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

Genomics, Proteomics & Bioinformatics 2021年第2期19卷 267-281页

作者： Qianhui Huang Yu Liu Yuheng Du lana x.garmire Department of Biostatistics University of MichiganAnn ArborMI 48109USA Department of Computational Medicine and Bioinformatics University of MichiganAnn ArborMI 48105USA

Annotating cell types is a critical step in single-cell RNA sequencing(scRNA-seq)data *** supervised or semi-supervised classification methods have recently emerged to enable automated cell type ***,comprehensive evaluations of these methods are ***,it is not clear whether some classification methods originally designed for analyzing other bulk omics data are adaptable to scRNA-seq *** this study,we evaluated ten cell type annotation methods publicly available as R *** of them are popular methods developed specifically for single-cell research,including Seurat,scmap,SingleR,CHETAH,SingleCellNet,scID,Garnett,and *** 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 *** 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 ***,methods such as Seurat,SingleR,CP,RPC,and SingleCellNet performed well,with Seurat being the best at annotating major cell ***,Seurat,SingleR,CP,and RPC were more robust against ***,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 *** the code and data are available from https://***/qianhuiSenn/scRNA_cell_deconv_benchmark.

关键词： scRNA-seq Cell type Annotation Classification Benchmark

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维普期刊数据库

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Computational Methods for Single-cell Multi-omics Integration and Alignment

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 836-849页

作者： Stefan Stanojevic Yijun Li Aleksandar Ristivojevic lana x.garmire Department of Computational Medicine and Bioinformatics University of MichiganAnn ArborMI 48109USA Department of Biostatistics University of MichiganAnn ArborMI 48109USA Amherst College AmherstMA 01002USA

Recently developed technologies to generate single-cell genomic data have made a revolutionary impact in the field of ***-omics assays offer even greater opportunities to understand cellular states and biological *** problem of integrating different omics data with very different dimensionality and statistical properties remains,however,quite challenging.A growing body of computational tools is being developed for this task,leveraging ideas ranging from machine translation to the theory of networks,and represents another frontier on the interface of biology and data *** goal in this review is to provide a comprehensive,up-to-date survey of computational techniques for the integration of single-cell multi-omics data,while making the concepts behind each algorithm approachable to a non-expert audience.

关键词： Single-cell Multi-omics Machine learning Unsupervised learning Integration

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维普期刊数据库

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Granatumx:A Community-engaging, Modularized, and Flexible Webtool for Single-cell Data Analysis

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Genomics, Proteomics & Bioinformatics 2021年第3期19卷 452-460页

作者： David G.garmire xun Zhu Aravind Mantravadi Qianhui Huang Breck Yunits Yu Liu Thomas Wolfgruber Olivier Poirion Tianying Zhao Cédric Arisdakessian Stefan Stanojevic lana x.garmire Department of Electrical Engineering&Computer Science University of MichiganAnn ArborMI 48109USA Epidemiology Program University of Hawaii Cancer CenterHonoluluHI 96813USA Department of Biostatistics University of MichiganAnn ArborMI 48109USA Department of Computational Medicine and Bioinformatics University of MichiganAnn ArborMI 48105USA Molecular Biosciences and Bioengineering Graduate Program University of Hawaii at ManoaHonoluluHI 96822USA

We present Granatumx,a next-generation software environment for single-cell RNA sequencing(scRNA-seq)data *** is inspired by the interactive webtool *** enables biologists to access the latest scRNA-seq bioinformatics methods in a web-based graphical *** also offers software developers the opportunity to rapidly promote their own tools with others in customizable *** architecture of Granatumx allows for easy inclusion of plugin modules,named Gboxes,which wrap around bioinformatics tools written in various programming languages and on various *** can be run on the cloud or private servers and generate reproducible *** is a community-engaging,flexible,and evolving software ecosystem for scRNA-seq analysis,connecting developers with bench *** is freely accessible at http://***/granatumx/app.

关键词： Single-cell RNA sequencing Analysis Pipeline Webtool Module

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维普期刊数据库

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