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Abstract MotivationModeling 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. ResultsHere, 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. Availability and implementationThe Python package scGTM is open-access and available at https://github.com/ElvisCuiHan/scGTM. Supplementary informationSupplementary data are available at Bioinformatics online.
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Differential Expression Gene Explorer (DrEdGE): a tool for generating interactive online visualizations of gene expression datasets
Abstract SummaryDifferential Expression Gene Explorer (DrEdGE) is a web-based tool that guides genomicists through easily creating interactive online data visualizations, which colleagues can query according to their own conditions to discover genes, samples or patterns of interest. We demonstrate DrEdGE’s features with three example websites generated from publicly available datasets—human neuronal tissue, mouse embryonic tissue and Caenorhabditis elegans whole embryos. DrEdGE increases the utility of large genomics datasets by removing technical obstacles to independent exploration. Availability and implementationFreely available at http://dredge.bio.unc.edu. Supplementary informationSupplementary data are available at Bioinformatics online.
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- Award ID(s):
- 1557432
- PAR ID:
- 10131384
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Bioinformatics
- ISSN:
- 1367-4803
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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