apeglm: Shrinkage Estimators for Differential Expression of RNA-Seq

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Presentation transcript:

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

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

Example: Differential Expression Analysis of RNA-Seq Count Matrix Design matrix Nuisance Parameter Likelihood function Detect genes with changes in expression level across different experimental conditions Figure: Estimates over true LFCs.

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)

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.