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Negative binomial additive model for RNA-Seq data analysis.
Ren, Xu; Kuan, Pei-Fen.
Afiliação
  • Ren X; Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, 11794, NY, USA.
  • Kuan PF; Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, 11794, NY, USA. peifen.kuan@stonybrook.edu.
BMC Bioinformatics ; 21(1): 171, 2020 May 01.
Article em En | MEDLINE | ID: mdl-32357831
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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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Modelos Estatísticos / Análise de Dados / RNA-Seq Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Modelos Estatísticos / Análise de Dados / RNA-Seq Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article