Human microbial research has become increasingly popular in biomedical areas due to the importance of role of human microbiome in human health. One purpose of studying human microbiome is to detect differentially abundant features from a limited group of subjects across biological conditions. Metagenomic analyses of the human microbial communities are extensively used for biomedical applications due to its reliable and evident comparative discoveries across more than one metagenomes when multiple communities are taken into consideration. Next-generation sequencing technology helps to detect taxonomic compositions of specific features/species contained in human microbial communities. Statistical analysis often starts by generating the Operational Taxonomic Units (OTUs) using taxonomic compositions to classify groups of closely associated human microbiomes. Oftentimes, the counts of features are observed as matched count data with excess zeros. Such data lead some differential abundance analysis methods to apply Zero-Inflated Poisson (ZIP) or Zero-Inflated Negative Binomial (ZINB) regression for modeling the microbial abundance. However, over-dispersion as well as within-subject variation and correlation of matched count data render the standard ZIP and ZINB regression inadequate. To account for the inherent within-subject variation and correlation, independent random effect terms are commonly included in the regressions. Therefore, a robust method that accounts the effect of matched samples and correlated random effects while considering over-dispersion and excess zeros of count data is need for statistical analysis. In this paper, a statistical method, the two-part correlated ZINB model with correlated random effects (cZINB), is proposed for testing the matched samples with repeated measurements.

The role of individual soil microorganisms changes over the course of a plant's life - microorganisms that have no discernable role at one developmental stage may affect the plant later in its growth. Traditional analysis of the soil microbiome, which has focused principally on the relative abundances (RA) of individual organisms, may be incomplete, as underlying differences in population size cannot be addressed. We conducted a metagenomic analysis of soil microorganisms from various maize (Zea mays L.) fields at two depths, accompanied by crop yield components, to provide insight into influences of edaphic microbes on maize productivity under commercial maize production systems in Missouri. This study assesses the influence of fungi and bacteria, not only in terms of RA, but also in their estimated absolute abundances (EAA), derived by combining the results of Illumina HiSeq sequencing data and phospholipid fatty acid abundance data. Significant interactions were identified between maize yield components and soil microbes at critical developmental states. Most interactions between fungi and yield components were negative, with notable exceptions. Bacterial interactions were more complex, with most interactions during early ear development identified as positive, and most interactions during tasseling identified as negative. In addition to the effects that microbial populations have on yield, plant populations reciprocally changed the microbial community. Plant developmental state was the greatest predictor of bacteria, with the microbial communities present during the active growing season being most similar to each other, whereas the preplant microbiome and post-reproductive microbiome being most similar to each other. Fungal communities were primarily dependent on location.