A mixed effect similarity matrix regression model (SMRmix) for integrating multiple microbiome datasets at the...

Recent studies have highlighted the importance of the human microbiota in health and disease. However, in many areas of research, individual microbiome studies often provide inconsistent results due to limited sample sizes and the heterogeneity in study populations and experimental procedures. This inconsistency underscores the need for integrative analysis of multiple microbiome datasets. Despite the critical need, statistical methods that incorporate multiple microbiome datasets and account for study heterogeneity are not available in the literature. To address this, we propose a mixed effect similarity matrix regression (SMRmix) approach for identifying community-level microbiome shifts associated with outcomes. SMRmix has a close connection with the microbiome kernel association test, one of the most popular approaches for such a task, but it is only applicable when we have a single study. SMRmix enables researchers to consolidate findings from diverse microbiome studies. Through extensive simulations, we show that SMRmix maintains well-controlled Type I error rates and achieves higher power than competing methods. We further demonstrate its utility on two real-world datasets-17 HIV gut dysbiosis studies and 11 colorectal cancer studies-showing that SMRmix provides consistent results on community-level shifts in both applications.