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A robust and accurate single-cell data trajectory inference method using ensemble pseudotime

BACKGROUND: The advance in single-cell RNA sequencing technology has enhanced the analysis of cell development by profiling heterogeneous cells in individual cell resolution. In recent years, many trajectory inference methods have been developed. They have focused on using the graph method to infer...

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Autores principales: Zhang, Yifan, Tran, Duc, Nguyen, Tin, Dascalu, Sergiu M., Harris, Frederick C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9942315/
https://www.ncbi.nlm.nih.gov/pubmed/36803767
http://dx.doi.org/10.1186/s12859-023-05179-2
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author Zhang, Yifan
Tran, Duc
Nguyen, Tin
Dascalu, Sergiu M.
Harris, Frederick C.
author_facet Zhang, Yifan
Tran, Duc
Nguyen, Tin
Dascalu, Sergiu M.
Harris, Frederick C.
author_sort Zhang, Yifan
collection PubMed
description BACKGROUND: The advance in single-cell RNA sequencing technology has enhanced the analysis of cell development by profiling heterogeneous cells in individual cell resolution. In recent years, many trajectory inference methods have been developed. They have focused on using the graph method to infer the trajectory using single-cell data, and then calculate the geodesic distance as the pseudotime. However, these methods are vulnerable to errors caused by the inferred trajectory. Therefore, the calculated pseudotime suffers from such errors. RESULTS: We proposed a novel framework for trajectory inference called the single-cell data Trajectory inference method using Ensemble Pseudotime inference (scTEP). scTEP utilizes multiple clustering results to infer robust pseudotime and then uses the pseudotime to fine-tune the learned trajectory. We evaluated the scTEP using 41 real scRNA-seq data sets, all of which had the ground truth development trajectory. We compared the scTEP with state-of-the-art methods using the aforementioned data sets. Experiments on real linear and non-linear data sets demonstrate that our scTEP performed superior on more data sets than any other method. The scTEP also achieved a higher average and lower variance on most metrics than other state-of-the-art methods. In terms of trajectory inference capacity, the scTEP outperforms those methods. In addition, the scTEP is more robust to the unavoidable errors resulting from clustering and dimension reduction. CONCLUSION: The scTEP demonstrates that utilizing multiple clustering results for the pseudotime inference procedure enhances its robustness. Furthermore, robust pseudotime strengthens the accuracy of trajectory inference, which is the most crucial component in the pipeline. scTEP is available at https://cran.r-project.org/package=scTEP.
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spelling pubmed-99423152023-02-22 A robust and accurate single-cell data trajectory inference method using ensemble pseudotime Zhang, Yifan Tran, Duc Nguyen, Tin Dascalu, Sergiu M. Harris, Frederick C. BMC Bioinformatics Research BACKGROUND: The advance in single-cell RNA sequencing technology has enhanced the analysis of cell development by profiling heterogeneous cells in individual cell resolution. In recent years, many trajectory inference methods have been developed. They have focused on using the graph method to infer the trajectory using single-cell data, and then calculate the geodesic distance as the pseudotime. However, these methods are vulnerable to errors caused by the inferred trajectory. Therefore, the calculated pseudotime suffers from such errors. RESULTS: We proposed a novel framework for trajectory inference called the single-cell data Trajectory inference method using Ensemble Pseudotime inference (scTEP). scTEP utilizes multiple clustering results to infer robust pseudotime and then uses the pseudotime to fine-tune the learned trajectory. We evaluated the scTEP using 41 real scRNA-seq data sets, all of which had the ground truth development trajectory. We compared the scTEP with state-of-the-art methods using the aforementioned data sets. Experiments on real linear and non-linear data sets demonstrate that our scTEP performed superior on more data sets than any other method. The scTEP also achieved a higher average and lower variance on most metrics than other state-of-the-art methods. In terms of trajectory inference capacity, the scTEP outperforms those methods. In addition, the scTEP is more robust to the unavoidable errors resulting from clustering and dimension reduction. CONCLUSION: The scTEP demonstrates that utilizing multiple clustering results for the pseudotime inference procedure enhances its robustness. Furthermore, robust pseudotime strengthens the accuracy of trajectory inference, which is the most crucial component in the pipeline. scTEP is available at https://cran.r-project.org/package=scTEP. BioMed Central 2023-02-20 /pmc/articles/PMC9942315/ /pubmed/36803767 http://dx.doi.org/10.1186/s12859-023-05179-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Research
Zhang, Yifan
Tran, Duc
Nguyen, Tin
Dascalu, Sergiu M.
Harris, Frederick C.
A robust and accurate single-cell data trajectory inference method using ensemble pseudotime
title A robust and accurate single-cell data trajectory inference method using ensemble pseudotime
title_full A robust and accurate single-cell data trajectory inference method using ensemble pseudotime
title_fullStr A robust and accurate single-cell data trajectory inference method using ensemble pseudotime
title_full_unstemmed A robust and accurate single-cell data trajectory inference method using ensemble pseudotime
title_short A robust and accurate single-cell data trajectory inference method using ensemble pseudotime
title_sort robust and accurate single-cell data trajectory inference method using ensemble pseudotime
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9942315/
https://www.ncbi.nlm.nih.gov/pubmed/36803767
http://dx.doi.org/10.1186/s12859-023-05179-2
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