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.
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References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- Jia, Chen: Kinetic foundation of the zero-inflated negative binomial model for single-cell RNA sequencing data (2020)
- 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)
- Loos, Carolin; Hasenauer, Jan: Robust calibration of hierarchical population models for heterogeneous cell populations (2020)
- Liu, Yiyi; Warren, Joshua L.; Zhao, Hongyu: A hierarchical Bayesian model for single-cell clustering using RNA-sequencing data (2019)
- Park, Seyoung; Zhao, Hongyu: Sparse principal component analysis with missing observations (2019)
- Rabin, Neta; Fishelov, Dalia: Two directional Laplacian pyramids with application to data imputation (2019)
- Ye, Mao; Zhang, Peng; Nie, Lizhen: Clustering sparse binary data with hierarchical Bayesian Bernoulli mixture model (2018)
- Zhu, Lingxue; Lei, Jing; Devlin, Bernie; Roeder, Kathryn: A unified statistical framework for single cell and bulk RNA sequencing data (2018)