Negative binomial additive model for RNA-Seq data analysis
Abstract Background High-throughput sequencing experiments followed by differential expression analysis is a widely used approach for detecting genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. Exist...
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Format: | Article |
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BMC
2020-05-01
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Series: | BMC Bioinformatics |
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Online Access: | http://link.springer.com/article/10.1186/s12859-020-3506-x |
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author | Xu Ren Pei-Fen Kuan |
author_facet | Xu Ren Pei-Fen Kuan |
author_sort | Xu Ren |
collection | DOAJ |
description | Abstract Background High-throughput sequencing experiments followed by differential expression analysis is a widely used approach for detecting genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. Existing models assume linear effect of covariates, which is restrictive and may not be sufficient for certain phenotypes. Results We introduce NBAMSeq, a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation. Specifically, we model the logarithm of mean gene counts as sums of smooth functions with the smoothing parameters and coefficients estimated simultaneously within a nested iterative method. The variance is estimated by the Bayesian shrinkage approach to fully exploit the information across all genes. Conclusions Based on extensive simulations and case studies of RNA-Seq data, we show that NBAMSeq offers improved performance in detecting nonlinear effect and maintains equivalent performance in detecting linear effect compared to existing methods. The vignette and source code of NBAMSeq are available at http://bioconductor.org/packages/release/bioc/html/NBAMSeq.html. |
first_indexed | 2024-12-10T18:33:25Z |
format | Article |
id | doaj.art-3eecb19a8ed647cdab8d144795b03a0f |
institution | Directory Open Access Journal |
issn | 1471-2105 |
language | English |
last_indexed | 2024-12-10T18:33:25Z |
publishDate | 2020-05-01 |
publisher | BMC |
record_format | Article |
series | BMC Bioinformatics |
spelling | doaj.art-3eecb19a8ed647cdab8d144795b03a0f2022-12-22T01:37:53ZengBMCBMC Bioinformatics1471-21052020-05-0121111510.1186/s12859-020-3506-xNegative binomial additive model for RNA-Seq data analysisXu Ren0Pei-Fen Kuan1Department of Applied Mathematics and Statistics, Stony Brook UniversityDepartment of Applied Mathematics and Statistics, Stony Brook UniversityAbstract Background High-throughput sequencing experiments followed by differential expression analysis is a widely used approach for detecting genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. Existing models assume linear effect of covariates, which is restrictive and may not be sufficient for certain phenotypes. Results We introduce NBAMSeq, a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation. Specifically, we model the logarithm of mean gene counts as sums of smooth functions with the smoothing parameters and coefficients estimated simultaneously within a nested iterative method. The variance is estimated by the Bayesian shrinkage approach to fully exploit the information across all genes. Conclusions Based on extensive simulations and case studies of RNA-Seq data, we show that NBAMSeq offers improved performance in detecting nonlinear effect and maintains equivalent performance in detecting linear effect compared to existing methods. The vignette and source code of NBAMSeq are available at http://bioconductor.org/packages/release/bioc/html/NBAMSeq.html.http://link.springer.com/article/10.1186/s12859-020-3506-xBayesian shrinkageDifferential expression analysisGeneralized additive modelRNA-SeqSpline model |
spellingShingle | Xu Ren Pei-Fen Kuan Negative binomial additive model for RNA-Seq data analysis BMC Bioinformatics Bayesian shrinkage Differential expression analysis Generalized additive model RNA-Seq Spline model |
title | Negative binomial additive model for RNA-Seq data analysis |
title_full | Negative binomial additive model for RNA-Seq data analysis |
title_fullStr | Negative binomial additive model for RNA-Seq data analysis |
title_full_unstemmed | Negative binomial additive model for RNA-Seq data analysis |
title_short | Negative binomial additive model for RNA-Seq data analysis |
title_sort | negative binomial additive model for rna seq data analysis |
topic | Bayesian shrinkage Differential expression analysis Generalized additive model RNA-Seq Spline model |
url | http://link.springer.com/article/10.1186/s12859-020-3506-x |
work_keys_str_mv | AT xuren negativebinomialadditivemodelforrnaseqdataanalysis AT peifenkuan negativebinomialadditivemodelforrnaseqdataanalysis |