Journal
BIOINFORMATICS
Volume 38, Issue 16, Pages 3927-3934Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btac423
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Funding
- National Science Foundation [DBI-1846216, DMS-2113754]
- National Institutes of Health/NIGMS [R01GM120507, R35GM140888]
- Johnson and Johnson WiSTEM2D Award
- Sloan Research Fellowship
- UCLA David Geffen School of Medicine W.M. Keck Foundation Junior Faculty Award
- Chan-Zuckerberg Initiative SingleCell Biology Data Insights Grant
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This article proposes a single-cell generalized trend model (scGTM) for capturing gene expression trends, helping to interpret biological processes along cell pseudotime. The model has excellent interpretability and flexibility, making it useful for analyzing gene expression data.
Motivation: Modeling single-cell gene expression trends along cell pseudotime is a crucial analysis for exploring biological processes. Most existing methods rely on nonparametric regression models for their flexibility; however, nonparametric models often provide trends too complex to interpret. Other existing methods use interpretable but restrictive models. Since model interpretability and flexibility are both indispensable for understanding biological processes, the single-cell field needs a model that improves the interpretability and largely maintains the flexibility of nonparametric regression models. Results: Here, we propose the single-cell generalized trend model (scGTM) for capturing a gene's expression trend, which may be monotone, hill-shaped or valley-shaped, along cell pseudotime. The scGTM has three advantages: (i) it can capture non-monotonic trends that are easy to interpret, (ii) its parameters are biologically interpretable and trend informative, and (iii) it can flexibly accommodate common distributions for modeling gene expression counts. To tackle the complex optimization problems, we use the particle swarm optimization algorithm to find the constrained maximum likelihood estimates for the scGTM parameters. As an application, we analyze several single-cell gene expression datasets using the scGTM and show that scGTM can capture interpretable gene expression trends along cell pseudotime and reveal molecular insights underlying biological processes.
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