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SkewC : Identifying cells with skewed gene body coverage in single-cell RNA sequencing data

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Titel: SkewC : Identifying cells with skewed gene body coverage in single-cell RNA sequencing data
Autoren: Abugessaisa, Imad, Hasegawa, Akira, Noguchi, Shuhei, Cardon, Melissa, Watanabe, Kazuhide, Takahashi, Masataka, Suzuki, Harukazu, Katayama, Shintaro, Kere, Juha, Kasukawa, Takeya
Weitere Verfasser: Research Programs Unit, STEMM - Stem Cells and Metabolism Research Program, Juha Kere / Principal Investigator, HUS Group
Verlagsinformationen: Elsevier Inc.
Publikationsjahr: 2022
Bestand: Helsingfors Universitet: HELDA – Helsingin yliopiston digitaalinen arkisto
Schlagwörter: EXPRESSION, HETEROGENEITY, PROGRAMS, NOISE, FATE, Biomedicine
Beschreibung: The 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. ; Peer reviewed
Publikationsart: article in journal/newspaper
Dateibeschreibung: application/pdf
Sprache: English
Relation: We greatly appreciate the efforts of Nobuyu Takeda, Teruaki Kitakura, and Akira Furukawa in providing technical support and the IT infrastructure. We are thankful for the English proofreading by Scott Walker. The authors would like to thank Dr Cody Kime from RIKEN Center for Biosystems Dynamics Research for testing SkewC at the early stage of development and for the feedback and discussion. This work was supported by research grants for the RIKEN Center for Life Science Technologies, RIKEN Center for Integrative Medical Sciences, and RIKEN Open Life Science Platform project from MEXT, Japan. SK was supported by Jane and Aatos Erkko Foundation (Finland) . JK was supported in part by Knut and Alice Wallenberg Foundation (KAW2015.0096) (Sweden) , Swedish Research Council, Jane and Aatos Erkko Foundation (Finland) , and Sigrid Juselius Foundation (Finland) . This work was initiated when JK was a Japan Society for the Promotion of Science Fellow (Japan) at RIKEN Center for Integrative Medical Sciences.; http://hdl.handle.net/10138/342576; 000760398900009
Verfügbarkeit: http://hdl.handle.net/10138/342576
Rights: cc_by ; info:eu-repo/semantics/openAccess ; openAccess
Dokumentencode: edsbas.131C4051
Datenbank: BASE
Beschreibung
Abstract:The 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. ; Peer reviewed