HmmSeq: a hidden Markov model for detecting differentially expressed genes from RNA-seq data

DOI10.1214/15-AOAS815zbMath1454.62323arXiv1509.04838MaRDI QIDQ746680

Shiqi Cui, Marco A. R. Ferreira, Allison N. Tegge, Subharup Guha

Publication date: 28 October 2015

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1509.04838

zbMATH Keywords

overdispersionserial correlationBayesian hierarchical modelnext-generation sequencingfirst-order dependence

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Markov processes: estimation; hidden Markov models (62M05)

Related Items (3)

Covariate-modulated large-scale multiple testing under dependence ⋮ HmmSeq: a hidden Markov model for detecting differentially expressed genes from RNA-seq data ⋮ HmmSeq

Uses Software

• R
• Bioconductor
• CRAN
• edgeR
• tmvtnorm
• CNAmet
• BaySeq
• DAVID

Cites Work

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• A two-stage Poisson model for testing RNA-Seq data
• HmmSeq: a hidden Markov model for detecting differentially expressed genes from RNA-seq data
• Bayes inference in regression models with ARMA\((p,q)\) errors
• Finite mixture and Markov switching models.
• An Optimal Test with Maximum Average Power While Controlling FDR with Application to RNA‐Seq Data
• Small-sample estimation of negative binomial dispersion, with applications to SAGE data
• Bayesian Hidden Markov Modeling of Array CGH Data
• Bayesian Methods for Hidden Markov Models
• Bayesian Measures of Model Complexity and Fit
• Statistical significance for genomewide studies
• Detecting differential gene expression with a semiparametric hierarchical mixture method

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