Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data

Rui Hong, Yusuke Koga, Shruthi Bandyadka, Anastasia Leshchyk, Yichen Wang, Vidya Akavoor, Xinyun Cao, Irzam Sarfraz, Zhe Wang, Salam Alabdullatif, Frederick Jansen, Masanao Yajima, W. Evan Johnson and Joshua D. Campbell ()
Additional contact information
Rui Hong: Bioinformatics Program, Boston University
Yusuke Koga: Bioinformatics Program, Boston University
Shruthi Bandyadka: Bioinformatics Program, Boston University
Anastasia Leshchyk: Bioinformatics Program, Boston University
Yichen Wang: Section of Computational Biomedicine, Boston University School of Medicine
Vidya Akavoor: Section of Computational Biomedicine, Boston University School of Medicine
Xinyun Cao: Software & Application Innovation Lab, Rafik B. Hariri Institute for Computing and Computational Science and Engineering
Irzam Sarfraz: Section of Computational Biomedicine, Boston University School of Medicine
Zhe Wang: Bioinformatics Program, Boston University
Salam Alabdullatif: Section of Computational Biomedicine, Boston University School of Medicine
Frederick Jansen: Software & Application Innovation Lab, Rafik B. Hariri Institute for Computing and Computational Science and Engineering
Masanao Yajima: Boston University
W. Evan Johnson: Bioinformatics Program, Boston University
Joshua D. Campbell: Bioinformatics Program, Boston University

Nature Communications, 2022, vol. 13, issue 1, 1-9

Abstract: Abstract 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.

Date: 2022
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DOI: 10.1038/s41467-022-29212-9

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