Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates
作者： Steven P. Lund , Dan Nettleton , Davis J. McCarthy , Gordon K. Smyth
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摘要: Next generation sequencing technology provides a powerful tool for measuring gene expression (mRNA) levels in the form of RNA-sequence data. Method development 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 popular area ongoing research. Here we present quasi-likelihood methods with shrunken dispersion estimates based on an adaptation Smyth's (2004) approach estimating gene-specific error variances microarray Our suggested are computationally simple, analogous ANOVA compare favorably versus competing detecting DE false discovery rates across variety simulations real
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