limma: Linear Models for Microarray Data. A survey is given of differential expression analyses using the linear modeling features of the limma package. The chapter starts with the simplest replicated designs and progresses through experiments with two or more groups, direct designs, factorial designs and time course experiments. Experiments with technical as well as biological replication are considered. Empirical Bayes test statistics are explained. The use of quality weights, adaptive background correction and control spots in conjunction with linear modelling is illustrated on the β7 data.

References in zbMATH (referenced in 24 articles )

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  1. Jauhari, Shaurya; Rizvi, S.A.M.: \itA priori, \itde novo mathematical exploration of gene expression mechanism via regression viewpoint with briefly cataloged modeling antiquity (2017)
  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. Shafaghati, Leila; Razaghi-Moghadam, Zahra; Mohammadnejad, Javad: A systems biology approach to understanding alcoholic liver disease molecular mechanism: the development of static and dynamic models (2017)
  4. Dasgupta, Nairanjana; Genz, Alan; Lazar, Nicole A.: A look at multiplicity through misclassification (2016)
  5. Phipson, Belinda; Lee, Stanley; Majewski, Ian J.; Alexander, Warren S.; Smyth, Gordon K.: Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression (2016)
  6. Ruddy, Sean; Johnson, Marla; Purdom, Elizabeth: Shrinkage of dispersion parameters in the binomial family, with application to differential exon skipping (2016)
  7. Lithio, Andrew; Nettleton, Dan: Hierarchical modeling and differential expression analysis for RNA-seq experiments with inbred and hybrid genotypes (2015)
  8. Ma, Yanyuan; Yao, Weixin: Flexible estimation of a semiparametric two-component mixture model with one parametric component (2015)
  9. Zehetmayer, Sonja; Graf, Alexandra C.; Posch, Martin: Sample size reassessment for a two-stage design controlling the false discovery rate (2015)
  10. Cristóbal Fresno; Mónica Balzarini; Elmer Fernández: lmdme: Linear Models on Designed Multivariate Experiments in R (2014)
  11. Blocker, Alexander W.; Meng, Xiao-Li: The potential and perils of preprocessing: building new foundations (2013)
  12. Larese, Mónica G.; Granitto, Pablo M.; Gómez, Juan C.: Spot defects detection in cDNA microarray images (2013) ioport
  13. Drăghici, Sorin: Statistics and data analysis for microarrays using R and Bioconductor. With CD-ROM. (2012)
  14. Klawonn, Frank: Significance tests to identify regulated proteins based on a large number of small samples (2012)
  15. Bar, Haim; Booth, James; Schifano, Elizabeth; Wells, Martin T.: Laplace approximated EM microarray analysis: an empirical Bayes approach for comparative microarray experiments (2010)
  16. Rossell, David: Gaga: a parsimonious and flexible model for differential expression analysis (2009)
  17. Xu, Ping; Brock, Guy N.; Parrish, Rudolph S.: Modified linear discriminant analysis approaches for classification of high-dimensional microarray data (2009)
  18. Tresch, Achim; Markowetz, Florian: Structure learning in nested effects models (2008)
  19. Angelini, Claudia; De Canditiis, Daniela; Mutarelli, Margherita; Pensky, Marianna: A Bayesian approach to estimation and testing in time-course microarray experiments (2007)
  20. Hu, Jianhua; Wright, Fred A.: Assessing differential gene expression with small sample sizes in oligonucleotide arrays using a mean-variance model (2007)

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