Abstract A common response to microbiome sample variation is to use clustering algorithms to reduce complex and variable datasets to a smaller number of ‘types’ (e.g. enterotypes for gut samples, or pulmotypes for lung samples). In light of recent analyses showing distinct clustering solutions to in principle similar datasets, we examine the extent to which clustering solutions are dependent on researcher choices of algorithm and dataset, using cystic fibrosis (CF) sputum microbiome data as a model system. Followi…