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 61 articles )

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  1. Djordjilović, Vera; Chiogna, Monica: Searching for a source of difference in graphical models (2022)
  2. Liang, Jane W.; Sen, Śaunak: Sparse matrix linear models for structured high-throughput data (2022)
  3. Tian, Tian; Cheng, Ruihua; Wei, Zhi: An empirical Bayes change-point model for transcriptome time-course data (2021)
  4. Zhao, Sihai Dave: Simultaneous estimation of normal means with side information (2021)
  5. Bommert, Andrea; Sun, Xudong; Bischl, Bernd; Rahnenführer, Jörg; Lang, Michel: Benchmark for filter methods for feature selection in high-dimensional classification data (2020)
  6. Chen, Jie; Chen, Jinggui; Sun, Bo; Wu, Jianghong; Du, Chunyan: Integrative analysis of immune microenvironment-related CeRNA regulatory axis in gastric cancer (2020)
  7. Jeong, Seok-Oh; Choi, Dongseok; Jang, Woncheol: A semiparametric mixture method for local false discovery rate estimation from multiple studies (2020)
  8. Marozzi, Marco; Mukherjee, Amitava; Kalina, Jan: Interpoint distance tests for high-dimensional comparison studies (2020)
  9. Niu, Lu; Liu, Xiumin; Zhao, Junlong: Robust estimator of the correlation matrix with sparse Kronecker structure for a high-dimensional matrix-variate (2020)
  10. Zhuo, Bin; Jiang, Duo; Di, Yanming: Test-statistic correlation and data-row correlation (2020)
  11. Bhattacharjee, Atanu; Vishwakarma, Gajendra K.: Time-course data prediction for repeatedly measured gene expression (2019)
  12. 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)
  13. 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)
  14. Madsen, Tobias; Świtnicki, Michał; Juul, Malene; Skou Pedersen, Jakob: \textttEBADIMEX: an empirical Bayes approach to detect joint differential expression and methylation and to classify samples (2019)
  15. 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)
  16. 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)
  17. Song, Wei; Liu, Huaping; Wang, Jiajia; Kong, Yan; Yin, Xia; Zang, Weidong: MATHT: a web server for comprehensive transcriptome data analysis (2018)
  18. Xia, Yinglin; Sun, Jun; Chen, Ding-Geng: Statistical analysis of microbiome data with R (2018)
  19. Felici, Giovanni; Tripathi, Kumar Parijat; Evangelista, Daniela; Guarracino, Mario Rosario: A mixed integer programming-based global optimization framework for analyzing gene expression data (2017)
  20. Jauhari, Shaurya; Rizvi, S. A. M.: \textitApriori, \textitdenovo mathematical exploration of gene expression mechanism via regression viewpoint with briefly cataloged modeling antiquity (2017)

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