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Deep hierarchical programs combined with Markov random fields.
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Structured hierarchical models

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Deep hierarchical programs combined with categorical Markov random fields.


Structured hierarchical models are a family of generative models for probabilistic inference of causal effects from genetic 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 genes. The random field induces a clustering of the genes which helps to inform inference of parameters in the hierarchical model. SHMs are designed for extremely noisy, experimental data sets for which the true data generating process is difficult to model.

An SHM has the following basic structure.

The top-level categorical variables $z_g$ are latent cluster assignments for $g$ genes with effect sizes $\gamma_g$. The effect sizes are used to parameterize a deep hierarchical model, for instance to represent a heterogeneous data set $y_{gci}$ of multiple conditions $c$ and interventions $i$.


You can install the package using:

pip install<version>.tar.gz

where <version> is the most recent release on GitHub. Please don't install from the master branch directly.


If you like this package, please consider citing it:

Dirmeier, S. and Beerenwinkel, N. (2019). Structured hierarchical models for probabilistic inference from perturbation screening data. bioRxiv.


Simon Dirmeier simon.dirmeier @

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