Journal article
Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates.
SP Lund, D Nettleton, DJ McCarthy, GK Smyth
Statistical applications in genetics and molecular biology | WALTER DE GRUYTER GMBH | Published : 2012
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 compet..
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Funding Acknowledgements
This work was supported by funding from the following sources: National Research Initiative of the USDA-CSREES Grant No. 2008-35600-18786 (to SPL and DN); National Science Foundation grant number 0820610 (to DN); General Sir John Monash Scholarship (to DJM); National Health and Medical Research Council Program Grant 490037 (to GKS). Disclaimer: The identification of any commercial products is given only for the sake of completely describing our experimental procedures. In no instance does such identification imply any recommendation by the National Institute of Standards and Technology; nor does it imply that the particular equipment identified is necessarily the best available for the described process. Steven P. Lund is also affiliated to Department of Statistics, Iowa State University. Davis J. McCarthy is also affiliated to Walter and Eliza Hall Institute of Medical Research. Gordon K. Smyth is also affiliated to Department of Mathematics and Statistics, University of Melbourne.