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 elegantly by a simple, yet highly flexible and scalable approach The proposed method, LinDA, only requires fitting linear regression models on the centered log-ratio trans- formed data, and correcting the bias due to compositional effects We show that LinDA enjoys asymptotic FDR control property and can be extended to mixed-effect models for correlated microbiome data Using simulations and real examples, we demonstrate the effectiveness of LinDA

LinDA: Linear Models for Differential Abundance Analysis of Microbiome Compositional Data

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.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.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. Video Abstract. 

https://microbiomejournal.biomedcentral.com/counter/pdf/10.1186/s40168-022-01320-0

A comprehensive evaluation of microbial differential abundance analysis methods: current status and potential solutions

/pdf/microbiotaprocess-a-comprehensive-r-package-for-deep-mining-3qhhmisz.pdf

MicrobiotaProcess: A comprehensive R package for deep mining microbiome

Abstract Nonalcoholic steatohepatitis (NASH) has been linked with the gut-liver axis. Here, we investigate the potential for repurposing disulfiram (DSF), a drug commonly used to treat chronic alcoholism, for NASH. Using a mouse model, we show that DSF ameliorates NASH in a gut microbiota-dependent manner. DSF modulates the gut microbiota and directly inhibits the growth of Clostridium . Administration of Clostridium abolishes the ameliorating effects of DSF on NASH. Mechanistically, DSF reduces Clostridium -mediated 7α-dehydroxylation activity to suppress secondary bile acid biosynthesis, which in turn activates hepatic farnesoid X receptor signaling to ameliorate NASH. To assess the effect of DSF on human gut microbiota, we performed a self-controlled clinical trial (ChiCTR2100048035), including 23 healthy volunteers who received 250 mg-qd DSF for 7 days. The primary objective outcomes were to assess the effects of the intervention on the diversity, composition and functional profile of gut microbiota. The pilot study shows that DSF also reduces Clostridium -mediated 7α-dehydroxylation activity. All volunteers tolerated DSF well and there were no serious adverse events in the 7-day follow-up period. Transferring fecal microbiota obtained from DSF-treated humans into germ-free mice ameliorates NASH. Collectively, the observations of similar ameliorating effects of DSF on mice and humans suggest that DSF ameliorates NASH by modulating the gut microbiota and bile acid metabolism. 

/pdf/disulfiram-ameliorates-nonalcoholic-steatohepatitis-by-3m8gp2i9.pdf

Disulfiram ameliorates nonalcoholic steatohepatitis by modulating the gut microbiota and bile acid metabolism

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. 

/pdf/linda-linear-models-for-differential-abundance-analysis-of-16yulhr6.pdf

LinDA: linear models for differential abundance analysis of microbiome compositional data

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.

Robust Differential Abundance Analysis of Microbiome Sequencing Data

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LinDA: linear models for differential abundance analysis of microbiome compositional data

Robust Differential Abundance Analysis of Microbiome Sequencing Data