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

Authors :
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
Joshua D. Campbell
Source :
Nature Communications, Vol 13, Iss 1, Pp 1-9 (2022)
Publication Year :
2022
Publisher :
Nature Portfolio, 2022.

Abstract

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.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
13
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.3b9c0a0a78f74f97957e4f3059d88d39
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-022-29212-9