Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression

DOI10.1214/16-AOAS920zbMath1400.62281arXiv1602.08678WikidataQ37730548 ScholiaQ37730548MaRDI QIDQ312950

Gordon K. Smyth, Warren S. Alexander, Ian J. Majewski, Stanley Lee, Belinda Phipson

Publication date: 9 September 2016

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

Full work available at URL: https://arxiv.org/abs/1602.08678

zbMATH Keywords

outliersrobustnessempirical Bayesgene expressionmicroarrays

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Robustness and adaptive procedures (parametric inference) (62F35) Protein sequences, DNA sequences (92D20) Empirical decision procedures; empirical Bayes procedures (62C12)

Related Items

Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression, No counts, no variance: allowing for loss of degrees of freedom when assessing biological variability from RNA-seq data, Regularized t$$ t $$ distribution: definition, properties, and applications, Continuous readout versus titer-based assays of influenza vaccine trials: sensitivity, specificity, and false discovery rates

Uses Software

• Bioconductor
• edgeR
• limma
• Voom
• BRENT
• Rsubread
• csaw
• diffHic

Cites Work

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