Abstract
In microbiome studies, addressing the unique characteristics of sequence data-such as compositionality, zero inflation, overdispersion, high dimensionality, and non-normality-is crucial for accurate analysis. In addition, integrating experimental design elements into microbiome data analysis is important for understanding how factors such as treatment, time, and interactions affect microbial abundance. To achieve these objectives, we developed a new method that combines generalized linear models (GLMs) with ANOVA simultaneous component analysis (ASCA), which we term GLM-ASCA. This method aims to improve microbiome analysis by providing a more comprehensive understanding of differential abundance patterns in response to experimental conditions. GLM-ASCA models the unique characteristics of microbiome sequence data with GLMs and uses ASCA to effectively separate the effects of different experimental factors on microbial abundance. We evaluated GLM-ASCA using simulated data and subsequently applied it to real data to analyze the effect of nitrogen deficiency on root microbiome recruitment in tomato. Simulation studies demonstrated the effectiveness of GLM-ASCA in analyzing microbiome data in complex experimental designs, and the real-data application revealed valuable insights into the dynamics of microbial communities under nitrogen starvation, including the identification of beneficial bacterial species that promote tomato (Solanum lycopersicum) growth and health through nitrogen fixation.