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Abstract MotivationEmerging omics technologies have introduced a two-way grouping structure in multiple testing, as seen in single-cell omics data, where the features can be grouped by either genes or cell types. Traditional multiple testing methods have limited ability to exploit such two-way grouping structure, leading to potential power loss. ResultsWe propose a new 2D Group Benjamini–Hochberg (2dGBH) procedure to harness the two-way grouping structure in omics data, extending the traditional one-way adaptive GBH procedure. Using both simulated and real datasets, we show that 2dGBH effectively controls the false discovery rate across biologically relevant settings, and it is more powerful than the BH or q-value procedure and more robust than the one-way adaptive GBH procedure. Availability and implementation2dGBH is available as an R package at: https://github.com/chloelulu/tdGBH. The analysis code and data are available at: https://github.com/chloelulu/tdGBH-paper. 

Abstract SummaryDue to the sparsity and high dimensionality, microbiome data are routinely summarized into pairwise distances capturing the compositional differences. Many biological insights can be gained by analyzing the distance matrix in relation to some covariates. A microbiome sampling method that characterizes the inter-sample relationship more reproducibly is expected to yield higher statistical power. Traditionally, the intraclass correlation coefficient (ICC) has been used to quantify the degree of reproducibility for a univariate measurement using technical replicates. In this work, we extend the traditional ICC to distance measures and propose a distance-based ICC (dICC). We derive the asymptotic distribution of the sample-based dICC to facilitate statistical inference. We illustrate dICC using a real dataset from a metagenomic reproducibility study. Availability and implementationdICC is implemented in the R CRAN package GUniFrac. Supplementary informationSupplementary data are available at Bioinformatics online. 

Abstract 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. Current microbiome studies frequently generate correlated samples from different microbiome sampling schemes such as spatial and temporal sampling. In the past decade, a number of DAA tools for correlated microbiome data (DAA-c) have been proposed. Disturbingly, different DAA-c tools could sometimes produce quite discordant results. To recommend the best practice to the field, we performed the first comprehensive evaluation of existing DAA-c tools using real data-based simulations. Overall, the linear model-based methods LinDA, MaAsLin2 and LDM are more robust than methods based on generalized linear models. The LinDA method is the only method that maintains reasonable performance in the presence of strong compositional effects. 

It is well known that the microbiome data are ridden with outliers and have heavy distribution tails, but the impact of outliers and heavy-tailedness has yet to be examined systematically. This paper investigates the impact of outliers and heavy-tailedness on differential abundance analysis (DAA) using the linear models for the differential abundance analysis (LinDA) method and proposes effective strategies to mitigate their influence. The presence of outliers and heavy-tailedness can significantly decrease the power of LinDA. We investigate various techniques to address outliers and heavy-tailedness, including generalizing LinDA into a more flexible framework that allows for the use of robust regression and winsorizing the data before applying LinDA. Our extensive numerical experiments and real-data analyses demonstrate that robust Huber regression has overall the best performance in addressing outliers and heavy-tailedness. 

Abstract 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. 

Abstract Differential abundance analysis is at the core of statistical analysis of microbiome data. The compositional nature of microbiome sequencing data makes false positive control challenging. Here, we show that the compositional effects can be addressed by a simple, yet highly flexible and scalable, approach. The proposed method, LinDA, only requires fitting linear regression models on the centered log-ratio transformed data, and correcting the bias due to compositional effects. We show that LinDA enjoys asymptotic FDR control and can be extended to mixed-effect models for correlated microbiome data. Using simulations and real examples, we demonstrate the effectiveness of LinDA. 

Random forest is considered as one of the most successful machine learning algorithms, which has been widely used to construct microbiome-based predictive models. However, its use as a statistical testing method has not been explored. In this study, we propose “Random Forest Test” (RFtest), a global (community-level) test based on random forest for high-dimensional and phylogenetically structured microbiome data. RFtest is a permutation test using the generalization error of random forest as the test statistic. Our simulations demonstrate that RFtest has controlled type I error rates, that its power is superior to competing methods for phylogenetically clustered signals, and that it is robust to outliers and adaptive to interaction effects and non-linear associations. Finally, we apply RFtest to two real microbiome datasets to ascertain whether microbial communities are associated or not with the outcome variables. 

Abstract Summary PERMANOVA (permutational multivariate analysis of variance based on distances) has been widely used for testing the association between the microbiome and a covariate of interest. Statistical significance is established by permutation, which is computationally intensive for large sample sizes. As large-scale microbiome studies, such as American Gut Project (AGP), become increasingly popular, a computationally efficient version of PERMANOVA is much needed. To achieve this end, we derive the asymptotic distribution of the PERMANOVA pseudo-F statistic and provide analytical P-value calculation based on chi-square approximation. We show that the asymptotic P-value is close to the PERMANOVA P-value even under a moderate sample size. Moreover, it is more accurate and an order-of-magnitude faster than the permutation-free method MDMR. We demonstrated the use of our procedure D-MANOVA on the AGP dataset. Availability and implementation D-MANOVA is implemented by the dmanova function in the CRAN package GUniFrac. Supplementary information Supplementary data are available at Bioinformatics online.