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arXiv · 2007.14299

Accounting for missing actors in interaction network inference from abundance data

Raphaëlle Momal·Stéphane Robin·Christophe Ambroise
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Abstract

Network inference aims at unraveling the dependency structure relating jointly observed variables. Graphical models provide a general framework to distinguish between marginal and conditional dependency. Unobserved variables (missing actors) may induce apparent conditional dependencies.In the context of count data, we introduce a mixture of Poisson log-normal distributions with tree-shaped graphical models, to recover the dependency structure, including missing actors. We design a variational EM algorithm and assess its performance on synthetic data. We demonstrate the ability of our approach to recover environmental drivers on two ecological datasets. The corresponding R package is available from github.com/Rmomal/nestor.

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Raphaëlle Momal, Stéphane Robin, Christophe Ambroise. 2020-07-28. Accounting for missing actors in interaction network inference from abundance data. https://arxiv.org/abs/2007.14299

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