edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. t is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data.

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  1. Martin, Bryan D.; Witten, Daniela; Willis, Amy D.: Modeling microbial abundances and dysbiosis with beta-binomial regression (2020)
  2. Ren, Boyu; Bacallado, Sergio; Favaro, Stefano; Vatanen, Tommi; Huttenhower, Curtis; Trippa, Lorenzo: Bayesian mixed effects models for zero-inflated compositions in microbiome data analysis (2020)
  3. Zhao, Sihai Dave; Nguyen, Yet Tien: Nonparametric false discovery rate control for identifying simultaneous signals (2020)
  4. Bandara, Udika; Gill, Ryan; Mitra, Riten: On computing maximum likelihood estimates for the negative binomial distribution (2019)
  5. Dobra, Adrian; Valdes, Camilo; Ajdic, Dragana; Clarke, Bertrand; Clarke, Jennifer: Modeling association in microbial communities with clique loglinear models (2019)
  6. Kuan-Hao Chao, Yi-Wen Hsiao, Yi-Fang Lee, Chien-Yueh Lee, Liang-Chuan Lai, Mong-Hsun Tsai, Tzu-Pin Lu, Eric Y. Chuang: RNASeqR: an R package for automated two-group RNA-Seq analysis workflow (2019) arXiv
  7. Zheng, Hong: A novel individualized drug repositioning approach for predicting personalized candidate drugs for type 1 diabetes mellitus (2019)
  8. Dadaneh, Siamak Zamani; Qian, Xiaoning; Zhou, Mingyuan: BNP-seq: Bayesian nonparametric differential expression analysis of sequencing count data (2018)
  9. Luo, Xiangyu; Wei, Yingying: Nonparametric Bayesian learning of heterogeneous dynamic transcription factor networks (2018)
  10. Song, Wei; Liu, Huaping; Wang, Jiajia; Kong, Yan; Yin, Xia; Zang, Weidong: MATHT: a web server for comprehensive transcriptome data analysis (2018)
  11. von Stechow, Louise (ed.); Delgado, Alberto Santos (ed.): Computational cell biology. Methods and protocols (2018)
  12. Wolff, Alexander: Analysis of expression profile and gene variation via development of methods for next generation sequencing data (2018)
  13. Xia, Yinglin; Sun, Jun; Chen, Ding-Geng: Statistical analysis of microbiome data with R (2018)
  14. Yang, Tae Young; Jeong, Seongmun: Controlling the false-discovery rate by procedures adapted to the length bias of RNA-seq (2018)
  15. Zhao, Lili; Wu, Weisheng; Feng, Dai; Jiang, Hui; Nguyen, Xuanlong: Bayesian analysis of RNA-Seq data using a family of negative binomial models (2018)
  16. Keith, Jonathan M. (ed.): Bioinformatics. Volume II: structure, function, and applications (2017)
  17. Kotoka, Ekua; Orr, Megan: Modifying SAMseq to account for asymmetry in the distribution of effect sizes when identifying differentially expressed genes (2017)
  18. 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)
  19. Papastamoulis, Panagiotis; Rattray, Magnus: Bayesian estimation of differential transcript usage from RNA-seq data (2017)
  20. Bonafede, Elisabetta; Picard, Franck; Robin, St├ęphane; Viroli, Cinzia: Modeling overdispersion heterogeneity in differential expression analysis using mixtures (2016)

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