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G2S3: A gene graph-based imputation method for single-cell RNA sequencing data
Single-cell RNA sequencing technology provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses in single-cell transcriptomic studies. We propose a new method, G2S3, that i...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8189489/ https://www.ncbi.nlm.nih.gov/pubmed/34003861 http://dx.doi.org/10.1371/journal.pcbi.1009029 |
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author | Wu, Weimiao Liu, Yunqing Dai, Qile Yan, Xiting Wang, Zuoheng |
author_facet | Wu, Weimiao Liu, Yunqing Dai, Qile Yan, Xiting Wang, Zuoheng |
author_sort | Wu, Weimiao |
collection | PubMed |
description | Single-cell RNA sequencing technology provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses in single-cell transcriptomic studies. We propose a new method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and ten existing imputation methods to eight single-cell transcriptomic datasets and compared their performance. Our results demonstrated that G2S3 has superior overall performance in recovering gene expression, identifying cell subtypes, reconstructing cell trajectories, identifying differentially expressed genes, and recovering gene regulatory and correlation relationships. Moreover, G2S3 is computationally efficient for imputation in large-scale single-cell transcriptomic datasets. |
format | Online Article Text |
id | pubmed-8189489 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-81894892021-06-16 G2S3: A gene graph-based imputation method for single-cell RNA sequencing data Wu, Weimiao Liu, Yunqing Dai, Qile Yan, Xiting Wang, Zuoheng PLoS Comput Biol Research Article Single-cell RNA sequencing technology provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses in single-cell transcriptomic studies. We propose a new method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and ten existing imputation methods to eight single-cell transcriptomic datasets and compared their performance. Our results demonstrated that G2S3 has superior overall performance in recovering gene expression, identifying cell subtypes, reconstructing cell trajectories, identifying differentially expressed genes, and recovering gene regulatory and correlation relationships. Moreover, G2S3 is computationally efficient for imputation in large-scale single-cell transcriptomic datasets. Public Library of Science 2021-05-18 /pmc/articles/PMC8189489/ /pubmed/34003861 http://dx.doi.org/10.1371/journal.pcbi.1009029 Text en © 2021 Wu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wu, Weimiao Liu, Yunqing Dai, Qile Yan, Xiting Wang, Zuoheng G2S3: A gene graph-based imputation method for single-cell RNA sequencing data |
title | G2S3: A gene graph-based imputation method for single-cell RNA sequencing data |
title_full | G2S3: A gene graph-based imputation method for single-cell RNA sequencing data |
title_fullStr | G2S3: A gene graph-based imputation method for single-cell RNA sequencing data |
title_full_unstemmed | G2S3: A gene graph-based imputation method for single-cell RNA sequencing data |
title_short | G2S3: A gene graph-based imputation method for single-cell RNA sequencing data |
title_sort | g2s3: a gene graph-based imputation method for single-cell rna sequencing data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8189489/ https://www.ncbi.nlm.nih.gov/pubmed/34003861 http://dx.doi.org/10.1371/journal.pcbi.1009029 |
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