Negative Binomial Additive Model for RNA-Seq Data Analysis

Details

• Personal Author:
  Kuan P-F ; Ren X
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Biomarkers Genetics Occupational Health Safety Software
• Keywords:
  Analytical Models Author Keywords: Bayesian Shrinkage Bioinformatics Computer Applications Computer Software Deoxyribonucleic Acids Differential Expression Analysis DNA DNA Microarrays Gene Expression Generalized Additive Model Genes Genetic Testing Genomics Mathematical Models Models Ribonucleic Acids RNA RNA-Seq Simulation Methods Spline Model Statistical Analysis

  More +
• ISSN:
  1471-2105
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Journal Article
• Place as Subject:
  New York ; OSHA Region 2
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  21
• NIOSHTIC Number:
  nn:20059497
• Citation:
  BMC Bioinformatics 2020 May; 21:171
• Contact Point Address:
  Pei-Fen Kuan, Department of Applied Mathematics and Statistics, Stony Brook University, 11794 Stony Brook, NY, USA
• Email:
  peifen.kuan@stonybrook.edu
• Federal Fiscal Year:
  2020
• Performing Organization:
  State University of New York at Stony Brook
• Peer Reviewed:
  True
• Start Date:
  20170701
• Source Full Name:
  BMC Bioinformatics
• End Date:
  20200630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:6b25dd5bcfb1f6e5ccf708956f6451174fc4d8f58f08dc994b511f76e5af9aa6cbaa22786d0b23a6b438c92c8dd9dad53c54ef629852c54181124b0d926f53cf
• Download URL:
  https://stacks.cdc.gov/view/cdc/223700/cdc_223700_DS1.pdf
• File Type:
  [PDF - 1.19 MB ]