Background Differential abundance analysis (DAA) is one central statistical task in microbiome data analysis. A robust and powerful DAA tool can help identify highly confident microbial candidates for further biological validation. Numerous DAA tools have been proposed in the past decade addressing the special characteristics of microbiome data such as zero inflation and compositional effects. Disturbingly, different DAA tools could sometimes produce quite discordant results, opening to the possibility of cherry-picking the tool in favor of one’s own hypothesis. To recommend the best DAA tool or practice to the field, a comprehensive evaluation, which covers as many biologically relevant scenarios as possible, is critically needed. Results We performed by far the most comprehensive evaluation of existing DAA tools using real data-based simulations. We found that DAA methods explicitly addressing compositional effects such as ANCOM-BC, Aldex2, metagenomeSeq (fitFeatureModel), and DACOMP did have improved performance in false-positive control. But they are still not optimal: type 1 error inflation or low statistical power has been observed in many settings. The recent LDM method generally had the best power, but its false-positive control in the presence of strong compositional effects was not satisfactory. Overall, none of the evaluated methods is simultaneously robust, powerful, and flexible, which makes the selection of the best DAA tool difficult. To meet the analysis needs, we designed an optimized procedure, ZicoSeq, drawing on the strength of the existing DAA methods. We show that ZicoSeq generally controlled for false positives across settings, and the power was among the highest. Application of DAA methods to a large collection of real datasets revealed a similar pattern observed in simulation studies. Conclusions Based on the benchmarking study, we conclude that none of the existing DAA methods evaluated can be applied blindly to any real microbiome dataset. The applicability of an existing DAA method depends on specific settings, which are usually unknown a priori. To circumvent the difficulty of selecting the best DAA tool in practice, we design ZicoSeq, which addresses the major challenges in DAA and remedies the drawbacks of existing DAA methods. ZicoSeq can be applied to microbiome datasets from diverse settings and is a useful DAA tool for robust microbiome biomarker discovery.

Background A third dose of measles-mumps-rubella vaccine (MMR3) is recommended in mumps outbreak scenarios, but the immune response and the need for widespread use of MMR3 remain uncertain. Herein, we characterized measles-specific immune responses to MMR3 in a cohort of 232 healthy subjects. Methods Serum and PBMCs were sampled at Day 0 and Day 28 after MMR3. Measles-specific binding and neutralizing antibodies were quantified in sera by ELISA and a microneutralization assay, respectively. PBMCs were stimulated with inactivated measles virus, and the release of cytokines/chemokines was assessed by a multiplex assay. Demographic variables of subjects were examined for potential correlations with immune outcomes. Results 95.69% and 100% of subjects were seropositive at Day 0 and Day 28, respectively. Antibody avidity significantly increased from 38.08% at Day 0 to 42.8% at Day 28 (p = 0.00026). Neutralizing antibodies significantly enhanced from 928.7 at Day 0 to 1,289.64 mIU/mL at Day 28 (p = 0.0001). Meanwhile, cytokine/chemokine responses remained largely unchanged. BMI was significantly correlated with the levels of inflammatory cytokines/chemokines. Conclusion Measles-specific humoral immune responses, but not cellular responses, were enhanced after MMR3 receipt, extending current understanding of immune responses to MMR3 and supporting MMR3 administration to seronegative or high-risk individuals.