Structure Learning in Nested Effects Models

TL;DR

This paper advances Nested Effects Models by deriving a new likelihood formula, proving model identifiability, enabling efficient model exploration, and integrating prior knowledge for noise reduction.

Contribution

It introduces a generalized likelihood formula, proves model identifiability, and incorporates prior knowledge and variable selection into NEMs.

Findings

New likelihood formula for NEMs generalized for binary data

Proved model identifiability under mild assumptions

Enhanced model exploration efficiency and noise handling

Abstract

Nested Effects Models (NEMs) are a class of graphical models introduced to analyze the results of gene perturbation screens. NEMs explore noisy subset relations between the high-dimensional outputs of phenotyping studies, e.g. the effects showing in gene expression profiles or as morphological features of the perturbed cell. In this paper we expand the statistical basis of NEMs in four directions: First, we derive a new formula for the likelihood function of a NEM, which generalizes previous results for binary data. Second, we prove model identifiability under mild assumptions. Third, we show that the new formulation of the likelihood allows to efficiently traverse model space. Fourth, we incorporate prior knowledge and an automated variable selection criterion to decrease the influence of noise in the data.