ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis. Single-cell RNA-seq data allows insight into normal cellular function and various disease states through molecular characterization of gene expression on the single cell level. Dimensionality reduction of such high-dimensional data sets is essential for visualization and analysis, but single-cell RNA-seq data are challenging for classical dimensionality-reduction methods because of the prevalence of dropout events, which lead to zero-inflated data. Here, we develop a dimensionality-reduction method, (Z)ero (I)nflated (F)actor (A)nalysis (ZIFA), which explicitly models the dropout characteristics, and show that it improves modeling accuracy on simulated and biological data sets.

References in zbMATH (referenced in 13 articles )

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  1. Bartlett, Thomas E.; Kosmidis, Ioannis; Silva, Ricardo: Two-way sparsity for time-varying networks with applications in genomics (2021)
  2. Jiang, Hao; Yi, Ming; Zhang, Shihua: A kernel non-negative matrix factorization framework for single cell clustering (2021)
  3. Lin, Kevin Z.; Lei, Jing; Roeder, Kathryn: Exponential-family embedding with application to cell developmental trajectories for single-cell RNA-seq data (2021)
  4. Ma, Xiuyu; Korthauer, Keegan; Kendziorski, Christina; Newton, Michael A.: A compositional model to assess expression changes from single-cell RNA-seq data (2021)
  5. Zeng, Yanyan; Zhao, Hongyu; Wang, Tao: Model-based microbiome data ordination: a variational approximation approach (2021)
  6. Jia, Chen: Kinetic foundation of the zero-inflated negative binomial model for single-cell RNA sequencing data (2020)
  7. 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)
  8. Loos, Carolin; Hasenauer, Jan: Robust calibration of hierarchical population models for heterogeneous cell populations (2020)
  9. Liu, Yiyi; Warren, Joshua L.; Zhao, Hongyu: A hierarchical Bayesian model for single-cell clustering using RNA-sequencing data (2019)
  10. Park, Seyoung; Zhao, Hongyu: Sparse principal component analysis with missing observations (2019)
  11. Rabin, Neta; Fishelov, Dalia: Two directional Laplacian pyramids with application to data imputation (2019)
  12. Ye, Mao; Zhang, Peng; Nie, Lizhen: Clustering sparse binary data with hierarchical Bayesian Bernoulli mixture model (2018)
  13. Zhu, Lingxue; Lei, Jing; Devlin, Bernie; Roeder, Kathryn: A unified statistical framework for single cell and bulk RNA sequencing data (2018)