DESeq2

R/Bioconductor package DESeq2: Differential gene expression analysis based on the negative binomial distribution. Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. Citation (from within R, enter citation(”DESeq2”)): Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology, 15, 550. doi: 10.1186/s13059-014-0550-8.


References in zbMATH (referenced in 27 articles )

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  1. Cao, Hongyuan; Chen, Jun; Zhang, Xianyang: Optimal false discovery rate control for large scale multiple testing with auxiliary information (2022)
  2. Wang, Qiang; Yu, Xia; Gong, Mingzhi: Single-cell transcriptome analysis reveals the importance of IRF1/FSTL1 in synovial fibroblast subsets for the development of rheumatoid arthritis (2022)
  3. Cheng, Xiaoqing; Ching, Wai-Ki; Guo, Sini; Akutsu, Tatsuya: Discrimination of attractors with noisy nodes in Boolean networks (2021)
  4. Ellenbach, Nicole; Boulesteix, Anne-Laure; Bischl, Bernd; Unger, Kristian; Hornung, Roman: Improved outcome prediction across data sources through robust parameter tuning (2021)
  5. Lim, David K.; Rashid, Naim U.; Ibrahim, Joseph G.: Model-based feature selection and clustering of RNA-seq data for unsupervised subtype discovery (2021)
  6. Ling, Wodan; Zhang, Wenfei; Cheng, Bin; Wei, Ying: Zero-inflated quantile rank-score based test (ZIQRank) with application to scRNA-seq differential gene expression analysis (2021)
  7. Lin, Kevin Z.; Lei, Jing; Roeder, Kathryn: Exponential-family embedding with application to cell developmental trajectories for single-cell RNA-seq data (2021)
  8. Lin, Kevin Z.; Lei, Jing; Roeder, Kathryn: Rejoinder for: “Exponential-family embedding with application to cell developmental trajectories for single-cell RNA-seq data” (2021)
  9. Ma, Xiuyu; Korthauer, Keegan; Kendziorski, Christina; Newton, Michael A.: A compositional model to assess expression changes from single-cell RNA-seq data (2021)
  10. Mulas, Raffaella; Casey, Michael J.: Estimating cellular redundancy in networks of genetic expression (2021)
  11. Paynter, Alex; Willis, Amy D.: Tuning parameter selection for a penalized estimator of species richness (2021)
  12. Signorelli, Mirko; Spitali, Pietro; Tsonaka, Roula: Poisson-Tweedie mixed-effects model: a flexible approach for the analysis of longitudinal RNA-seq data (2021)
  13. Tian, Tian; Cheng, Ruihua; Wei, Zhi: An empirical Bayes change-point model for transcriptome time-course data (2021)
  14. Lin, Zhixiang; Zamanighomi, Mahdi; Daley, Timothy; Ma, Shining; Wong, Wing Hung: Model-based approach to the joint analysis of single-cell data on chromatin accessibility and gene expression (2020)
  15. Martin, Bryan D.; Witten, Daniela; Willis, Amy D.: Modeling microbial abundances and dysbiosis with beta-binomial regression (2020)
  16. Fukuyama, Julia: Adaptive gPCA: a method for structured dimensionality reduction with applications to microbiome data (2019)
  17. 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)
  18. Dadaneh, Siamak Zamani; Qian, Xiaoning; Zhou, Mingyuan: BNP-seq: Bayesian nonparametric differential expression analysis of sequencing count data (2018)
  19. Philtron, Daisy; Lyu, Yafei; Li, Qunhua; Ghosh, Debashis: Maximum rank reproducibility: a nonparametric approach to assessing reproducibility in replicate experiments (2018)
  20. Sankaran, Kris; Holmes, Susan: Interactive visualization of hierarchically structured data (2018)

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