QuasiSeq

QuasiSeq: Analyzing RNA Sequence Count Tables Using Quasi-likelihood. Identify differentially expressed genes in RNA-seq count data using quasi-Poisson or quasi-negative binomial models with QL, QLShrink and QLSpline methods (Lund, Nettleton, McCarthy, and Smyth, 2012).

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References in zbMATH (referenced in 7 articles , 1 standard article )

Showing results 1 to 7 of 7.
Sorted by year (citations)

  1. Qiu, Yumou; Chen, Song Xi; Nettleton, Dan: Detecting rare and faint signals via thresholding maximum likelihood estimators (2018)
  2. Lun, Aaron T. L.; Smyth, Gordon K.: No counts, no variance: allowing for loss of degrees of freedom when assessing biological variability from RNA-seq data (2017)
  3. Bonafede, Elisabetta; Picard, Franck; Robin, Stéphane; Viroli, Cinzia: Modeling overdispersion heterogeneity in differential expression analysis using mixtures (2016)
  4. Cui, Shiqi; Ji, Tieming; Li, Jilong; Cheng, Jianlin; Qiu, Jing: What if we ignore the random effects when analyzing RNA-seq data in a multifactor experiment (2016)
  5. Nguyen, Yet; Nettleton, Dan; Liu, Haibo; Tuggle, Christopher K.: Detecting differentially expressed genes with RNA-seq data using backward selection to account for the effects of relevant covariates (2015)
  6. Ji, Tieming; Liu, Peng; Nettleton, Dan: Estimation and testing of gene expression heterosis (2014)
  7. Lund, Steven P.; Nettleton, Dan; McCarthy, Davis J.; Smyth, Gordon K.: Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates (2012)