Detecting differentially expressed genes with RNA-seq data using backward selection to account for the effects of relevant covariates

DOI10.1007/s13253-015-0226-1zbMath1329.62446OpenAlexW1842080361WikidataQ31031790 ScholiaQ31031790MaRDI QIDQ906078

Haibo Liu, Dan Nettleton, Christopher K. Tuggle, Yet Tien Nguyen

Publication date: 29 January 2016

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13253-015-0226-1

zbMATH Keywords

quasi-likelihoodgeneralized linear modelfalse discovery rateresidual feed intake

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12) Protein sequences, DNA sequences (92D20)

Uses Software

• R
• Bioconductor
• BaySeq
• DEseq
• RUVseq
• limma
• QuasiSeq
• ShrinkBayes
• Voom
• svaseq

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
• Small-sample estimation of negative binomial dispersion, with applications to SAGE data
• Adaptive and Dynamic Adaptive Procedures for False Discovery Rate Control and Estimation
• A Direct Approach to False Discovery Rates

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