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

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          Abstract

          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.

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          Author and article information

          Contributors
          Journal
          Genomics Proteomics Bioinformatics
          Genomics Proteomics Bioinformatics
          Genomics, Proteomics & Bioinformatics
          Elsevier
          1672-0229
          2210-3244
          24 December 2020
          April 2021
          24 December 2020
          : 19
          : 2
          : 267-281
          Affiliations
          [1 ]Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
          [2 ]Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48105, USA
          Author notes
          [* ]Corresponding author. lgarmire@ 123456med.umich.edu
          Article
          S1672-0229(20)30144-3
          10.1016/j.gpb.2020.07.004
          8602772
          33359678
          9c91880f-d35c-4197-a97c-15d9132552f7
          © 2021 Beijing Institute of Genomics

          This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

          History
          : 30 October 2019
          : 16 July 2020
          : 27 October 2020
          Categories
          Original Research

          scrna-seq,cell type,annotation,classification,benchmark
          scrna-seq, cell type, annotation, classification, benchmark

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