biology and cell differentiation.