Sometimes, stronger measures are necessary: for example, the American Gut Project received samples from self-reported healthy individuals that contained levels of gammaproteobacteria beyond anything previously observed in healthy populations (although similar to those observed in samples from ICU patients [manuscript in prep]). It was determined that these blooms likely stemmed from the shipping conditions for some samples. The blooms can be bioinformatically subtracted from the dataset [manuscript in prep] by removing organisms observed to bloom (as has been observed to happen in storage [unpublished observations]). As a result, any meta-analysis that leverages the American Gut data must perform this same subtraction in order to equalize bias that the filter introduces. Ongoing studies of stability [41, 42] are explicitly exploring the effect of different types of storage effects so that they can be controlled for as necessary in the future.
