Bayesian mixture analysis for metagenomic community profiling.

Sofia Morfopoulou; Vincent Plagnol

We use a Monte Carlo Markov Chain (MCMC) to explore the set of present species of size 2 S − 1, where S is the total number of potential species. In practice we observe that S can be greater than 1,000. The MCMC must explore the state-space in a clinically useful timespan. Therefore we reduce the size of the state-space, by decreasing the number of S species to the low hundreds. We achieve this by fitting a mixture model . CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/007476 doi: bioRxiv preprint with S categories, considering all potential species simultaneously. Post fitting, we retain only the species categories that are not empty, that is categories that have at least one read assigned to them.