SkewC: Identifying cells with skewed gene body coverage in single-cell RNA sequencing data
Summary: 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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-02-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004222000475 |
| _version_ | 1819282878305927168 |
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| author | Imad Abugessaisa Akira Hasegawa Shuhei Noguchi Melissa Cardon Kazuhide Watanabe Masataka Takahashi Harukazu Suzuki Shintaro Katayama Juha Kere Takeya Kasukawa |
| author_facet | Imad Abugessaisa Akira Hasegawa Shuhei Noguchi Melissa Cardon Kazuhide Watanabe Masataka Takahashi Harukazu Suzuki Shintaro Katayama Juha Kere Takeya Kasukawa |
| author_sort | Imad Abugessaisa |
| collection | DOAJ |
| description | Summary: 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. |
| first_indexed | 2024-12-24T01:22:35Z |
| format | Article |
| id | doaj.art-46664ab7721b452a85c7254b5d303786 |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-12-24T01:22:35Z |
| publishDate | 2022-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-46664ab7721b452a85c7254b5d3037862022-12-21T17:22:36ZengElsevieriScience2589-00422022-02-01252103777SkewC: Identifying cells with skewed gene body coverage in single-cell RNA sequencing dataImad Abugessaisa0Akira Hasegawa1Shuhei Noguchi2Melissa Cardon3Kazuhide Watanabe4Masataka Takahashi5Harukazu Suzuki6Shintaro Katayama7Juha Kere8Takeya Kasukawa9Laboratory for Large-Scale Biomedical Data Technology, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, JapanLaboratory for Large-Scale Biomedical Data Technology, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, JapanLaboratory for Large-Scale Biomedical Data Technology, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, JapanLaboratory for Large-Scale Biomedical Data Technology, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, JapanLaboratory for Cellular Function Conversion Technology, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, JapanLaboratory for Cellular Function Conversion Technology, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, JapanLaboratory for Cellular Function Conversion Technology, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, JapanFolkhälsan Research Center, Topeliuksenkatu 20, 00250 Helsinki, Finland; Department of Biosciences and Nutrition, Karolinska Institutet, 141 83 Huddinge, Sweden; Stem Cells and Metabolism Research Program, University of Helsinki, P.O. Box 4 (Yliopistonkatu 3), Helsinki, FinlandFolkhälsan Research Center, Topeliuksenkatu 20, 00250 Helsinki, Finland; Department of Biosciences and Nutrition, Karolinska Institutet, 141 83 Huddinge, Sweden; Stem Cells and Metabolism Research Program, University of Helsinki, P.O. Box 4 (Yliopistonkatu 3), Helsinki, Finland; Corresponding authorLaboratory for Large-Scale Biomedical Data Technology, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan; Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan; Corresponding authorSummary: 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.http://www.sciencedirect.com/science/article/pii/S2589004222000475Biological sciencesCell biologyBiocomputational methodBiological sciences research methodologies |
| spellingShingle | Imad Abugessaisa Akira Hasegawa Shuhei Noguchi Melissa Cardon Kazuhide Watanabe Masataka Takahashi Harukazu Suzuki Shintaro Katayama Juha Kere Takeya Kasukawa SkewC: Identifying cells with skewed gene body coverage in single-cell RNA sequencing data iScience Biological sciences Cell biology Biocomputational method Biological sciences research methodologies |
| title | SkewC: Identifying cells with skewed gene body coverage in single-cell RNA sequencing data |
| title_full | SkewC: Identifying cells with skewed gene body coverage in single-cell RNA sequencing data |
| title_fullStr | SkewC: Identifying cells with skewed gene body coverage in single-cell RNA sequencing data |
| title_full_unstemmed | SkewC: Identifying cells with skewed gene body coverage in single-cell RNA sequencing data |
| title_short | SkewC: Identifying cells with skewed gene body coverage in single-cell RNA sequencing data |
| title_sort | skewc identifying cells with skewed gene body coverage in single cell rna sequencing data |
| topic | Biological sciences Cell biology Biocomputational method Biological sciences research methodologies |
| url | http://www.sciencedirect.com/science/article/pii/S2589004222000475 |
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