Cross-Validation for Training and Testing Co-occurrence Network
  Inference Algorithms
Daniel Agyapong Jeffrey Ryan Propster Jane Marks Toby Dylan Hocking
Microorganisms are found in almost every environment, including the soil,
water, air, and inside other organisms, like animals and plants. While some
microorganisms cause diseases, most of them help in biological processes such
as decomposition, fermentation and nutrient cycling. A lot of research has gone
into studying microbial communities in various environments and how their
interactions and relationships can provide insights into various diseases.
Co-occurrence network inference algorithms help us understand the complex
associations of micro-organisms, especially bacteria. Existing network
inference algorithms employ techniques such as correlation, regularized linear
regression, and conditional dependence, which have different hyper-parameters
that determine the sparsity of the network. Previous methods for evaluating the
quality of the inferred network include using external data, and network
consistency across sub-samples, both which have several drawbacks that limit
their applicability in real microbiome composition data sets. We propose a
novel cross-validation method to evaluate co-occurrence network inference
algorithms, and new methods for applying existing algorithms to predict on test
data. Our empirical study shows that the proposed method is useful for
hyper-parameter selection (training) and comparing the quality of the inferred
networks between different algorithms (testing).