limma

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 48 articles )

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  1. Bommert, Andrea; Sun, Xudong; Bischl, Bernd; Rahnenführer, Jörg; Lang, Michel: Benchmark for filter methods for feature selection in high-dimensional classification data (2020)
  2. Niu, Lu; Liu, Xiumin; Zhao, Junlong: Robust estimator of the correlation matrix with sparse Kronecker structure for a high-dimensional matrix-variate (2020)
  3. Bhattacharjee, Atanu; Vishwakarma, Gajendra K.: Time-course data prediction for repeatedly measured gene expression (2019)
  4. Huang, Ping; Ge, Peng; Tian, Qing-Fen; Huang, Guo-Bao: Prediction of key transcription factors during skin regeneration by combining gene expression data and regulatory network information analysis (2019)
  5. Kiihl, Samara F.; Martinez-Garrido, Maria Jose; Domingo-Relloso, Arce; Bermudez, Jose; Tellez-Plaza, Maria: \textttMLML2R: an R package for maximum likelihood estimation of DNA methylation and hydroxymethylation proportions (2019)
  6. Zou, Ren-Chao; Xiao, Shu-Feng; Shi, Zhi-Tian; Ke, Yang; Tang, Hao-Ran; Wu, Tian-Gen; Guo, Zhi-Tang; Ni, Fan; Li, Wen-Xing; Wang, Lin: Identification of metabolism-associated pathways and genes involved in male and female liver cancer patients (2019)
  7. Page, Christian M.; Vos, Linda; Rounge, Trine B.; Harbo, Hanne F.; Andreassen, Bettina K.: Assessing genome-wide significance for the detection of differentially methylated regions (2018)
  8. Song, Wei; Liu, Huaping; Wang, Jiajia; Kong, Yan; Yin, Xia; Zang, Weidong: MATHT: a web server for comprehensive transcriptome data analysis (2018)
  9. Xia, Yinglin; Sun, Jun; Chen, Ding-Geng: Statistical analysis of microbiome data with R (2018)
  10. Felici, Giovanni; Tripathi, Kumar Parijat; Evangelista, Daniela; Guarracino, Mario Rosario: A mixed integer programming-based global optimization framework for analyzing gene expression data (2017)
  11. Jauhari, Shaurya; Rizvi, S. A. M.: \textitApriori, \textitdenovo mathematical exploration of gene expression mechanism via regression viewpoint with briefly cataloged modeling antiquity (2017)
  12. 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)
  13. Papastamoulis, Panagiotis; Rattray, Magnus: Bayesian estimation of differential transcript usage from RNA-seq data (2017)
  14. 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)
  15. Dasgupta, Nairanjana; Genz, Alan; Lazar, Nicole A.: A look at multiplicity through misclassification (2016)
  16. 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)
  17. Ruddy, Sean; Johnson, Marla; Purdom, Elizabeth: Shrinkage of dispersion parameters in the binomial family, with application to differential exon skipping (2016)
  18. Lithio, Andrew; Nettleton, Dan: Hierarchical modeling and differential expression analysis for RNA-seq experiments with inbred and hybrid genotypes (2015)
  19. Ma, Yanyuan; Yao, Weixin: Flexible estimation of a semiparametric two-component mixture model with one parametric component (2015)
  20. 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)

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