mbDecoda: a debiased approach to compositional data analysis for microbiome surveys.

Potentially pathogenic or probiotic microbes can be identified by comparing their abundance levels between healthy and diseased populations, or more broadly, by linking microbiome composition with clinical phenotypes or environmental factors. However, in microbiome studies, feature tables provide relative rather than absolute abundance of each feature in each sample, as the microbial loads of the samples and the ratios of sequencing depth to microbial load are both unknown and subject to considerable variation. Moreover, microbiome abundance data are count-valued, often over-dispersed and contain a substantial proportion of zeros. To carry out differential abundance analysis while addressing these challenges, we introduce mbDecoda, a model-based approach for debiased analysis of sparse compositions of microbiomes. mbDecoda employs a zero-inflated negative binomial model, linking mean abundance to the variable of interest through a log link function, and it accommodates the adjustment for confounding factors. To efficiently obtain maximum likelihood estimates of model parameters, an Expectation Maximization algorithm is developed. A minimum coverage interval approach is then proposed to rectify compositional bias, enabling accurate and reliable absolute abundance analysis. Through extensive simulation studies and analysis of real-world microbiome datasets, we demonstrate that mbDecoda compares favorably with state-of-the-art methods in terms of effectiveness, robustness and reproducibility.

msqrob2PTM: Differential Abundance and Differential Usage Analysis of MS-Based Proteomics Data at the Posttranslational Modification and Peptidoform Level.

In the era of open-modification search engines, more posttranslational modifications than ever can be detected by LC-MS/MS-based proteomics. This development can switch proteomics research into a higher gear, as PTMs are key in many cellular pathways important in cell proliferation, migration, metastasis, and aging. However, despite these advances in modification identification, statistical methods for PTM-level quantification and differential analysis have yet to catch up. This absence can partly be explained by statistical challenges inherent to the data, such as the confounding of PTM intensities with its parent protein abundance. Therefore, we have developed msqrob2PTM, a new workflow in the msqrob2 universe capable of differential abundance analysis at the PTM and at the peptidoform level. The latter is important for validating PTMs found as significantly differential. Indeed, as our method can deal with multiple PTMs per peptidoform, there is a possibility that significant PTMs stem from one significant peptidoform carrying another PTM, hinting that it might be the other PTM driving the perceived differential abundance. Our workflows can flag both differential peptidoform abundance (DPA) and differential peptidoform usage (DPU). This enables a distinction between direct assessment of differential abundance of peptidoforms (DPA) and differences in the relative usage of peptidoforms corrected for corresponding protein abundances (DPU). For DPA, we directly model the log2-transformed peptidoform intensities, while for DPU, we correct for parent protein abundance by an intermediate normalization step which calculates the log2-ratio of the peptidoform intensities to their summarized parent protein intensities. We demonstrated the utility and performance of msqrob2PTM by applying it to datasets with known ground truth, as well as to biological PTM-rich datasets. Our results show that msqrob2PTM is on par with, or surpassing the performance of, the current state-of-the-art methods. Moreover, msqrob2PTM is currently unique in providing output at the peptidoform level.

Benchmarking differential abundance analysis methods for correlated microbiome sequencing 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. 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.

Development of MetaXplore: An Interactive Tool for Targeted Metagenomic Analysis.

Over the last decades, the analysis of complex microbial communities by high-throughput sequencing of marker gene amplicons has become routine work for many research groups. However, the main challenges faced by scientists who want to make use of the generated sequencing datasets are the lack of expertise to select a suitable pipeline and the need for bioinformatics or programming skills to apply it. Here, we present MetaXplore, an interactive, user-friendly platform that enables the discovery and visualization of amplicon sequencing data. Currently, it provides a set of well-documented choices for downstream analysis, including alpha and beta diversity analysis, taxonomic composition, differential abundance analysis, identification of the core microbiome within a population, and biomarker analysis. These features are presented in a user-friendly format that facilitates easy customization and the generation of publication-quality graphics. MetaXplore is implemented entirely in the R language using the Shiny framework. It can be easily used locally on any system with R installed, including Windows, Mac OS, and most Linux distributions, or remotely via a web server without bioinformatic expertise. It can also be used as a framework for advanced users who can modify and expand the tool.

Statistical challenges in longitudinal microbiome data analysis.

The microbiome is a complex and dynamic community of microorganisms that co-exist interdependently within an ecosystem, and interact with its host or environment. Longitudinal studies can capture temporal variation within the microbiome to gain mechanistic insights into microbial systems; however, current statistical methods are limited due to the complex and inherent features of the data. We have identified three analytical objectives in longitudinal microbial studies: (1) differential abundance over time and between sample groups, demographic factors or clinical variables of interest; (2) clustering of microorganisms evolving concomitantly across time and (3) network modelling to identify temporal relationships between microorganisms. This review explores the strengths and limitations of current methods to fulfill these objectives, compares different methods in simulation and case studies for objectives (1) and (2), and highlights opportunities for further methodological developments. R tutorials are provided to reproduce the analyses conducted in this review.

MZINBVA: variational approximation for multilevel zero-inflated negative-binomial models for association analysis in microbiome surveys.

As our understanding of the microbiome has expanded, so has the recognition of its critical role in human health and disease, thereby emphasizing the importance of testing whether microbes are associated with environmental factors or clinical outcomes. However, many of the fundamental challenges that concern microbiome surveys arise from statistical and experimental design issues, such as the sparse and overdispersed nature of microbiome count data and the complex correlation structure among samples. For example, in the human microbiome project (HMP) dataset, the repeated observations across time points (level 1) are nested within body sites (level 2), which are further nested within subjects (level 3). Therefore, there is a great need for the development of specialized and sophisticated statistical tests. In this paper, we propose multilevel zero-inflated negative-binomial models for association analysis in microbiome surveys. We develop a variational approximation method for maximum likelihood estimation and inference. It uses optimization, rather than sampling, to approximate the log-likelihood and compute parameter estimates, provides a robust estimate of the covariance of parameter estimates and constructs a Wald-type test statistic for association testing. We evaluate and demonstrate the performance of our method using extensive simulation studies and an application to the HMP dataset. We have developed an R package MZINBVA to implement the proposed method, which is available from the GitHub repository https://github.com/liudoubletian/MZINBVA.

Chemotherapy-induced gut microbiome disruption, inflammation, and cognitive decline in female patients with breast cancer.

Chemotherapy is notorious for causing behavioral side effects (e.g., cognitive decline). Notably, the gut microbiome has recently been reported to communicate with the brain to affect behavior, including cognition. Thus, the aim of this clinical longitudinal observational study was to determine whether chemotherapy-induced disruption of the gut microbial community structure relates to cognitive decline and circulating inflammatory signals. Fecal samples, blood, and cognitive measures were collected from 77 patients with breast cancer before, during, and after chemotherapy. Chemotherapy altered the gut microbiome community structure and increased circulating TNF-α. Both the chemotherapy-induced changes in microbial relative abundance and decreased microbial diversity were related to elevated circulating pro-inflammatory cytokines TNF-α and IL-6. Participants reported subjective cognitive decline during chemotherapy, which was not related to changes in the gut microbiome or inflammatory markers. In contrast, a decrease in overall objective cognition was related to a decrease in microbial diversity, independent of circulating cytokines. Stratification of subjects, via a reliable change index based on 4 objective cognitive tests, identified objective cognitive decline in 35% of the subjects. Based on a differential microbial abundance analysis, those characterized by cognitive decline had unique taxonomic shifts (Faecalibacterium, Bacteroides, Fusicatenibacter, Erysipelotrichaceae UCG-003, and Subdoligranulum) over chemotherapy treatment compared to those without cognitive decline. Taken together, gut microbiome change was associated with cognitive decline during chemotherapy, independent of chemotherapy-induced inflammation. These results suggest that microbiome-related strategies may be useful for predicting and preventing behavioral side effects of chemotherapy.

Finding microbial composition and biological processes as predictive signature to access the ongoing status of mangrove preservation.

Mangroves are complex land-sea transition ecosystems whose microbiota are essential for their nutrient recycling and conservation. Brazil is the third-largest estuarine area in the world and "Baía de Todos os Santos" (BTS) is one of the largest bays of the country, with wide anthropogenic exploration. Using a metagenomic approach, we investigated composition and functional adaptability as signatures of the microbiome of pristine and anthropized areas of BTS, including those under petroleum refinery influence. The taxonomic analysis showed dominance of sulfate-reducing Desulfobacteraceae, Rhodobacteraceae, and Flavobacteriaceae. Taxa were significantly diverse between pristine and disturbed areas. Disturbed mangroves showed a notary increase in abundance of halophilic, sulfur-related, and hydrocarbon-degrading genera and a decrease in diatoms compared to pristine area. The metabolic profile of BTS mangroves was correlated with the differentially abundant microbiota. Two ecological scenarios were observed: one marked by functions of central metabolism associated with biomass degradation and another by mechanisms of microbial adaptability to pollution conditions and environmental degradation. Part of the microbiome was distinct and not abundant in Brazilian estuarine soils. The microbiome signature observed in each BTS mangrove reflects how human actions impact the diversity of these ecosystems and also emphasize their role in attempting to restore disturbed mangroves. The microbiome may act as a potential biological indicator of the preservation status of these soils, despite the limitation of soil property conditions. Additionally, our data pointed to metagenomics as an additional tool for environmental assessment and reinforced the need for protective measures for the mangroves under study.

Co-inoculation with novel nodule-inhabiting bacteria reduces the benefits of legume-rhizobium symbiosis.

The ecologically and economically vital symbiosis between nitrogen-fixing rhizobia and leguminous plants is often thought of as a bi-partite interaction, yet studies increasingly show the prevalence of non-rhizobial endophytes (NREs) that occupy nodules alongside rhizobia. Yet, what impact these NREs have on plant or rhizobium fitness remains unclear. Here, we investigated four NRE strains found to naturally co-occupy nodules of the legume Medicago truncatula alongside Sinorhizobium meliloti in native soils. Our objectives were to (1) examine the direct and indirect effects of NREs on M. truncatula and S. meliloti fitness, and (2) determine whether NREs can re-colonize root and nodule tissues upon reinoculation. We identified one NRE strain (522) as a novel Paenibacillus species, another strain (717A) as a novel Bacillus species, and the other two (702A and 733B) as novel Pseudomonas species. Additionally, we found that two NREs (Bacillus 717A and Pseudomonas 733B) reduced the fitness benefits obtained from symbiosis for both partners, while the other two (522, 702A) had little effect. Lastly, we found that NREs were able to co-infect host tissues alongside S. meliloti. This study demonstrates that variation of NREs present in natural populations must be considered to better understand legume-rhizobium dynamics in soil communities.

Detection of differentially abundant cell subpopulations in scRNA-seq data.

Comprehensive and accurate comparisons of transcriptomic distributions of cells from samples taken from two different biological states, such as healthy versus diseased individuals, are an emerging challenge in single-cell RNA sequencing (scRNA-seq) analysis. Current methods for detecting differentially abundant (DA) subpopulations between samples rely heavily on initial clustering of all cells in both samples. Often, this clustering step is inadequate since the DA subpopulations may not align with a clear cluster structure, and important differences between the two biological states can be missed. Here, we introduce DA-seq, a targeted approach for identifying DA subpopulations not restricted to clusters. DA-seq is a multiscale method that quantifies a local DA measure for each cell, which is computed from its k nearest neighboring cells across a range of k values. Based on this measure, DA-seq delineates contiguous significant DA subpopulations in the transcriptomic space. We apply DA-seq to several scRNA-seq datasets and highlight its improved ability to detect differences between distinct phenotypes in severe versus mildly ill COVID-19 patients, melanomas subjected to immune checkpoint therapy comparing responders to nonresponders, embryonic development at two time points, and young versus aging brain tissue. DA-seq enabled us to detect differences between these phenotypes. Importantly, we find that DA-seq not only recovers the DA cell types as discovered in the original studies but also reveals additional DA subpopulations that were not described before. Analysis of these subpopulations yields biological insights that would otherwise be undetected using conventional computational approaches.

Nontargeted metabolomics reveals sequential changes in amino acid and ferroptosis-related metabolism in Parkinson's disease.

Parkinson's disease (PD) is inseparable from metabolic disorders but lacks assessment of specific metabolite alteration. To explore the sequential metabolic changes in PD progression, we evenly divided 78 C57BL/6 mice (10 weeks) into six groups (one control group and five experimental groups) and collected the hippocampus tissue of mice after treating with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, and probenecid (twice a week) at five periods (1, 2, 3, 4, and 5 weeks) for metabolome analysis. Our study identified 567 differentially abundant metabolites (DAMs) (total 4348 metabolites). Compared with controls, 145, 146, 171, 208, and 213 DAMs were obtained from the five experimental groups, respectively. Notably, 40 shared DAMs were present in five experimental groups, of which 22 shared DAMs formed a new metabolic network based on amino acid metabolism. Compared with group W3, 84 DAMs were identified in group W5, including 12 unique DAMs. DAMs in different stages of PD were significantly enriched in amino acid metabolism pathway, lipid metabolism pathway, and ferroptosis pathway. l-Glutamine, spermidine, and l-tryptophan were the key hubs in the whole metabolic process of PD. N-Formyl-l-methionine gradually increased in abundance with PD progression, whereas 5-methylcytosine gradually decreased. The study emphasized the sequential changes in DAMs in PD progression, stimulating subsequent studies.

A cell abundance analysis based on efficient PAM clustering for a better understanding of the dynamics of endometrial remodelling.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) is a powerful tool for investigating cell abundance changes during tissue regeneration and remodeling processes. Differential cell abundance supports the initial clustering of all cells; then, the number of cells per cluster and sample are evaluated, and the dependence of these counts concerning the phenotypic covariates of the samples is studied. Analysis heavily depends on the clustering method. Partitioning Around Medoids (PAM or k-medoids) represents a well-established clustering procedure that leverages the downstream interpretation of clusters by pinpointing real individuals in the dataset as cluster centers (medoids) without reducing dimensions. Of note, PAM suffers from high computational costs and memory requirements. RESULTS: This paper proposes a method for differential abundance analysis using PAM as a clustering method and negative binomial regression as a statistical model to relate covariates to cluster/cell counts. We used this approach to study the differential cell abundance of human endometrial cell types throughout the natural secretory phase of the menstrual cycle. We developed a new R package -scellpam-, that incorporates an efficient parallel C++ implementation of PAM, and applied this package in this study. We compared the PAM-BS clustering method with other methods and evaluated both the computational aspects of its implementation and the quality of the classifications obtained using distinct published datasets with known subpopulations that demonstrate promising results. CONCLUSIONS: The implementation of PAM-BS, included in the scellpam package, exhibits robust performance in terms of speed and memory usage compared to other related methods. PAM allowed quick and robust clustering of sets of cells with a size ranging from 70,000 to 300,000 cells. https://cran.r-project.org/web/packages/scellpam/index.html . Finally, our approach provides important new insights into the transient subpopulations associated with the fertile time frame when applied to the study of changes in the human endometrium during the secretory phase of the menstrual cycle.

Rank normalization empowers a t-test for microbiome differential abundance analysis while controlling for false discoveries.

A major task in the analysis of microbiome data is to identify microbes associated with differing biological conditions. Before conducting analysis, raw data must first be adjusted so that counts from different samples are comparable. A typical approach is to estimate normalization factors by which all counts in a sample are multiplied or divided. However, the inherent variation associated with estimation of normalization factors are often not accounted for in subsequent analysis, leading to a loss of precision. Rank normalization is a nonparametric alternative to the estimation of normalization factors in which each count for a microbial feature is replaced by its intrasample rank. Although rank normalization has been successfully applied to microarray analysis in the past, it has yet to be explored for microbiome data, which is characterized by high frequencies of 0s, strongly correlated features and compositionality. We propose to use rank normalization as an alternative to the estimation of normalization factors and examine its performance when paired with a two-sample t-test. On a rigorous 3rd-party benchmarking simulation, it is shown to offer strong control over the false discovery rate, and at sample sizes greater than 50 per treatment group, to offer an improvement in performance over commonly used normalization factors paired with t-tests, Wilcoxon rank-sum tests and methodologies implemented by R packages. On two real datasets, it yielded valid and reproducible results that were strongly in agreement with the original findings and the existing literature, further demonstrating its robustness and future potential. Availability: The data underlying this article are available online along with R code and supplementary materials at https://github.com/matthewlouisdavisBioStat/Rank-Normalization-Empowers-a-T-Test.

Quantitative Amplicon Sequencing Is Necessary to Identify Differential Taxa and Correlated Taxa Where Population Sizes Differ.

High-throughput, multiplexed-amplicon sequencing has become a core tool for understanding environmental microbiomes. As researchers have widely adopted sequencing, many open-source analysis pipelines have been developed to compare microbiomes using compositional analysis frameworks. However, there is increasing evidence that compositional analyses do not provide the information necessary to accurately interpret many community assembly processes. This is especially true when there are large gradients that drive distinct community assembly processes. Recently, sequencing has been combined with Q-PCR (among other sources of total quantitation) to generate "Quantitative Sequencing" (QSeq) data. QSeq more accurately estimates the true abundance of taxa, is a more reliable basis for inferring correlation, and, ultimately, can be more reliably related to environmental data to infer community assembly processes. In this paper, we use a combination of published data sets, synthesis, and empirical modeling to offer guidance for which contexts QSeq is advantageous. As little as 5% variation in total abundance among experimental groups resulted in more accurate inference by QSeq than compositional methods. Compositional methods for differential abundance and correlation unreliably detected patterns in abundance and covariance when there was greater than 20% variation in total abundance among experimental groups. Whether QSeq performs better for beta diversity analysis depends on the question being asked, and the analytic strategy (e.g., what distance metric is being used); for many questions and methods, QSeq and compositional analysis are equivalent for beta diversity analysis. QSeq is especially useful for taxon-specific analysis; QSeq transformation and analysis should be the default for answering taxon-specific questions of amplicon sequence data. Publicly available bioinformatics pipelines should incorporate support for QSeq transformation and analysis.

Ulcerative Colitis Seems to Imply Oral Microbiome Dysbiosis.

Ulcerative colitis (UC) is a recurrent pathology of complex etiology that has been occasionally associated with oral lesions, but the overall composition of the oral microbiome in UC patients and its role in the pathogenesis of the disease are still poorly understood. In this study, the oral microbiome of UC patients and healthy individuals was compared to ascertain the possible changes in the oral microbial communities associated with UC. For this, the salivary microbiota of 10 patients diagnosed with an active phase of UC and 11 healthy controls was analyzed by 16S rRNA gene sequencing (trial ref. ISRCTN39987). Metataxonomic analysis revealed a decrease in the alpha diversity and an imbalance in the relative proportions of some key members of the oral core microbiome in UC patients. Additionally, Staphylococcus members and four differential species or phylotypes were only present in UC patients, not being detected in healthy subjects. This study provides a global snapshot of the existence of oral dysbiosis associated with UC, and the possible presence of potential oral biomarkers.

Changes in root microbiome during wheat evolution.

BACKGROUND: Although coevolutionary signatures of host-microbe interactions are considered to engineer the healthy microbiome of humans, little is known about the changes in root-microbiome during plant evolution. To understand how the composition of the wheat and its ancestral species microbiome have changed over the evolutionary processes, we performed a 16S rRNA metagenomic analysis on rhizobacterial communities associated with a phylogenetic framework of four Triticum species T. urartu, T. turgidum, T. durum, and T. aestivum along with their ancestral species Aegilops speltoides, and Ae. tauschii during vegetative and reproductive stages. RESULTS: In this study, we illustrated that the genome contents of wild species Aegilops speltoides and Ae. tauschii can be significant factors determining the composition of root-associated bacterial communities in domesticated bread wheat. Although it was found that domestication and modern breeding practices might have had a significant impact on microbiome-plant interactions especially at the reproductive stage, we observed an extensive and selective control by wheat genotypes on associated rhizobacterial communities at the same time. Our data also showed a strong genotypic variation within species of T. aestivum and Ae. tauschii, suggesting potential breeding targets for plants surveyed. CONCLUSIONS: This study performed with different genotypes of Triticum and Aegilops species is the first study showing that the genome contents of Ae. speltoides and Ae. tauschii along with domestication-related changes can be significant factors determining the composition of root-associated bacterial communities in bread wheat. It is also indirect evidence that shows a very extensive range of host traits and genes are probably involved in host-microbe interactions. Therefore, understanding the wheat root-associated microbiome needs to take into consideration of its polygenetic mosaic nature.

Fecal microbiota and their association with heat stress in Bos taurus.

BACKGROUND: Humans have been influencing climate changes by burning fossil fuels, farming livestock, and cutting down rainforests, which has led to global temperature rise. This problem of global warming affects animals by causing heat stress, which negatively affects their health, biological functions, and reproduction. On the molecular level, it has been proved that heat stress changes the expression level of genes and therefore causes changes in proteome and metabolome. The importance of a microbiome in many studies showed that it is considered as individuals' "second genome". Physiological changes caused by heat stress may impact the microbiome composition. RESULTS: In this study, we identified fecal microbiota associated with heat stress that was quantified by three metrics - rectal temperature, drooling, and respiratory scores represented by their Estimated Breeding Values. We analyzed the microbiota from 136 fecal samples of Chinese Holstein cows through a 16S rRNA gene sequencing approach. Statistical modeling was performed using a negative binomial regression. The analysis revealed the total number of 24 genera and 12 phyla associated with heat stress metrics. Rhizobium and Pseudobutyrivibrio turned out to be the most significant genera, while Acidobacteria and Gemmatimonadetes were the most significant phyla. Phylogenetic analysis revealed that three heat stress indicators quantify different metabolic ways of animals' reaction to heat stress. Other studies already identified that those genera had significantly increased abundance in mice exposed to stressor-induced changes. CONCLUSIONS: This study provides insights into the analysis of microbiome composition in cattle using heat stress measured as a continuous variable. The bacteria highly associated with heat stress were highlighted and can be used as biomarkers in further microbiological studies.

A 4-Week Diet Low or High in Advanced Glycation Endproducts Has Limited Impact on Gut Microbial Composition in Abdominally Obese Individuals: The deAGEing Trial.

Dietary advanced glycation endproducts (AGEs), abundantly present in Westernized diets, are linked to negative health outcomes, but their impact on the gut microbiota has not yet been well investigated in humans. We investigated the effects of a 4-week isocaloric and macronutrient-matched diet low or high in AGEs on the gut microbial composition of 70 abdominally obese individuals in a double-blind parallel-design randomized controlled trial (NCT03866343). Additionally, we investigated the cross-sectional associations between the habitual intake of dietary dicarbonyls, reactive precursors to AGEs, and the gut microbial composition, as assessed by 16S rRNA amplicon-based sequencing. Despite a marked percentage difference in AGE intake, we observed no differences in microbial richness and the general community structure. Only the Anaerostipes spp. had a relative abundance >0.5% and showed differential abundance (0.5 versus 1.11%; p = 0.028, after low- or high-AGE diet, respectively). While the habitual intake of dicarbonyls was not associated with microbial richness or a general community structure, the intake of 3-deoxyglucosone was especially associated with an abundance of several genera. Thus, a 4-week diet low or high in AGEs has a limited impact on the gut microbial composition of abdominally obese humans, paralleling its previously observed limited biological consequences. The effects of dietary dicarbonyls on the gut microbiota composition deserve further investigation.

Censcyt: censored covariates in differential abundance analysis in cytometry.

BACKGROUND: Innovations in single cell technologies have lead to a flurry of datasets and computational tools to process and interpret them, including analyses of cell composition changes and transition in cell states. The diffcyt workflow for differential discovery in cytometry data consist of several steps, including preprocessing, cell population identification and differential testing for an association with a binary or continuous covariate. However, the commonly measured quantity of survival time in clinical studies often results in a censored covariate where classical differential testing is inapplicable. RESULTS: To overcome this limitation, multiple methods to directly include censored covariates in differential abundance analysis were examined with the use of simulation studies and a case study. Results show that multiple imputation based methods offer on-par performance with the Cox proportional hazards model in terms of sensitivity and error control, while offering flexibility to account for covariates. The tested methods are implemented in the R package censcyt as an extension of diffcyt and are available at https://bioconductor.org/packages/censcyt . CONCLUSION: Methods for the direct inclusion of a censored variable as a predictor in GLMMs are a valid alternative to classical survival analysis methods, such as the Cox proportional hazard model, while allowing for more flexibility in the differential analysis.

Comparison study of differential abundance testing methods using two large Parkinson disease gut microbiome datasets derived from 16S amplicon sequencing.

BACKGROUND: Testing for differential abundance of microbes in disease is a common practice in microbiome studies. Numerous differential abundance (DA) testing methods exist and range from traditional statistical tests to methods designed for microbiome data. Comparison studies of DA testing methods have been performed, but none performed on microbiome datasets collected for the study of real, complex disease. Due to this, DA testing was performed here using various DA methods in two large, uniformly collected gut microbiome datasets on Parkinson disease (PD), and their results compared. RESULTS: Overall, 78-92% of taxa tested were detected as differentially abundant by at least one method, while 5-22% were called differentially abundant by the majority of methods (depending on dataset and filtering of taxonomic data prior to testing). Concordances between method results ranged from 1 to 100%. Average concordance for datasets 1 and 2 were 24% and 28% respectively, and 27% for replicated DA signatures. Concordances increased when removing rarer taxa before testing, increasing average concordances by 2-32%. Certain methods consistently resulted in higher concordances (e.g. ANCOM-BC, LEfSe), while others consistently resulted in lower (e.g. edgeR, fitZIG). Hierarchical clustering revealed three groups of DA signatures that were (1) replicated by the majority of methods on average and included taxa previously associated with PD, (2) replicated by a subset of methods and included taxa largely enriched in PD, and (3) replicated by few to one method(s). CONCLUSIONS: Differential abundance tests yielded varied concordances, and amounts of detected DA signatures. Some methods were more concordant than others on both filtered and unfiltered data, therefore, if consistency with other study methodology is a key goal, one might choose among these methods. Even still, using one method on one dataset may find true associations, but may also detect false positives. To help lower false positives, one might analyze data with two or more DA methods to gauge concordance, and use a built-in replication dataset. This study will hopefully serve to complement previously reported DA method comparison studies by implementing and coalescing a large number of both previously and yet to be compared methods on two real gut microbiome datasets.