The advent of large-scale microbiome studies affords newfound analytical opportunities to understand how these communities of microbes operate and relate to their environment. However, the analytical methodology needed to model microbiome data and integrate them with other data constructs remains nascent. This emergent analytical toolset frequently ports over techniques developed in other multi-omics investigations, especially the growing array of statistical and computational techniques for integrating and representing data through networks. While network analysis has emerged as a powerful approach to modeling microbiome data, oftentimes by integrating these data with other types of omics data to discern their functional linkages, it is not always evident if the statistical details of the approach being applied are consistent with the assumptions of microbiome data or how they impact data interpretation. In this review, we overview some of the most important network methods for integrative analysis, with an emphasis on methods that have been applied or have great potential to be applied to the analysis of multi-omics integration of microbiome data. We compare advantages and disadvantages of various statistical tools, assess their applicability to microbiome data, and discuss their biological interpretability. We also highlight on-going statistical challenges and opportunities for integrative network analysis of microbiome data.
Coffee contains bioactive compounds with anti-inflammatory properties, and its consumption may reduce c-reactive protein (CRP) levels, a biomarker of chronic inflammation. A previous meta-analysis reported no overall association between blood CRP level and coffee consumption by modeling the coffee consumption in categories, with substantial heterogeneity. However, the coffee cup volume was not considered. We conducted a systematic review and dose–response meta-analysis investigating the association between coffee consumption and CRP levels reported in previous observational studies. A dose–response meta-analysis was conducted by mixed-effects meta-regression models using the volume of coffee consumed as metric. Eleven studies from three continents were identified using the PubMed database, totaling 61,047 participants. Three studies with the largest sample sizes observed a statistically significant association between coffee and CRP levels, which was inverse among European and United States (US) women and Japanese men (1.3–5.5% decrease in CRP per 100 mL of coffee consumed) and positive among European men (2.2% increase). Other studies showed no statistically significant associations. When all studies were combined in the dose–response meta-analysis, no statistically significant associations were observed among all participants or when stratified by gender or geographic location, reflecting the conflicting associations reported in the included studies. Further studies are warranted to explore these inconsistent associations.
In biomedical applications, it is often of interest to test the alternative hypothesis that the means of three or more groups follow a strictly monotonic trend such as µ1 > µ2 > µ3 against the null hypothesis that the group means are either equal or unequal but are not monotonic. This is useful, for example, for detecting biomarkers whose level in healthy, low-risk cancer and aggressive cancer subjects increases or decreases throughout the three groups. Various trend tests are available for testing monotonic alternatives. However, existing methods are designed for a highly restrictive null hypothesis where all group means are equal, which represents a special case of the null space in our problem. We demonstrate that these methods fail to control type I error when the group means may be unequal under the null. To test this broader null hypothesis, develop a greedy testing method which has an intuitive interpretation related to two-sample t tests. We show both theoretically and through simulations that the proposed method effectively controls type 1 error throughout the entire null space and achieves higher power than a naive implementation of multiple t-tests. We illustrate the greedy trend test method in real data to study microbial associations with parasite-related pathology in zebrafish.
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