Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data
Date
2022-03-30
Authors
Hong, Rui
Koga, Yusuke
Bandyadka, Shruthi
Leshchyk, Anastasia
Wang, Yichen
Akavoor, Vidya
Cao, Xinyun
Sarfraz, Irzam
Wang, Zhe
Alabdullatif, Salam
Version
Published version
OA Version
Citation
R. Hong, Y. Koga, S. Bandyadka, A. Leshchyk, Y. Wang, V. Akavoor, X. Cao, I. Sarfraz, Z. Wang, S. Alabdullatif, F. Jansen, M. Yajima, W.E. Johnson, J.D. Campbell. 2022. "Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data." Nature Communications, Volume 13, Issue 1, pp.1688-. https://doi.org/10.1038/s41467-022-29212-9
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.
Description
License
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