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。
Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data.
机构信息
Bioinformatics Program, Boston University, Boston, MA, USA.
Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.
出版信息
Nat Commun. 2022 Mar 30;13(1):1688. doi: 10.1038/s41467-022-29212-9.
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.
单细胞 RNA 测序 (scRNA-seq) 可用于深入了解复杂组织内的细胞异质性。然而，scRNA-seq 数据中可能存在各种技术伪影，在进行下游分析之前应评估这些伪影。虽然已经开发了几种工具来执行单个质量控制 (QC) 任务，但它们分散在不同编程环境的多个软件包中。在这里，为了简化生成和可视化 scRNA-seq 数据 QC 指标的过程，我们在 singleCellTK R 包中构建了 SCTK-QC 管道。SCTK-QC 工作流程可以从多个单细胞平台和预处理工具导入数据，并包括空液滴检测、标准 QC 指标生成、二聚体预测和环境 RNA 估计步骤。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。总体而言，SCTK-QC 管道简化并标准化了 scRNA-seq 数据的 QC 执行过程。
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