A unified statistical framework for single cell and bulk RNA sequencing data

DOI10.1214/17-AOAS1110zbMath1393.62120arXiv1609.08028OpenAlexW3099404564WikidataQ91266474 ScholiaQ91266474MaRDI QIDQ1647646

Bernie Devlin, Lingxue Zhu, Kathryn Roeder, Jing Lei

Publication date: 26 June 2018

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

Full work available at URL: https://arxiv.org/abs/1609.08028

zbMATH Keywords

EM algorithmempirical Bayeshierarchical modelGibbs samplingsingle cell RNA sequencing

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12) Protein sequences, DNA sequences (92D20)

Related Items (5)

A unified statistical framework for single cell and bulk RNA sequencing data ⋮ Kinetic Foundation of the Zero-Inflated Negative Binomial Model for Single-Cell RNA Sequencing Data ⋮ Model-based approach to the joint analysis of single-cell data on chromatin accessibility and gene expression ⋮ A kernel non-negative matrix factorization framework for single cell clustering ⋮ Nonparametric Bayesian multiarmed bandits for single-cell experiment design

Uses Software

• BayesLogit
• MAST
• CIDR
• ZIFA
• BPSC
• BASiCS

Cites Work

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• A unified statistical framework for single cell and bulk RNA sequencing data
• An introduction to variational methods for graphical models
• Sampling-Based Approaches to Calculating Marginal Densities
• Graphical Models, Exponential Families, and Variational Inference
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• 10.1162/jmlr.2003.3.4-5.993
• Gene expression distribution deconvolution in single-cell RNA sequencing
• Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables

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