  Current advances in next generation sequencing techniques have allowed
researchers to conduct comprehensive research on microbiome and human diseases,
with recent studies identifying associations between human microbiome and
health outcomes for a number of chronic conditions. However, microbiome data
structure, characterized by sparsity and skewness, presents challenges to
building effective classifiers. To address this, we present an innovative
approach for distance-based classification using mixture distributions (DCMD).
The method aims to improve classification performance when using microbiome
community data, where the predictors are composed of sparse and heterogeneous
count data. This approach models the inherent uncertainty in sparse counts by
estimating a mixture distribution for the sample data, and representing each
observation as a distribution, conditional on observed counts and the estimated
mixture, which are then used as inputs for distance-based classification. The
method is implemented into a k-means and k-nearest neighbours framework and we
identify two distance metrics that produce optimal results. The performance of
the model is assessed using simulations and applied to a human microbiome
study, with results compared against a number of existing machine learning and
distance-based approaches. The proposed method is competitive when compared to
the machine learning approaches and showed a clear improvement over commonly
used distance-based classifiers. The range of applicability and robustness make
the proposed method a viable alternative for classification using sparse
microbiome count data.
