  Microbial communities are diverse biological systems that include taxa from
across multiple kingdoms of life. Notably, interactions between bacteria and
fungi play a significant role in determining community structure. However,
these statistical associations across kingdoms are more difficult to infer than
intra-kingdom associations due to the nature of the data involved using
standard network inference techniques. We quantify the challenges of
cross-kingdom network inference from both a theoretical and practical viewpoint
using synthetic and real-world microbiome data. We detail the theoretical issue
presented by combining compositional data sets drawn from the same environment,
e.g. 16S and ITS sequencing of a single set of samples, and survey common
network inference techniques for their ability to handle this error. We then
test these techniques for the accuracy and usefulness of their intra- and
inter-kingdom associations by inferring networks from a set of simulated
samples for which a ground-truth set of associations is known. We show that
while two methods mitigate the error of cross-kingdom inference, there is
little difference between techniques for key practical applications including
identification of strong correlations and identification of possible keystone
taxa (i.e. hub nodes in the network). Furthermore, we identify a signature of
the error caused transkingdom network inference and demonstrate that it appears
in networks constructed using real-world environmental microbiome data.
