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Goodness-of-Fit Tests and Model Diagnostics for Negative Binomial Regression of RNA Sequencing Data Public Deposited

https://ir.library.oregonstate.edu/concern/articles/6t053h871

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  • This work is about assessing model adequacy for negative binomial (NB) regression, particularly (1) assessing the adequacy of the NB assumption, and (2) assessing the appropriateness of models for NB dispersion parameters. Tools for the first are appropriate for NB regression generally; those for the second are primarily intended for RNA sequencing (RNA-Seq) data analysis. The typically small number of biological samples and large number of genes in RNA-Seq analysis motivate us to address the trade-offs between robustness and statistical power using NB regression models. One widely-used power-saving strategy, for example, is to assume some commonalities of NB dispersion parameters across genes via simple models relating them to mean expression rates, and many such models have been proposed. As RNA-Seq analysis is becoming ever more popular, it is appropriate to make more thorough investigations into power and robustness of the resulting methods, and into practical tools for model assessment. In this article, we propose simulation-based statistical tests and diagnostic graphics to address model adequacy. We provide simulated and real data examples to illustrate that our proposed methods are effective for detecting the misspecification of the NB mean-variance relationship as well as judging the adequacy of fit of several NB dispersion models.
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  • Mi, G., Di, Y., & Schafer, D. W. (2015). Goodness-of-Fit Tests and Model Diagnostics for Negative Binomial Regression of RNA Sequencing Data. PLoS ONE, 10(3), e0119254. doi:10.1371/journal.pone.0119254
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  • YD and GM were supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R01GM104977.
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  • description.provenance : Approved for entry into archive by Erin Clark(erin.clark@oregonstate.edu) on 2015-04-29T19:21:23Z (GMT) No. of bitstreams: 3 license_rdf: 1370 bytes, checksum: cd1af5ab51bcc7a5280cf305303530e9 (MD5) MiGuStatisticsGoodness-of-FitTests.pdf: 746678 bytes, checksum: 9cd19902d240da252e1b1b9373637a05 (MD5) MiGuStatisticsGoodness-of-FitTests_SupportingInformation.pdf: 413182 bytes, checksum: 3a7178542be33b54440deff4db4b2b51 (MD5)
  • description.provenance : Submitted by Erin Clark (erin.clark@oregonstate.edu) on 2015-04-29T19:21:07Z No. of bitstreams: 3 license_rdf: 1370 bytes, checksum: cd1af5ab51bcc7a5280cf305303530e9 (MD5) MiGuStatisticsGoodness-of-FitTests.pdf: 746678 bytes, checksum: 9cd19902d240da252e1b1b9373637a05 (MD5) MiGuStatisticsGoodness-of-FitTests_SupportingInformation.pdf: 413182 bytes, checksum: 3a7178542be33b54440deff4db4b2b51 (MD5)
  • description.provenance : Made available in DSpace on 2015-04-29T19:21:23Z (GMT). No. of bitstreams: 3 license_rdf: 1370 bytes, checksum: cd1af5ab51bcc7a5280cf305303530e9 (MD5) MiGuStatisticsGoodness-of-FitTests.pdf: 746678 bytes, checksum: 9cd19902d240da252e1b1b9373637a05 (MD5) MiGuStatisticsGoodness-of-FitTests_SupportingInformation.pdf: 413182 bytes, checksum: 3a7178542be33b54440deff4db4b2b51 (MD5) Previous issue date: 2015-03-18

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