References in zbMATH (referenced in 59 articles )

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  1. Jouvin, Nicolas; Latouche, Pierre; Bouveyron, Charles; Bataillon, Guillaume; Livartowski, Alain: Greedy clustering of count data through a mixture of multinomial PCA (2021)
  2. Lim, David K.; Rashid, Naim U.; Ibrahim, Joseph G.: Model-based feature selection and clustering of RNA-seq data for unsupervised subtype discovery (2021)
  3. Ma, Xiuyu; Korthauer, Keegan; Kendziorski, Christina; Newton, Michael A.: A compositional model to assess expression changes from single-cell RNA-seq data (2021)
  4. Tyler Grimes, Somnath Datta: SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data (2021) not zbMATH
  5. Wang, Minjie; Allen, Genevera I.: Integrative generalized convex clustering optimization and feature selection for mixed multi-view data (2021)
  6. Wang, Y. X. Rachel; Li, Lexin; Li, Jingyi Jessica; Huang, Haiyan: Network modeling in biology: statistical methods for gene and brain networks (2021)
  7. Zhang, Rong; Ren, Zhao; Celedón, Juan C.; Chen, Wei: Inference of large modified Poisson-type graphical models: application to RNA-seq data in childhood atopic asthma studies (2021)
  8. Daghyani, Masoud; Zamzami, Nuha; Bouguila, Nizar: Toward an efficient computation of log-likelihood functions in statistical inference: overdispersed count data clustering (2020)
  9. Jia, Chen: Kinetic foundation of the zero-inflated negative binomial model for single-cell RNA sequencing data (2020)
  10. Mayrink, Vinícius Diniz; Gonçalves, Flávio B.: Identifying atypically expressed chromosome regions using RNA-Seq data (2020)
  11. 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)
  12. Zhuang, Yonghua; Wade, Kristen; Saba, Laura M.; Kechris, Katerina: Development of a tissue augmented Bayesian model for expression quantitative trait loci analysis (2020)
  13. Alvarez-Castro, Ignacio; Niemi, Jarad: Fully Bayesian analysis of allele-specific RNA-seq data (2019)
  14. Bandara, Udika; Gill, Ryan; Mitra, Riten: On computing maximum likelihood estimates for the negative binomial distribution (2019)
  15. 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
  16. Li, Xiaohong; Wu, Dongfeng; Cooper, Nigel G. F.; Rai, Shesh N.: Sample size calculations for the differential expression analysis of RNA-seq data using a negative binomial regression model (2019)
  17. Monod, Anthea; Kališnik, Sara; Patiño-Galindo, Juan Ángel; Crawford, Lorin: Tropical sufficient statistics for persistent homology (2019)
  18. Plunkett, Amanda; Park, Junyong: Two-sample test for sparse high-dimensional multinomial distributions (2019)
  19. Dadaneh, Siamak Zamani; Qian, Xiaoning; Zhou, Mingyuan: BNP-seq: Bayesian nonparametric differential expression analysis of sequencing count data (2018)
  20. Liu, Lydia T.; Dobriban, Edgar; Singer, Amit: (e)PCA: high dimensional exponential family PCA (2018)

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