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scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured

A pressing challenge in single-cell transcriptomics is to benchmark experimental protocols and computational methods. A solution is to use computational simulators, but existing simulators cannot simultaneously achieve three goals: preserving genes, capturing gene correlations, and generating any nu...

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Detalles Bibliográficos
Autores principales: Sun, Tianyi, Song, Dongyuan, Li, Wei Vivian, Li, Jingyi Jessica
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8147071/
https://www.ncbi.nlm.nih.gov/pubmed/34034771
http://dx.doi.org/10.1186/s13059-021-02367-2
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author Sun, Tianyi
Song, Dongyuan
Li, Wei Vivian
Li, Jingyi Jessica
author_facet Sun, Tianyi
Song, Dongyuan
Li, Wei Vivian
Li, Jingyi Jessica
author_sort Sun, Tianyi
collection PubMed
description A pressing challenge in single-cell transcriptomics is to benchmark experimental protocols and computational methods. A solution is to use computational simulators, but existing simulators cannot simultaneously achieve three goals: preserving genes, capturing gene correlations, and generating any number of cells with varying sequencing depths. To fill this gap, we propose scDesign2, a transparent simulator that achieves all three goals and generates high-fidelity synthetic data for multiple single-cell gene expression count-based technologies. In particular, scDesign2 is advantageous in its transparent use of probabilistic models and its ability to capture gene correlations via copulas. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-021-02367-2).
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spelling pubmed-81470712021-05-25 scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured Sun, Tianyi Song, Dongyuan Li, Wei Vivian Li, Jingyi Jessica Genome Biol Method A pressing challenge in single-cell transcriptomics is to benchmark experimental protocols and computational methods. A solution is to use computational simulators, but existing simulators cannot simultaneously achieve three goals: preserving genes, capturing gene correlations, and generating any number of cells with varying sequencing depths. To fill this gap, we propose scDesign2, a transparent simulator that achieves all three goals and generates high-fidelity synthetic data for multiple single-cell gene expression count-based technologies. In particular, scDesign2 is advantageous in its transparent use of probabilistic models and its ability to capture gene correlations via copulas. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-021-02367-2). BioMed Central 2021-05-25 /pmc/articles/PMC8147071/ /pubmed/34034771 http://dx.doi.org/10.1186/s13059-021-02367-2 Text en © The Author(s) 2021, , corrected publication 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Sun, Tianyi
Song, Dongyuan
Li, Wei Vivian
Li, Jingyi Jessica
scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured
title scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured
title_full scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured
title_fullStr scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured
title_full_unstemmed scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured
title_short scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured
title_sort scdesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8147071/
https://www.ncbi.nlm.nih.gov/pubmed/34034771
http://dx.doi.org/10.1186/s13059-021-02367-2
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