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 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

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

Objective There are altered mucosal functions in irritable bowel syndrome with diarrhoea (IBS-D); ~30% of patients with IBS-D have abnormal bile acid (BA) metabolism (ABAM) and diarrhoea (summarised as BAD). Aim To compare biochemical parameters, gastrointestinal and colonic transit, rectal sensation and pathobiological mechanisms in IBS-D without ABAM and in BAD (serum 7C4>52 ng/mL). Design In patients with Rome III criteria of IBS-D, we compared biochemical features, colonic transit, rectal sensation, deep genotype of five BA-related genes, ileal and colonic mucosal mRNA (differential expression (DE) analysis) and stool dysbiosis (including functional analysis of microbiome). Results in BAD were compared with IBS-D without ABAM. Results Compared with 161 patients with IBS-D without ABAM, 44 patients with BAD had significantly faster colonic transit, lower microbial alpha diversity, different compositional profile (beta diversity) and higher Firmicutes to Bacteroidetes ratio with evidence of decreased expression of bile acid thiol ligase (involved in transformation of primary to secondary BAs) and decreased sulfatases. In BAD (compared with IBS-D without ABAM), terminal ileal biopsies showed downregulation of SLC44A5 (a BA transporter), and ascending colon biopsies showed upregulation in barrier-weakening genes (CLDN2), serine protease inhibitors, immune activation, cellular differentiation and a cellular transporter (FABP6; BA binding). No DE of genes was documented in descending colon biopsies. The two groups had similar rectal sensation. Conclusion Though sharing clinical symptoms with IBS-D, BAD is associated with biological differences and mechanisms that have potential to enhance diagnosis and treatment targeting barrier dysfunction, inflammatory and microbial changes.

Comparison of biochemical, microbial and mucosal mRNA expression in bile acid diarrhoea and irritable bowel syndrome with diarrhoea

The family-wise error rate (FWER) has been widely used in genome-wide association studies. With the increasing availability of functional genomics data, it is possible to increase the detection power by leveraging these genomic functional annotations. Previous efforts to accommodate covariates in multiple testing focus on the false discovery rate control while covariate-adaptive FWER-controlling procedures remain under-developed. Here we propose a novel covariate-adaptive FWER-controlling procedure that incorporates external covariates which are potentially informative of either the statistical power or the prior null probability. An efficient algorithm is developed to implement the proposed method. We prove its asymptotic validity and obtain the rate of convergence through a perturbation-type argument. Our numerical studies show that the new procedure is more powerful than competing methods and maintains robustness across different settings. We apply the proposed approach to the UK Biobank data and analyze 27 traits with 9 million single-nucleotide polymorphisms tested for associations. Seventy-five genomic annotations are used as covariates. Our approach detects more genome-wide significant loci than other methods in 21 out of the 27 traits.

Huijuan Zhou

Papers

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

Covariate Adaptive Family-wise Error Rate Control for Genome-Wide Association Studies