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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
Summary
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.
- 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.
