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

Author

Listed:
  • Rui Hong

    (Bioinformatics Program, Boston University
    Section of Computational Biomedicine, Boston University School of Medicine)

  • Yusuke Koga

    (Bioinformatics Program, Boston University
    Section of Computational Biomedicine, Boston University School of Medicine)

  • Shruthi Bandyadka

    (Bioinformatics Program, Boston University
    Boston University)

  • Anastasia Leshchyk

    (Bioinformatics Program, Boston University
    Section of Computational Biomedicine, Boston University School of Medicine)

  • Yichen Wang

    (Section of Computational Biomedicine, Boston University School of Medicine)

  • Vidya Akavoor

    (Section of Computational Biomedicine, Boston University School of Medicine
    Software & Application Innovation Lab, Rafik B. Hariri Institute for Computing and Computational Science and Engineering)

  • 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
    Section of Computational Biomedicine, Boston University School of Medicine)

  • 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
    Section of Computational Biomedicine, Boston University School of Medicine)

  • Joshua D. Campbell

    (Bioinformatics Program, Boston University
    Section of Computational Biomedicine, Boston University School of Medicine)

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.

Suggested Citation

  • 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, 2022. "Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29212-9
    DOI: 10.1038/s41467-022-29212-9
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    References listed on IDEAS

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    1. Orit Rozenblatt-Rosen & Michael J. T. Stubbington & Aviv Regev & Sarah A. Teichmann, 2017. "The Human Cell Atlas: from vision to reality," Nature, Nature, vol. 550(7677), pages 451-453, October.
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