Structured hierarchical models for probabilistic inference from perturbation screening data

DOI10.1214/21-AOAS1580zbMath1498.62208OpenAlexW4286005063WikidataQ114599205 ScholiaQ114599205MaRDI QIDQ2170453

Niko Beerenwinkel, Simon Dirmeier

Publication date: 5 September 2022

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

Full work available at URL: https://doi.org/10.1214/21-aoas1580

zbMATH Keywords

Markov random fieldsprobabilistic modelshierarchical modelsbiological networkgenetic perturbation screeninterventional data

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Probabilistic graphical models (62H22)

Uses Software

• edgeR
• PyMC
• STRING

Cites Work

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• Philosophy and the practice of Bayesian statistics
• Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)
• A default conjugate prior for variance components in generalized linear mixed models (Comment on article by Browne and Draper)

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