Of particular relevance are studies on antimicrobial resistance 4, 6, 7, 9– 11, as well as studies in other areas, such as changes in gut flora in different human communities 12 or through the ingestion of probiotics 13.įor robust reporting of research, it is important to use statistics correctly to ascertain whether there is evidence that the observed changes in the taxonomic or genetic composition of a community reflect the factors under study, as opposed to merely reflecting random variation between the samples. This has broad application across biomedical and environmental science, and in particular, allows for detection of changes in communities or the genes that they carry in the face of biological 5, chemical 6 or environmental 7, 8 factors. New high throughput technologies, including high throughput sequencing 1, or qPCR arrays 2, allow for the characterisation of microbial communities 3, or the genes that they carry 4. Our method has broad uses for statistical testing and experimental design in research on changing microbiomes, including studies on antimicrobial resistance.īacteria live in complex communities, whether in water, in soil, or on larger organisms, as the microbiota of organs such as the gut or the skin. This is a much larger data set and is used to verify the new method. The third is to published data on bacterial communities surrounding rice crops. While the observed differences are not significant, we show that a minimum group size of eight sheep would provide sufficient power to observe significance of similar changes in further experiments. The second is to new data on seasonality in bacterial communities and ARGs in hooves from four sheep. The first is to published data on bacterial communities and antimicrobial resistance genes (ARGs) in the environment we show that there are significant changes in both ARG and community composition. We present three example analyses in the area of antimicrobial resistance. It can also be used for experimental design, to estimate how many samples to use in future experiments, again with the advantage of being universally most powerful.
Our method goes beyond previous published work in being universally most powerful, thus better able to detect statistically significant differences, and through being more reliable for smaller sample sizes. We describe a method to test the statistical significance of differences in bacterial population or gene composition, applicable to metagenomic or quantitative polymerase chain reaction data. High throughput genomics technologies are applied widely to microbiomes in humans, animals, soil and water, to detect changes in bacterial communities or the genes they carry, between different environments or treatments.
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