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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Main Authors: Xu Ren, Pei-Fen Kuan
Format: Article
Language:English
Published: BMC 2020-05-01
Series:BMC Bioinformatics
Subjects:
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.
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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