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Microbiome data are subject to experimental bias that is caused by DNA extraction and PCR amplification, among other sources, but this important feature is often ignored when developing statistical methods for analyzing microbiome data. McLaren, Willis, and Callahan (2019) proposed a model for how such biases affect the observed taxonomic profiles; this model assumes the main effects of bias without taxon-taxon interactions. Our newly developed method for testing the differential abundance of taxa, LOCOM, is the first method to account for experimental bias and is robust to the main effect biases. However, there is also evidence for taxon-taxon interactions. In this report, we formulated a model for interaction biases and used simulations based on this model to evaluate the impact of interaction biases on the performance of LOCOM as well as other available compositional analysis methods. Our simulation results indicate that LOCOM remained robust to a reasonable range of interaction biases. The other methods tend to have an inflated FDR even when there were only main effect biases. LOCOM maintained the highest sensitivity even when the other methods could not control the FDR. We thus conclude that LOCOM outperforms the other methods for compositional analysis of microbiome data considered here.

Background: Finding microbiome associations with possibly censored survival times is an important problem, especially as specific taxa could serve as biomarkers for disease prognosis or as targets for therapeutic interventions. The two existing methods for survival outcomes, MiRKAT-S and OMiSA, are restricted to testing associations at the community level and do not provide results at the individual taxon level. An ad hoc approach testing each taxon with a survival outcome using the Cox proportional hazard model may not perform well in the microbiome setting with sparse count data and small sample sizes., Methods: We have previously developed the linear decomposition model (LDM) for testing continuous or discrete outcomes that unifies community-level and taxon-level tests into one framework. Here we extend the LDM to test survival outcomes. We propose to use the Martingale residuals or the deviance residuals obtained from the Cox model as continuous covariates in the LDM. We further construct tests that combine the results of analyzing each set of residuals separately. Finally, we extend PERMANOVA, the most commonly used distance-based method for testing community-level hypotheses, to handle survival outcomes in a similar manner., Results: Using simulated data, we showed that the LDM-based tests preserved the false discovery rate for testing individual taxa and had good sensitivity. The LDM-based community-level tests and PERMANOVA-based tests had comparable or better power than MiRKAT-S and OMiSA. An analysis of data on the association of the gut microbiome and the time to acute graft-versus-host disease revealed several dozen associated taxa that would not have been achievable by any community-level test, as well as improved community-level tests by the LDM and PERMANOVA over those obtained using MiRKAT-S and OMiSA., Conclusions: Unlike existing methods, our new methods are capable of discovering individual taxa that are associated with survival times, which could be of important use in clinical settings., Competing Interests: The authors have declared that no competing interests exist.

Compositional analysis is based on the premise that a relatively small proportion of taxa are differentially abundant, while the ratios of the relative abundances of the remaining taxa remain unchanged. Most existing methods use log-transformed data, but log-transformation of data with pervasive zero counts is problematic, and these methods cannot always control the false discovery rate (FDR). Further, high-throughput microbiome data such as 16S amplicon or metagenomic sequencing are subject to experimental biases that are introduced in every step of the experimental workflow. McLaren et al. [ eLife 8, e46923 (2019)] have recently proposed a model for how these biases affect relative abundance data. Motivated by this model, we show that the odds ratios in a logistic regression comparing counts in two taxa are invariant to experimental biases. With this motivation, we propose logistic compositional analysis (LOCOM), a robust logistic regression approach to compositional analysis, that does not require pseudocounts. Inference is based on permutation to account for overdispersion and small sample sizes. Traits can be either binary or continuous, and adjustment for confounders is supported. Our simulations indicate that LOCOM always preserved FDR and had much improved sensitivity over existing methods. In contrast, analysis of composition of microbiomes (ANCOM) and ANCOM with bias correction (ANCOM-BC)/ANOVA-Like Differential Expression tool (ALDEx2) had inflated FDR when the effect sizes were small and large, respectively. Only LOCOM was robust to experimental biases in every situation. The flexibility of our method for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. Our R package LOCOM is publicly available.

Background: Evidence suggests that intravaginal practices (IVPs) women use to cleanse their vagina or enhance sexual pleasure may be associated with unhealthy changes in the vaginal microbiome (VM). However, the effects of these practices in postmenopausal women are unknown., Objectives: The objective of this pilot study was to characterize the VM communities of postmenopausal women, identify types and frequency of IVPs, and explore associations between the VM and IVPs in postmenopausal women., Methods: We analyzed the VM data of 21 postmenopausal women in Atlanta, Georgia, from vaginal swabs collected at a routine gynecological visit. 16S rRNA gene sequencing in the V3-V4 region was used to characterize the VM. In addition, we described the IVPs of these women, identified by using our newly developed instrument: the Vaginal Cleansing Practices Questionnaire. The associations between the VM and IVPs were explored by comparing the alpha diversities, beta diversities, and the relative abundances at both the community level and individual genus level., Results: The most abundant known bacterial genus found in the VM samples was Lactobacillus (35.7%), followed by Prevotella (21.4%). Eleven women (52%) reported using at least one type of IVP since menopause. The most common type of IVP was soap and water to clean inside the vagina. The use of IVPs was not associated with any alpha diversity metric, including Shannon index, inverse Simpson index, and Chao1 index; beta diversity metric, including Bray-Curtis and Jaccard distances; nor relative abundances at the community and individual genus level. Sociodemographic factors were also not associated with any alpha diversity metric., Discussion: Clinicians must assess IVPs and other vaginal and sexual hygiene practices of women of all ages to educate and promote healthy behaviors. More than half of the postmenopausal women in this pilot study use IVPs. Understanding the reasoning behind participants' use of IVPs and their perceptions of the possible effects of these practices will require further research. Although the small sample did not show associations with the VM, more extensive studies are warranted., Competing Interests: The authors have no conflicts of interest to report., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)