Structure Learning in Nested Effects Models

Details Structure Learning in Nested Effects Models Structure Learning in Nested Effects Models

2007-10-24

arxiv.org/abs/0710.4481v2

Stat Appl in Gen and Mol Bio (SAGMB): Vol. 7: Iss. 1, Article 9, 2008

Biology

Quantitative Biology

Quantitative Methods

Scientific paper

10.2202/1544-6115.1332

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.

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