Rscreenorm: normalization of CRISPR and siRNA screen data for more reproducible hit selection
Abstract Background Reproducibility of hits from independent CRISPR or siRNA screens is poor. This is partly due to data normalization primarily addressing technical variability within independent screens, and not the technical differences between them. Results We present “rscreenorm”, a method that...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2018-08-01
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| Series: | BMC Bioinformatics |
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| Online Access: | http://link.springer.com/article/10.1186/s12859-018-2306-z |
| _version_ | 1819106568112701440 |
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| author | Costa Bachas Jasmina Hodzic Johannes C. van der Mijn Chantal Stoepker Henk M. W. Verheul Rob M. F. Wolthuis Emanuela Felley-Bosco Wessel N. van Wieringen Victor W. van Beusechem Ruud H. Brakenhoff Renée X. de Menezes |
| author_facet | Costa Bachas Jasmina Hodzic Johannes C. van der Mijn Chantal Stoepker Henk M. W. Verheul Rob M. F. Wolthuis Emanuela Felley-Bosco Wessel N. van Wieringen Victor W. van Beusechem Ruud H. Brakenhoff Renée X. de Menezes |
| author_sort | Costa Bachas |
| collection | DOAJ |
| description | Abstract Background Reproducibility of hits from independent CRISPR or siRNA screens is poor. This is partly due to data normalization primarily addressing technical variability within independent screens, and not the technical differences between them. Results We present “rscreenorm”, a method that standardizes the functional data ranges between screens using assay controls, and subsequently performs a piecewise-linear normalization to make data distributions across all screens comparable. In simulation studies, rscreenorm reduces false positives. Using two multiple-cell lines siRNA screens, rscreenorm increased reproducibility between 27 and 62% for hits, and up to 5-fold for non-hits. Using publicly available CRISPR-Cas screen data, application of commonly used median centering yields merely 34% of overlapping hits, in contrast with rscreenorm yielding 84% of overlapping hits. Furthermore, rscreenorm yielded at most 8% discordant results, whilst median-centering yielded as much as 55%. Conclusions Rscreenorm yields more consistent results and keeps false positive rates under control, improving reproducibility of genetic screens data analysis from multiple cell lines. |
| first_indexed | 2024-12-22T02:40:12Z |
| format | Article |
| id | doaj.art-826f3204ee2b48118fd68b46cacf334f |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-22T02:40:12Z |
| publishDate | 2018-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-826f3204ee2b48118fd68b46cacf334f2022-12-21T18:41:40ZengBMCBMC Bioinformatics1471-21052018-08-0119111210.1186/s12859-018-2306-zRscreenorm: normalization of CRISPR and siRNA screen data for more reproducible hit selectionCosta Bachas0Jasmina Hodzic1Johannes C. van der Mijn2Chantal Stoepker3Henk M. W. Verheul4Rob M. F. Wolthuis5Emanuela Felley-Bosco6Wessel N. van Wieringen7Victor W. van Beusechem8Ruud H. Brakenhoff9Renée X. de Menezes10Department of Otolaryngology - Head and Neck Surgery, Amsterdam UMC, Vrije Universiteit AmsterdamDepartment of Medical Oncology, Amsterdam UMC, Vrije Universiteit AmsterdamDepartment of Medical Oncology, Amsterdam UMC, Vrije Universiteit AmsterdamDivision of Tumor Biology and Immunology, Netherlands Cancer InstituteDepartment of Medical Oncology, Amsterdam UMC, Vrije Universiteit AmsterdamSection of Oncogenetics, Department of Clinical Genetics, Amsterdam UMC, Vrije Universiteit AmsterdamLaboratory of Molecular Oncology, University Hospital ZürichDepartment of Epidemiology and Biostatistics, Amsterdam UMC, Vrije Universiteit AmsterdamDepartment of Medical Oncology, Amsterdam UMC, Vrije Universiteit AmsterdamDepartment of Otolaryngology - Head and Neck Surgery, Amsterdam UMC, Vrije Universiteit AmsterdamDepartment of Epidemiology and Biostatistics, Amsterdam UMC, Vrije Universiteit AmsterdamAbstract Background Reproducibility of hits from independent CRISPR or siRNA screens is poor. This is partly due to data normalization primarily addressing technical variability within independent screens, and not the technical differences between them. Results We present “rscreenorm”, a method that standardizes the functional data ranges between screens using assay controls, and subsequently performs a piecewise-linear normalization to make data distributions across all screens comparable. In simulation studies, rscreenorm reduces false positives. Using two multiple-cell lines siRNA screens, rscreenorm increased reproducibility between 27 and 62% for hits, and up to 5-fold for non-hits. Using publicly available CRISPR-Cas screen data, application of commonly used median centering yields merely 34% of overlapping hits, in contrast with rscreenorm yielding 84% of overlapping hits. Furthermore, rscreenorm yielded at most 8% discordant results, whilst median-centering yielded as much as 55%. Conclusions Rscreenorm yields more consistent results and keeps false positive rates under control, improving reproducibility of genetic screens data analysis from multiple cell lines.http://link.springer.com/article/10.1186/s12859-018-2306-zFunctional genomicsReproducibilityNormalization |
| spellingShingle | Costa Bachas Jasmina Hodzic Johannes C. van der Mijn Chantal Stoepker Henk M. W. Verheul Rob M. F. Wolthuis Emanuela Felley-Bosco Wessel N. van Wieringen Victor W. van Beusechem Ruud H. Brakenhoff Renée X. de Menezes Rscreenorm: normalization of CRISPR and siRNA screen data for more reproducible hit selection BMC Bioinformatics Functional genomics Reproducibility Normalization |
| title | Rscreenorm: normalization of CRISPR and siRNA screen data for more reproducible hit selection |
| title_full | Rscreenorm: normalization of CRISPR and siRNA screen data for more reproducible hit selection |
| title_fullStr | Rscreenorm: normalization of CRISPR and siRNA screen data for more reproducible hit selection |
| title_full_unstemmed | Rscreenorm: normalization of CRISPR and siRNA screen data for more reproducible hit selection |
| title_short | Rscreenorm: normalization of CRISPR and siRNA screen data for more reproducible hit selection |
| title_sort | rscreenorm normalization of crispr and sirna screen data for more reproducible hit selection |
| topic | Functional genomics Reproducibility Normalization |
| url | http://link.springer.com/article/10.1186/s12859-018-2306-z |
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