Modeling overdispersion heterogeneity in differential expression analysis using mixtures

DOI10.1111/biom.12458zbMath1390.62237arXiv1410.8093OpenAlexW1589771869WikidataQ40195583 ScholiaQ40195583MaRDI QIDQ2827191

Cinzia Viroli, Stephane Robin, Franck Picard, Elisabetta Bonafede

Publication date: 12 October 2016

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1410.8093

zbMATH Keywords

mixture modelsdifferential expression analysisRNA-seq datatype-I errorROC/AUC

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Uses Software

• Bioconductor
• edgeR
• DEGseq
• BaySeq
• DEseq
• QuasiSeq
• Voom
• DEXUS
• compcodeR
• DESeq2

Cites Work

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• Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates
• The NBP negative binomial model for assessing differential gene expression from RNA-Seq
• A two-stage Poisson model for testing RNA-Seq data
• DESeq2
• Small-sample estimation of negative binomial dispersion, with applications to SAGE data
• Asymptotic Statistics
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Mixture Model on the Variance for the Differential Analysis of Gene Expression Data

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