Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates
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Steven P. Lund
Abstract
Next generation sequencing technology provides a powerful tool for measuring gene expression (mRNA) levels in the form of RNA-sequence data. Method development for identifying differentially expressed (DE) genes from RNA-seq data, which frequently includes many low-count integers and can exhibit severe overdispersion relative to Poisson or binomial distributions, is a popular area of ongoing research. Here we present quasi-likelihood methods with shrunken dispersion estimates based on an adaptation of Smyth's (2004) approach to estimating gene-specific error variances for microarray data. Our suggested methods are computationally simple, analogous to ANOVA and compare favorably versus competing methods in detecting DE genes and estimating false discovery rates across a variety of simulations based on real data.
©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Artikel in diesem Heft
- Article
- Large-scale Parentage Inference with SNPs: an Efficient Algorithm for Statistical Confidence of Parent Pair Allocations
- ExactDAS: An Exact Test Procedure for the Detection of Differential Alternative Splicing in Microarray Experiments
- Incorporating Genomic Annotation into a Hidden Markov Model for DNA Methylation Tiling Array Data
- Variational Bayes Procedure for Effective Classification of Tumor Type with Microarray Gene Expression Data
- Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates
- Empirical Bayesian Selection of Hypothesis Testing Procedures for Analysis of Sequence Count Expression Data
- Analyzing Genetic Association Studies with an Extended Propensity Score Approach
- Genotype Copy Number Variations using Gaussian Mixture Models: Theory and Algorithms
- Estimators of the local false discovery rate designed for small numbers of tests
- A PAUC-based Estimation Technique for Disease Classification and Biomarker Selection
- Comparison of Targeted Maximum Likelihood and Shrinkage Estimators of Parameters in Gene Networks
- DNA Pooling and Statistical Tests for the Detection of Single Nucleotide Polymorphisms
