A compositional model to assess expression changes from single-cell RNA-seq data

DOI10.1214/20-AOAS1423zbMath1478.62336OpenAlexW3181775732MaRDI QIDQ2245165

Christina Kendziorski, Keegan Korthauer, Michael A. Newton, Xiuyu Ma

Publication date: 15 November 2021

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

Full work available at URL: https://doi.org/10.1214/20-aoas1423

zbMATH Keywords

empirical Bayesclusteringmixture modellocal false discovery ratedouble Dirichlet mixture

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) Empirical decision procedures; empirical Bayes procedures (62C12)

Uses Software

• mixfdr
• DEseq
• EBSeq
• SC3
• MAST
• CIDR
• ZIFA
• DESeq2

Cites Work

• Unnamed Item
• An empirical Bayes mixture method for effect size and false discovery rate estimation
• Bayesian testing of many hypotheses \(\times \) many genes: a study of sleep apnea
• Markovian modelling of gene product synthesis
• Pearson-type goodness-of-fit test with bootstrap maximum likelihood estimation
• Size, power and false discovery rates
• 10.1162/153244303321897735
• On the Identifiability of Finite Mixtures
• Detecting differential gene expression with a semiparametric hierarchical mixture method
• Modal clustering in a class of product partition models

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