  Motivation: Studies including more than one type of 'omics data sets are
becoming more prevalent. Integrating these data sets can be a way to solidify
findings and even to make new discoveries. However, integrating multi-omics
data sets is challenging. Typically, data sets are integrated by performing an
all-vs-all correlation analysis, where each feature of the first data set is
correlated to each feature of the second data set. However, all-vs-all
association testing produces unstructured results that are hard to interpret,
and involves potentially unnecessary hypothesis testing that reduces
statistical power due to false discovery rate (FDR) adjustment.
  Implementation: Here, we present the anansi framework, and accompanying R
package, as a way to improve upon all-vs-all association analysis. We take a
knowledge-based approach where external databases like KEGG are used to
constrain the all-vs-all association hypothesis space, only considering
pairwise associations that are a priori known to occur. This produces
structured results that are easier to interpret, and increases statistical
power by skipping unnecessary hypothesis tests. In this paper, we present the
anansi framework and demonstrate its application to learn metabolite-function
interactions in the context of host-microbe interactions. We further extend our
framework beyond pairwise association testing to differential association
testing, and show how anansi can be used to identify associations that differ
in strength or degree based on sample covariates such as case/control status.
  Availability: https://github.com/thomazbastiaanssen/anansi
