ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion

Abstract Background With single-cell RNA sequencing (scRNA-seq) methods, gene expression patterns at the single-cell resolution can be revealed. But as impacted by current technical defects, dropout events in scRNA-seq lead to missing data and noise in the gene-cell expression matrix and adversely a...

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Autores principales:	Xiutao Pan, Zhong Li, Shengwei Qin, Minzhe Yu, Hang Hu
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Single-cell RNA-seq Data imputation Low-rank tensor Biotechnology TP248.13-248.65 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/a227ddcbfda84bb3beae8230029210ad
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spelling	oai:doaj.org-article:a227ddcbfda84bb3beae8230029210ad2021-12-05T12:17:17ZScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion10.1186/s12864-021-08101-31471-2164https://doaj.org/article/a227ddcbfda84bb3beae8230029210ad2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12864-021-08101-3https://doaj.org/toc/1471-2164Abstract Background With single-cell RNA sequencing (scRNA-seq) methods, gene expression patterns at the single-cell resolution can be revealed. But as impacted by current technical defects, dropout events in scRNA-seq lead to missing data and noise in the gene-cell expression matrix and adversely affect downstream analyses. Accordingly, the true gene expression level should be recovered before the downstream analysis is carried out. Results In this paper, a novel low-rank tensor completion-based method, termed as scLRTC, is proposed to impute the dropout entries of a given scRNA-seq expression. It initially exploits the similarity of single cells to build a third-order low-rank tensor and employs the tensor decomposition to denoise the data. Subsequently, it reconstructs the cell expression by adopting the low-rank tensor completion algorithm, which can restore the gene-to-gene and cell-to-cell correlations. ScLRTC is compared with other state-of-the-art methods on simulated datasets and real scRNA-seq datasets with different data sizes. Specific to simulated datasets, scLRTC outperforms other methods in imputing the dropouts closest to the original expression values, which is assessed by both the sum of squared error (SSE) and Pearson correlation coefficient (PCC). In terms of real datasets, scLRTC achieves the most accurate cell classification results in spite of the choice of different clustering methods (e.g., SC3 or t-SNE followed by K-means), which is evaluated by using adjusted rand index (ARI) and normalized mutual information (NMI). Lastly, scLRTC is demonstrated to be also effective in cell visualization and in inferring cell lineage trajectories. Conclusions a novel low-rank tensor completion-based method scLRTC gave imputation results better than the state-of-the-art tools. Source code of scLRTC can be accessed at https://github.com/jianghuaijie/scLRTC .Xiutao PanZhong LiShengwei QinMinzhe YuHang HuBMCarticleSingle-cell RNA-seqData imputationLow-rank tensorBiotechnologyTP248.13-248.65GeneticsQH426-470ENBMC Genomics, Vol 22, Iss 1, Pp 1-19 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Single-cell RNA-seq Data imputation Low-rank tensor Biotechnology TP248.13-248.65 Genetics QH426-470
spellingShingle	Single-cell RNA-seq Data imputation Low-rank tensor Biotechnology TP248.13-248.65 Genetics QH426-470 Xiutao Pan Zhong Li Shengwei Qin Minzhe Yu Hang Hu ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion
description	Abstract Background With single-cell RNA sequencing (scRNA-seq) methods, gene expression patterns at the single-cell resolution can be revealed. But as impacted by current technical defects, dropout events in scRNA-seq lead to missing data and noise in the gene-cell expression matrix and adversely affect downstream analyses. Accordingly, the true gene expression level should be recovered before the downstream analysis is carried out. Results In this paper, a novel low-rank tensor completion-based method, termed as scLRTC, is proposed to impute the dropout entries of a given scRNA-seq expression. It initially exploits the similarity of single cells to build a third-order low-rank tensor and employs the tensor decomposition to denoise the data. Subsequently, it reconstructs the cell expression by adopting the low-rank tensor completion algorithm, which can restore the gene-to-gene and cell-to-cell correlations. ScLRTC is compared with other state-of-the-art methods on simulated datasets and real scRNA-seq datasets with different data sizes. Specific to simulated datasets, scLRTC outperforms other methods in imputing the dropouts closest to the original expression values, which is assessed by both the sum of squared error (SSE) and Pearson correlation coefficient (PCC). In terms of real datasets, scLRTC achieves the most accurate cell classification results in spite of the choice of different clustering methods (e.g., SC3 or t-SNE followed by K-means), which is evaluated by using adjusted rand index (ARI) and normalized mutual information (NMI). Lastly, scLRTC is demonstrated to be also effective in cell visualization and in inferring cell lineage trajectories. Conclusions a novel low-rank tensor completion-based method scLRTC gave imputation results better than the state-of-the-art tools. Source code of scLRTC can be accessed at https://github.com/jianghuaijie/scLRTC .
format	article
author	Xiutao Pan Zhong Li Shengwei Qin Minzhe Yu Hang Hu
author_facet	Xiutao Pan Zhong Li Shengwei Qin Minzhe Yu Hang Hu
author_sort	Xiutao Pan
title	ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion
title_short	ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion
title_full	ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion
title_fullStr	ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion
title_full_unstemmed	ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion
title_sort	sclrtc: imputation for single-cell rna-seq data via low-rank tensor completion
publisher	BMC
publishDate	2021
url	https://doaj.org/article/a227ddcbfda84bb3beae8230029210ad
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ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion

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