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apeglm : Shrinkage Estimators for Differential Expression of RNA- Seq

Learn about apeglm, a shrinkage estimator for RNA-Seq analysis. This article covers its workflow, inputs, outputs, and downstream analysis. It also discusses other features such as contrasts, weights, and random nuisance parameters.

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apeglm : Shrinkage Estimators for Differential Expression of RNA- Seq

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  1. apeglm: Shrinkage Estimators for Differential Expression of RNA-Seq Anqi Zhu, Joseph G. Ibrahim, Michael I. Love Department of Biostatistics, Gillings School of Public Health, University of North Carolina Chapel Hill

  2. apeglm: • Shrinkage estimator on Generalized Linear Model (GLM) coefficients • Workflow: • apeglm(Y, x, log.lik, param=NULL, coef=NULL, …) Count matrix, design matrix, likelihood function, nuisance parameters Input Shrinkage estimator and Standard Error, False Sign Rate and s value, Stephens 2016 Credible intervals apeglm Downstream analysis

  3. Example: Differential Expression Analysis of RNA-Seq Nuisance Parameter Count Matrix Design matrix Likelihoodfunction Figure: Estimates over true LFCs.

  4. Other features of apeglm • Contrasts • Weights • Random nuisance parameter • Other type of High Throughput Sequencing data • Available at • https://bitbucket.org/azhu513/apeglm • DESeq2::lfcshrink() (after submitting to Bioconductor)

  5. Reference • Love, Michael I., Wolfgang Huber, and Simon Anders. "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2." Genome biology 15.12 (2014): 550. • Stephens, Matthew. "False discovery rates: a new deal." Biostatistics 18.2 (2016): 275-294.

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