文章基本信息

标题：SkewC: Identifying cells with skewed gene body coverage in single-cell RNA sequencing data
本地全文：下载
作者：Imad Abugessaisa ; Akira Hasegawa ; Shuhei Noguchi 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2022
卷号：25
期号：2
页码：1-20
DOI：10.1016/j.isci.2022.103777
语种：English
出版社：Elsevier
摘要：SummaryThe analysis and interpretation of single-cell RNA sequencing (scRNA-seq) experiments are compromised by the presence of poor-quality cells. For meaningful analyses, such poor-quality cells should be excluded as they introduce noise in the data. We introduce SkewC, a quality-assessment tool, to identify skewed cells in scRNA-seq experiments. The tool’s methodology is based on the assessment of gene coverage for each cell, and its skewness as a quality measure; the gene body coverage is a unique characteristic for each protocol, and different protocols yield highly different coverage profiles. This tool is designed to avoid misclustering or false clusters by identifying, isolating, and removing cells with skewed gene body coverage profiles. SkewC is capable of processing any type of scRNA-seq dataset, regardless of the protocol. We envision SkewC as a distinctive QC method to be incorporated into scRNA-seq QC processing to preclude the possibility of scRNA-seq data misinterpretation.Graphical abstractDisplay OmittedHighlights•We developed a quality assessment method applicable to single-cell RNA-Seq data•SkewC relies on the use of gene coverage profile of cells as a quality measure•SkewC reveals two classes of cells: typical cells and skewed cells•Typical with prototypical coverage profiles, and skewed with skewed profilesBiological sciences; Cell biology; Biocomputational method; Biological sciences research methodologies;