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BIRS Workshop Lecture Videos

Analyzing matched sets of microbiome data using the LDM and PERMANOVA (Presenter: Glen Satten) Hu, Yijuan


Matched data arise frequently in microbiome studies. For example, we may collect samples pre and post treatment from a set of subjects, or matched case-control subjects who were matched on important confounding factors. However, there is a lack of methods to provide both a global test of microbiome effect and tests of individual operational taxonomic units (OTUs) in a unified manner, while accommodating complex data such as those with unbalanced sample sizes per set, confounders varying within a set, and continuous traits of interest. PERMANOVA is a commonly used distance-based method for testing the global hypotheses of any microbiome effect. We have also developed the linear decomposition model (LDM) that includes the global test and tests of individual OTU effects while controlling the false discovery rate (FDR). Here we present a strategy that can be used in the LDM and PERMANOVA for analyzing matched-set data. We propose to include set indicators as covariates so as to constrain comparisons between samples within a set. We also propose to permute covariates within each set which can account for exchangeable sample correlations. Additionally, the flexible nature of the LDM and PERMANOVA allows discrete or continuous variables (e.g., clinical outcomes) to be tested, within-set confounders to be adjusted, and unbalanced data to be fully exploited. Our simulations indicate that the proposed strategy outperformed alternative strategies in a wide range of scenarios. Using simulation, we also explored optimal designs for matched-set studies. The flexibility of the LDM and PERMANOVA for a variety of matched-set microbiome data is illustrated by the analysis of data from two microbiome studies.

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