A hierarchical Bayesian model for single-cell clustering using RNA-sequencing data

DOI10.1214/19-AOAS1250zbMath1433.62311OpenAlexW2980819072MaRDI QIDQ2281217

Joshua L. Warren, Hongyu Zhao, Yiyi Liu

Publication date: 19 December 2019

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aoas/1571277771

zbMATH Keywords

clusteringDirichlet processmissing dataBayesian hierarchical modelGaussian mixture modelsingle-cell RNA-sequencing

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Protein sequences, DNA sequences (92D20) Missing data (62D10)

Related Items (2)

Nonparametric Bayesian Two-Level Clustering for Subject-Level Single-Cell Expression Data ⋮ A hierarchical Bayesian model for single-cell clustering using RNA-sequencing data

Uses Software

• mixtools
• t-SNE
• MAST
• CIDR
• ZIFA
• scRNAseq
• SNN-Cliq

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Inference from iterative simulation using multiple sequences
• A hierarchical Bayesian model for single-cell clustering using RNA-sequencing data
• A Bayesian analysis of some nonparametric problems
• Inconsistency of Pitman-Yor process mixtures for the number of components
• Bayesian Repulsive Gaussian Mixture Model
• Improved criteria for clustering based on the posterior similarity matrix

This page was built for publication: A hierarchical Bayesian model for single-cell clustering using RNA-sequencing data