  Microbiota profiles measure the structure of microbial communities in a
defined environment (known as microbiomes). In the past decade, microbiome
research has focused on health applications as a result of which the gut
microbiome has been implicated in the development of a broad range of diseases
such as obesity, inflammatory bowel disease, and major depressive disorder. A
key goal of many microbiome experiments is to characterise or describe the
microbial community. High-throughput sequencing is used to generate microbiota
profiles, but data gathered via this method are extremely challenging to
analyse, as the data violate multiple strong assumptions of standard models.
Rough Set Theory (RST) has weak assumptions that are less likely to be
violated, and offers a range of attractive tools for extracting knowledge from
complex data. In this paper we present the first application of RST for
characterising microbiomes. We begin with a demonstrative benchmark microbiota
profile and extend the approach to gut microbiomes gathered from depressed
subjects to enable knowledge discovery. We find that RST is capable of
excellent characterisation of the gut microbiomes in depressed subjects and
identifying previously undescribed alterations to the microbiome-gut-brain
axis. An important aspect of the application of RST is that it provides a
possible solution to an open research question regarding the search for an
optimal normalisation approach for microbiome census data, as one does not
currently exist.
