## 4 Discussion
### Detection of Minor Compositional Outliers  
BRaVO, EdgeR, and DESeq2 have different features for the composition of detected outliers. EdgeR tends to detect zero-inflated features that are not observed in either of the comparison groups. In the animal-based diet group, 8 outliers detected by EdgeR were ultra-rare microorganisms that were not observed in the diet group. Therefore, this method may be affected by sampling biases that can produce non-observed features. In contrast, DESeq2 tends to detect outliers with more than 1% composition in the taxonomic abundance. Therefore, this method may be required for some pre-processing procedures for non-observed features in the taxonomic abundance. Although BRaVO can detect compositional changes in minor microorganisms with lower compositions, as compared to the other two approaches, our method is less susceptible to features with non-observed values. Therefore, our approach is effective to assess the compositional changes including rare components, without being affected by noise factors such as sampling biases.  
We should consider that the taxonomic abundance data in the microbiome have the features of the composition data. The abundance data are subjected to a constant sum constraint. Therefore, the compositional changes of certain microbial species can affect the compositions of other microbial species. Our approach deals with this technical issue by using a centered log-ratio transformation. Our method can also capture the compositional changes of rare species by calculating the relative variation value in the taxonomic abundance data. In addition, the taxonomic abundance data from metagenome sequencing often include many zero values. BRaVO supports the existence of zero values in taxonomic abundance tables without changing the composition ratios, using the multiplicative replacement. Unfortunately, changes in features with rare compositions are often underestimated. Outlier detection for compositional variations of minor microbial species might shed light toward understanding microbial communities and their complex relationships.  
The following are available online at [[http://www.mdpi.com/2076-3417/9/7/1355/s1](http://www.mdpi.com/2076-3417/9/7/1355/s1)]([http://www.mdpi.com/2076-3417/9/7/1355/s1](http://www.mdpi.com/2076-3417/9/7/1355/s1)), Table S1: Summary of detected outliers in the animal-based diet, Table S2: Summary of detected outliers in the plant-based diet.  
Conceptualization, K.I. and S.A.; Methodology, K.I.; Investigation, K.I.; Resources, K.I.; Writing-Original draft preparation, K.I.; writing-review and editing, S.A.; visualization, K.I.; project administration, S.A.  
We thank our lab members for helpful discussions. Especially, we appreciate [PERSON] for his expert advice.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} NGS & Next-generation sequencing \\ OTUs & Operational taxonomic units \\ rRNA & ribosomal RNA \\ TMM & Trimmed mean of M values \\ HKGs & House keeping genes \\ RV & Relative variation \\ \end{tabular}  
GMPR  
Geometric mean of pairwise ratios  
Relative log expression normalization  
TSS  
Total sum scaling  
CLR  
Centered log-ratio  
NIQR  
Normalized interquartile range  
IQR  
Interquantile range  
FDR  
False discovery rate