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dc.contributor.author
Dirmeier, Simon
dc.contributor.author
Beerenwinkel, Niko
dc.date.accessioned
2022-01-25T08:09:11Z
dc.date.available
2022-01-24T19:16:11Z
dc.date.available
2022-01-25T08:09:11Z
dc.date.issued
2019-11-20
dc.identifier.other
10.1101/848234
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/528090
dc.identifier.doi
10.3929/ethz-b-000528090
dc.description.abstract
Genetic perturbation screening is an experimental method in biology to study cause and effect relationships between different biological entities. However, knocking out or knocking down genes is a highly error-prone process that complicates estimation of the effect sizes of the interventions. Here, we introduce a family of generative models, called the structured hierarchical model (SHM), for probabilistic inference of causal effects from perturbation screens. SHMs utilize classical hierarchical models to represent heterogeneous data and combine them with categorical Markov random fields to encode biological prior information over functionally related biological entities. The random field induces a clustering of functionally related genes which informs inference of parameters in the hierarchical model. The SHM is designed for extremely noisy data sets for which the true data generating process is difficult to model due to lack of domain knowledge or high stochasticity of the interventions. We apply the SHM to a pan-cancer genetic perturbation screen in order to identify genes that restrict the growth of an entire group of cancer cell lines and show that incorporating prior knowledge in the form of a graph improves inference of parameters.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Cold Spring Harbor Laboratory
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject
Probabilistic models
en_US
dc.subject
Hierarchical model
en_US
dc.subject
Markov random fields
en_US
dc.subject
Biological network
en_US
dc.subject
Genetic perturbation screen
en_US
dc.subject
Interventional data
en_US
dc.subject
Python
en_US
dc.subject
PyMC3
en_US
dc.title
Structured hierarchical models for probabilistic inference from perturbation screening data
en_US
dc.type
Working Paper
dc.rights.license
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
ethz.journal.title
bioRxiv
ethz.size
29 p.
en_US
ethz.publication.place
Cold Spring Harbor, NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::03790 - Beerenwinkel, Niko / Beerenwinkel, Niko
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::03790 - Beerenwinkel, Niko / Beerenwinkel, Niko
en_US
ethz.date.deposited
2022-01-24T19:16:38Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2022-01-25T08:09:22Z
ethz.rosetta.lastUpdated
2022-03-29T17:48:26Z
ethz.rosetta.versionExported
true
ethz.COinS
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