Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data.
Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data.
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全面生成、可视化和报告单细胞RNA测序数据的质量控制指标。
DOI：
10.1038/s41467-022-29212-9
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发表时间：
2022-03-30
影响因子：
16.6
通讯作者：
Campbell JD
中科院分区：
文献类型：
--
作者：
Hong R;Koga Y;Bandyadka S;Leshchyk A;Wang Y;Akavoor V;Cao X;Sarfraz I;Wang Z;Alabdullatif S;Jansen F;Yajima M;Johnson WE;Campbell JD
单细胞RNA测序（scRNA-seq）可用于深入了解复杂组织中的细胞异质性。然而，scRNA-seq数据中可能存在各种各样的技术伪影，在进行下游分析之前应该进行评估。虽然已经开发了一些工具来执行单独的质量控制（QC）任务，但它们分散在多个编程环境中的不同包中。在这里，为了简化scRNA-seq数据生成和可视化QC指标的过程，我们在singleCellTK R包中构建了SCTK-QC管道。SCTK-QC工作流程可以从几个单细胞平台和预处理工具中导入数据，包括空液滴检测、标准QC指标生成、双峰预测和环境RNA估计等步骤。它可以在命令行、R控制台、云平台或交互式图形用户界面上运行。总的来说，SCTK-QC流水线简化和标准化了对scRNA-seq数据执行QC的过程。质量控制（QC）是单细胞RNA-seq数据分析的关键步骤。在这里，作者介绍了SCTK-QC管道，它生成并可视化了一套全面的QC指标，以简化检测和去除质量差的细胞和其他工件的过程。
Single-cell RNA sequencing (scRNA-seq) can be used to gain insights into cellular heterogeneity within complex tissues. However, various technical artifacts can be present in scRNA-seq data and should be assessed before performing downstream analyses. While several tools have been developed to perform individual quality control (QC) tasks, they are scattered in different packages across several programming environments. Here, to streamline the process of generating and visualizing QC metrics for scRNA-seq data, we built the SCTK-QC pipeline within the singleCellTK R package. The SCTK-QC workflow can import data from several single-cell platforms and preprocessing tools and includes steps for empty droplet detection, generation of standard QC metrics, prediction of doublets, and estimation of ambient RNA. It can run on the command line, within the R console, on the cloud platform or with an interactive graphical user interface. Overall, the SCTK-QC pipeline streamlines and standardizes the process of performing QC for scRNA-seq data. Quality control (QC) is a crucial step in single-cell RNA-seq data analysis. Here, the authors present the SCTK-QC pipeline which generates and visualizes a comprehensive set of QC metrics to streamline the process of detecting and removing poor quality cells and other artifacts.
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DOI：
10.1073/pnas.1402030111
发表时间：
2014-05-13
影响因子：
11.1
作者：
Streets, Aaron M.;Zhang, Xiannian;Huang, Yanyi
通讯作者：
Huang, Yanyi
影响因子：
9.3
作者：
Wolock, Samuel L.;Lopez, Romain;Klein, Allon M.
通讯作者：
Klein, Allon M.
影响因子：
5.8
作者：
Wang, Liguo;Wang, Shengqin;Li, Wei
通讯作者：
Li, Wei
影响因子：
2.8
作者：
Stirling C;Andrews S;Croft T;Vickers J;Turner P;Robinson A
通讯作者：
Robinson A
影响因子：
48
作者：
Picelli, Simone;Bjorklund, Asa K.;Sandberg, Rickard
通讯作者：
Sandberg, Rickard