دورية أكاديمية
scShapes: a statistical framework for identifying distribution shapes in single-cell RNA-sequencing data.
| العنوان: | scShapes: a statistical framework for identifying distribution shapes in single-cell RNA-sequencing data. |
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| المؤلفون: | Dharmaratne M; Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, 4072, Australia., Kulkarni AS; Institute for Aging Research, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA.; Department of Medicine, Division of Endocrinology, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA., Taherian Fard A; Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, 4072, Australia., Mar JC; Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, 4072, Australia. |
| المصدر: | GigaScience [Gigascience] 2022 Dec 28; Vol. 12. Date of Electronic Publication: 2023 Jan 24. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't |
| اللغة: | English |
| بيانات الدورية: | Publisher: Oxford University Press Country of Publication: United States NLM ID: 101596872 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2047-217X (Electronic) Linking ISSN: 2047217X NLM ISO Abbreviation: Gigascience Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: 2017- : New York : Oxford University Press Original Publication: London : BioMed Central |
| مواضيع طبية MeSH: | Software* , Transcriptome*, Sequence Analysis, RNA/methods ; Gene Expression Regulation ; RNA/genetics ; Single-Cell Analysis/methods ; Gene Expression Profiling/methods |
| مستخلص: | Background: Single-cell RNA sequencing (scRNA-seq) methods have been advantageous for quantifying cell-to-cell variation by profiling the transcriptomes of individual cells. For scRNA-seq data, variability in gene expression reflects the degree of variation in gene expression from one cell to another. Analyses that focus on cell-cell variability therefore are useful for going beyond changes based on average expression and, instead, identifying genes with homogeneous expression versus those that vary widely from cell to cell. Results: We present a novel statistical framework, scShapes, for identifying differential distributions in single-cell RNA-sequencing data using generalized linear models. Most approaches for differential gene expression detect shifts in the mean value. However, as single-cell data are driven by overdispersion and dropouts, moving beyond means and using distributions that can handle excess zeros is critical. scShapes quantifies gene-specific cell-to-cell variability by testing for differences in the expression distribution while flexibly adjusting for covariates if required. We demonstrate that scShapes identifies subtle variations that are independent of altered mean expression and detects biologically relevant genes that were not discovered through standard approaches. Conclusions: This analysis also draws attention to genes that switch distribution shapes from a unimodal distribution to a zero-inflated distribution and raises open questions about the plausible biological mechanisms that may give rise to this, such as transcriptional bursting. Overall, the results from scShapes help to expand our understanding of the role that gene expression plays in the transcriptional regulation of a specific perturbation or cellular phenotype. Our framework scShapes is incorporated into a Bioconductor R package (https://www.bioconductor.org/packages/release/bioc/html/scShapes.html). (© The Author(s) 2023. Published by Oxford University Press GigaScience.) |
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| معلومات مُعتمدة: | FT170100047 Australian Research Council; Australasian Genomic Technologies Association |
| فهرسة مساهمة: | Keywords: distribution shapes; single-cell RNA-sequencing; zero inflation |
| المشرفين على المادة: | 63231-63-0 (RNA) |
| تواريخ الأحداث: | Date Created: 20230124 Date Completed: 20230125 Latest Revision: 20240911 |
| رمز التحديث: | 20240911 |
| مُعرف محوري في PubMed: | PMC9871437 |
| DOI: | 10.1093/gigascience/giac126 |
| PMID: | 36691728 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 2047-217X |
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| DOI: | 10.1093/gigascience/giac126 |