Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data

Annotating cell types is a critical step in single-cell RNA sequencing (scRNA-seq) data analysis. Some supervised or semi-supervised classification methods have recently emerged to enable automated cell type identification. However, comprehensive evaluations of these methods are lacking. Moreover, i...

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Autores principales:	Qianhui Huang, Yu Liu, Yuheng Du, Lana X. Garmire
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	scRNA-seq Cell type Annotation Classification Benchmark Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/5b5482447ab840af91ef7a274bd04308
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spelling	oai:doaj.org-article:5b5482447ab840af91ef7a274bd043082021-11-16T04:09:20ZEvaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data1672-022910.1016/j.gpb.2020.07.004https://doaj.org/article/5b5482447ab840af91ef7a274bd043082021-04-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1672022920301443https://doaj.org/toc/1672-0229Annotating cell types is a critical step in single-cell RNA sequencing (scRNA-seq) data analysis. Some supervised or semi-supervised classification methods have recently emerged to enable automated cell type identification. However, comprehensive evaluations of these methods are lacking. Moreover, it is not clear whether some classification methods originally designed for analyzing other bulk omics data are adaptable to scRNA-seq analysis. In this study, we evaluated ten cell type annotation methods publicly available as R packages. Eight of them are popular methods developed specifically for single-cell research, including Seurat, scmap, SingleR, CHETAH, SingleCellNet, scID, Garnett, and SCINA. The other two methods were repurposed from deconvoluting DNA methylation data, i.e., linear constrained projection (CP) and robust partial correlations (RPC). We conducted systematic comparisons on a wide variety of public scRNA-seq datasets as well as simulation data. We assessed the accuracy through intra-dataset and inter-dataset predictions; the robustness over practical challenges such as gene filtering, high similarity among cell types, and increased cell type classes; as well as the detection of rare and unknown cell types. Overall, methods such as Seurat, SingleR, CP, RPC, and SingleCellNet performed well, with Seurat being the best at annotating major cell types. Additionally, Seurat, SingleR, CP, and RPC were more robust against downsampling. However, Seurat did have a major drawback at predicting rare cell populations, and it was suboptimal at differentiating cell types highly similar to each other, compared to SingleR and RPC. All the code and data are available from https://github.com/qianhuiSenn/scRNA_cell_deconv_benchmark.Qianhui HuangYu LiuYuheng DuLana X. GarmireElsevierarticlescRNA-seqCell typeAnnotationClassificationBenchmarkBiology (General)QH301-705.5ENGenomics, Proteomics & Bioinformatics, Vol 19, Iss 2, Pp 267-281 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	scRNA-seq Cell type Annotation Classification Benchmark Biology (General) QH301-705.5
spellingShingle	scRNA-seq Cell type Annotation Classification Benchmark Biology (General) QH301-705.5 Qianhui Huang Yu Liu Yuheng Du Lana X. Garmire Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data
description	Annotating cell types is a critical step in single-cell RNA sequencing (scRNA-seq) data analysis. Some supervised or semi-supervised classification methods have recently emerged to enable automated cell type identification. However, comprehensive evaluations of these methods are lacking. Moreover, it is not clear whether some classification methods originally designed for analyzing other bulk omics data are adaptable to scRNA-seq analysis. In this study, we evaluated ten cell type annotation methods publicly available as R packages. Eight of them are popular methods developed specifically for single-cell research, including Seurat, scmap, SingleR, CHETAH, SingleCellNet, scID, Garnett, and SCINA. The other two methods were repurposed from deconvoluting DNA methylation data, i.e., linear constrained projection (CP) and robust partial correlations (RPC). We conducted systematic comparisons on a wide variety of public scRNA-seq datasets as well as simulation data. We assessed the accuracy through intra-dataset and inter-dataset predictions; the robustness over practical challenges such as gene filtering, high similarity among cell types, and increased cell type classes; as well as the detection of rare and unknown cell types. Overall, methods such as Seurat, SingleR, CP, RPC, and SingleCellNet performed well, with Seurat being the best at annotating major cell types. Additionally, Seurat, SingleR, CP, and RPC were more robust against downsampling. However, Seurat did have a major drawback at predicting rare cell populations, and it was suboptimal at differentiating cell types highly similar to each other, compared to SingleR and RPC. All the code and data are available from https://github.com/qianhuiSenn/scRNA_cell_deconv_benchmark.
format	article
author	Qianhui Huang Yu Liu Yuheng Du Lana X. Garmire
author_facet	Qianhui Huang Yu Liu Yuheng Du Lana X. Garmire
author_sort	Qianhui Huang
title	Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data
title_short	Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data
title_full	Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data
title_fullStr	Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data
title_full_unstemmed	Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data
title_sort	evaluation of cell type annotation r packages on single-cell rna-seq data
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/5b5482447ab840af91ef7a274bd04308
work_keys_str_mv	AT qianhuihuang evaluationofcelltypeannotationrpackagesonsinglecellrnaseqdata AT yuliu evaluationofcelltypeannotationrpackagesonsinglecellrnaseqdata AT yuhengdu evaluationofcelltypeannotationrpackagesonsinglecellrnaseqdata AT lanaxgarmire evaluationofcelltypeannotationrpackagesonsinglecellrnaseqdata
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Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data

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