Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications
Abstract Dropout events in single-cell RNA sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero i...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2018-02-01
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| Series: | Genome Biology |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13059-018-1406-4 |
| _version_ | 1818272708658462720 |
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| author | Koen Van den Berge Fanny Perraudeau Charlotte Soneson Michael I. Love Davide Risso Jean-Philippe Vert Mark D. Robinson Sandrine Dudoit Lieven Clement |
| author_facet | Koen Van den Berge Fanny Perraudeau Charlotte Soneson Michael I. Love Davide Risso Jean-Philippe Vert Mark D. Robinson Sandrine Dudoit Lieven Clement |
| author_sort | Koen Van den Berge |
| collection | DOAJ |
| description | Abstract Dropout events in single-cell RNA sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene- and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq. |
| first_indexed | 2024-12-12T21:46:22Z |
| format | Article |
| id | doaj.art-1fb890a1e7054cc1bb3da7acb3f98b6c |
| institution | Directory Open Access Journal |
| issn | 1474-760X |
| language | English |
| last_indexed | 2024-12-12T21:46:22Z |
| publishDate | 2018-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj.art-1fb890a1e7054cc1bb3da7acb3f98b6c2022-12-22T00:10:55ZengBMCGenome Biology1474-760X2018-02-0119111710.1186/s13059-018-1406-4Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applicationsKoen Van den Berge0Fanny Perraudeau1Charlotte Soneson2Michael I. Love3Davide Risso4Jean-Philippe Vert5Mark D. Robinson6Sandrine Dudoit7Lieven Clement8Department of Applied Mathematics, Computer Science and Statistics, Ghent UniversityDivision of Biostatistics, School of Public Health, University of CaliforniaInstitute of Molecular Life Sciences, University of ZurichDepartment of Biostatistics and Genetics, The University of North Carolina at Chapel HillDivision of Biostatistics and Epidemiology, Department of Healthcare Policy and Research, Weill Cornell MedicineMINES ParisTech, PSL Research University, CBIO-Centre for Computational BiologyInstitute of Molecular Life Sciences, University of ZurichDivision of Biostatistics, School of Public Health, University of CaliforniaDepartment of Applied Mathematics, Computer Science and Statistics, Ghent UniversityAbstract Dropout events in single-cell RNA sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene- and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq.http://link.springer.com/article/10.1186/s13059-018-1406-4Single-cell RNA sequencingDifferential expressionZero-inflated negative binomialWeights |
| spellingShingle | Koen Van den Berge Fanny Perraudeau Charlotte Soneson Michael I. Love Davide Risso Jean-Philippe Vert Mark D. Robinson Sandrine Dudoit Lieven Clement Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications Genome Biology Single-cell RNA sequencing Differential expression Zero-inflated negative binomial Weights |
| title | Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications |
| title_full | Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications |
| title_fullStr | Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications |
| title_full_unstemmed | Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications |
| title_short | Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications |
| title_sort | observation weights unlock bulk rna seq tools for zero inflation and single cell applications |
| topic | Single-cell RNA sequencing Differential expression Zero-inflated negative binomial Weights |
| url | http://link.springer.com/article/10.1186/s13059-018-1406-4 |
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