Gut microbiome has recently gained considerable attention, and its composition and diversity has been linked to several aspects of health and disease. Intervention studies are often used to investigate how the microbiome is affected by external factors such as treatments and diets. Data from such trials need to be analysed by multivariate ANOVA-like methods.
Microbiome data have some special features. The raw data typically consist of millions of DNA reads, which are converted to taxa counts or abundances through advanced bioinformatic pipelines. The data is zero-inflated, and a high number of rare taxa are usually removed before further analysis. The statistical analysis can be performed on either sequence counts, abundances, transformed abundances or distances.
We have compared the chemometric-based ANOVA-Simultaneous Component Analysis (ASCA)  to a range of other ANOVA-like methods that are frequently used for analysing microbial data, including: PERMANOVA , ANOSIM , SIMPER , ALDEx2 , ANCOM , LEfSe  and 50-50 MANOVA .
Comparisons were done using simulated data and five real dietary intervention studies. We have evaluated the methods abilities to detect community-level (multivariate) effects, as well as their abilities to identify differentially abundant bacterial groups. We report on the overall agreement between the methods, to assess to what extent the choice of method affects the results.