Gender-affirming hormone therapy preserves skeletal maturation in young mice via the gut microbiome.

Gender-affirming hormone therapy (GAHT) is often prescribed to transgender (TG) adolescents to alleviate gender dysphoria, but the effect of GAHT on the growing skeleton is unclear. We found GAHT to improve trabecular bone structure via increased bone formation in young male mice and not to affect trabecular structure in female mice. GAHT modified gut microbiome composition in both male and female mice. However, fecal microbiota transfers (FMTs) revealed that GAHT-shaped gut microbiome was a communicable regulator of bone structure and turnover in male, but not in female mice. Mediation analysis identified 2 species of Bacteroides as significant contributors to the skeletal effects of GAHT in male mice, with Bacteroides supplementation phenocopying the effects of GAHT on bone. Bacteroides have the capacity to expand Treg populations in the gut. Accordingly, GAHT expanded intestinal Tregs and stimulated their migration to the bone marrow (BM) in male but not in female mice. Attesting to the functional relevance of Tregs, pharmacological blockade of Treg expansion prevented GAHT-induced bone anabolism. In summary, in male mice GAHT stimulated bone formation and improved trabecular structure by promoting Treg expansion via a microbiome-mediated effect, while in female mice, GAHT neither improved nor impaired trabecular structure.

MERIT: Controlling Monte-Carlo error rate in large-scale Monte-Carlo hypothesis testing.

The use of Monte-Carlo (MC) p $$ p $$ -values when testing the significance of a large number of hypotheses is now commonplace. In large-scale hypothesis testing, we will typically encounter at least some p $$ p $$ -values near the threshold of significance, which require a larger number of MC replicates than p $$ p $$ -values that are far from the threshold. As a result, some incorrect conclusions can be reached due to MC error alone; for hypotheses near the threshold, even a very large number (eg, 1 0 6 $$ 1{0}^6 $$ ) of MC replicates may not be enough to guarantee conclusions reached using MC p $$ p $$ -values. Gandy and Hahn (GH)6-8 have developed the only method that directly addresses this problem. They defined a Monte-Carlo error rate (MCER) to be the probability that any decisions on accepting or rejecting a hypothesis based on MC p $$ p $$ -values are different from decisions based on ideal p $$ p $$ -values; their method then makes decisions by controlling the MCER. Unfortunately, the GH method is frequently very conservative, often making no rejections at all and leaving a large number of hypotheses "undecided". In this article, we propose MERIT, a method for large-scale MC hypothesis testing that also controls the MCER but is more statistically efficient than the GH method. Through extensive simulation studies, we demonstrate that MERIT controls the MCER while making more decisions that agree with the ideal p $$ p $$ -values than GH does. We also illustrate our method by an analysis of gene expression data from a prostate cancer study.

Unique microbial diversity, community composition, and networks among Pacific Islander endocervical and vaginal microbiomes with and without Chlamydia trachomatis infection in Fiji.

IMPORTANCE: Chlamydia trachomatis (Ct) is the most common sexually transmitted bacterium globally. Endocervical and vaginal microbiome interactions are rarely examined within the context of Ct or among vulnerable populations. We evaluated 258 vaginal and 92 paired endocervical samples from Fijian women using metagenomic shotgun sequencing. Over 37% of the microbiomes could not be classified into sub-community state types (subCSTs). We, therefore, developed subCSTs IV-D0, IV-D1, IV-D2, and IV-E-dominated primarily by Gardnerella vaginalis-to improve classification. Among paired microbiomes, the endocervix had a significantly higher alpha diversity and, independently, higher diversity for high-risk human papilloma virus (HPV) genotypes compared to low-risk and no HPV. Ct-infected endocervical networks had smaller clusters without interactions with potentially beneficial Lactobacillus spp. Overall, these data suggest that G. vaginalis may generate polymicrobial biofilms that predispose to and/or promote Ct and possibly HPV persistence and pathogenicity. Our findings expand on the existing repertoire of endocervical and vaginal microbiomes and fill in knowledge gaps regarding Pacific Islanders.

Comparison of genomic diversity between single and pooled Staphylococcus aureus colonies isolated from human colonization cultures.

The most common approach to sampling the bacterial populations within an infected or colonized host is to sequence genomes from a single colony obtained from a culture plate. However, it is recognized that this method does not capture the genetic diversity in the population. Sequencing a mixture of several colonies (pool-seq) is a better approach to detect population heterogeneity, but it is more complex to analyse due to different types of heterogeneity, such as within-clone polymorphisms, multi-strain mixtures, multi-species mixtures and contamination. Here, we compared 8 single-colony isolates (singles) and pool-seq on a set of 2286 Staphylococcus aureus culture samples to identify features that can distinguish pure samples, samples undergoing intraclonal variation and mixed strain samples. The samples were obtained by swabbing 3 body sites on 85 human participants quarterly for a year, who initially presented with a methicillin-resistant S. aureus skin and soft-tissue infection (SSTI). We compared parameters such as sequence quality, contamination, allele frequency, nucleotide diversity and pangenome diversity in each pool to those for the corresponding singles. Comparing singles from the same culture plate, we found that 18% of sample collections contained mixtures of multiple multilocus sequence types (MLSTs or STs). We showed that pool-seq data alone could predict the presence of multi-ST populations with 95% accuracy. We also showed that pool-seq could be used to estimate the number of intra-clonal polymorphic sites in the population. Additionally, we found that the pool may contain clinically relevant genes such as antimicrobial resistance markers that may be missed when only examining singles. These results highlight the potential advantage of analysing genome sequences of total populations obtained from clinical cultures rather than single colonies.

Compositional analysis of microbiome data using the linear decomposition model (LDM).

SUMMARY: There are compelling reasons to test compositional hypotheses about microbiome data. We present here linear decomposition model-centered log ratio (LDM-clr), an extension of our LDM approach to allow fitting linear models to centered-log-ratio-transformed taxa count data. As LDM-clr is implemented within the existing LDM program, this extension enjoys all the features supported by LDM, including a compositional analysis of differential abundance at both the taxon and community levels, while allowing for a wide range of covariates and study designs for either association or mediation analysis. AVAILABILITY AND IMPLEMENTATION: LDM-clr has been added to the R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM.

Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency.

Background: The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Interestingly, many microbiome studies have performed both sequencing experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, highlighting the potential for an integrative analysis to improve power of testing these signatures. However, differential experimental biases, partially overlapping samples, and differential library sizes pose tremendous challenges when combining the two datasets. Currently, researchers either discard one dataset entirely or use different datasets for different objectives. Methods: In this article, we introduce the first method of this kind, named Com-2seq, that combines the two sequencing datasets for testing differential abundance at the genus and community levels while overcoming these difficulties. The new method is based on our LOCOM model (Hu et al., 2022), which employs logistic regression for testing taxon differential abundance while remaining robust to experimental bias. To benchmark the performance of Com-2seq, we introduce two ad hoc approaches: applying LOCOM to pooled taxa count data and combining LOCOM p-values from analyzing each dataset separately. Results: Our simulation studies indicate that Com-2seq substantially improves statistical efficiency over analysis of either dataset alone and works better than the two ad hoc approaches. An application of Com-2seq to two real microbiome studies uncovered scientifically plausible findings that would have been missed by analyzing individual datasets. Conclusions: Com-2seq performs integrative analysis of 16S and metagenomic sequencing data, which improves statistical efficiency and has the potential to accelerate the search of microbial communities and taxa that are involved in human health and diseases.

Impact of Experimental Bias on Compositional Analysis of Microbiome Data.

Microbiome data are subject to experimental bias that is caused by DNA extraction and PCR amplification, among other sources, but this important feature is often ignored when developing statistical methods for analyzing microbiome data. McLaren, Willis, and Callahan (2019) proposed a model for how such biases affect the observed taxonomic profiles; this model assumes the main effects of bias without taxon-taxon interactions. Our newly developed method for testing the differential abundance of taxa, LOCOM, is the first method to account for experimental bias and is robust to the main effect biases. However, there is also evidence for taxon-taxon interactions. In this report, we formulated a model for interaction biases and used simulations based on this model to evaluate the impact of interaction biases on the performance of LOCOM as well as other available compositional analysis methods. Our simulation results indicate that LOCOM remained robust to a reasonable range of interaction biases. The other methods tend to have an inflated FDR even when there were only main effect biases. LOCOM maintained the highest sensitivity even when the other methods could not control the FDR. We thus conclude that LOCOM outperforms the other methods for compositional analysis of microbiome data considered here.

Comparison of genomic diversity between single and pooled Staphylococcus aureus colonies isolated from human colonisation cultures.

The most common approach to sampling the bacterial populations within an infected or colonised host is to sequence genomes from a single colony obtained from a culture plate. However, it is recognized that this method does not capture the genetic diversity in the population. An alternative is to sequence a mixture containing multiple colonies ("pool-seq"), but this has the disadvantage that it is a non-homogeneous sample, making it difficult to perform specific experiments. We compared differences in measures of genetic diversity between eight single-colony isolates (singles) and pool-seq on a set of 2286 S. aureus culture samples. The samples were obtained by swabbing three body sites on 85 human participants quarterly for a year, who initially presented with a methicillin-resistant S. aureus skin and soft-tissue infection (SSTI). We compared parameters such as sequence quality, contamination, allele frequency, nucleotide diversity and pangenome diversity in each pool to the corresponding singles. Comparing singles from the same culture plate, we found that 18% of sample collections contained mixtures of multiple Multilocus sequence types (MLSTs or STs). We showed that pool-seq data alone could predict the presence of multi-ST populations with 95% accuracy. We also showed that pool-seq could be used to estimate the number of polymorphic sites in the population. Additionally, we found that the pool may contain clinically relevant genes such as antimicrobial resistance markers that may be missed when only examining singles. These results highlight the potential advantage of analysing genome sequences of total populations obtained from clinical cultures rather than single colonies.

Compositional analysis of microbiome data using the linear decomposition model (LDM).

Summary: There are compelling reasons to test compositional hypotheses about microbiome data. We present here LDM-clr, an extension of our linear decomposition model (LDM) approach to allow fitting linear models to centered-log-ratio-transformed taxa count data. As LDM-clr is implemented within the existing LDM program, it enjoys all the features supported by LDM, including a compositional analysis of differential abundance at both the taxon and community levels, while allowing for a wide range of covariates and study designs for either association or mediation analysis. Availability and Implementation: LDM-clr has been added to the R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM . Contact: yijuan.hu@emory.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency.

The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Interestingly, many microbiome studies have performed both sequencing experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, highlighting the potential for an integrative analysis to improve power of testing these signatures. However, differential experimental biases, partially overlapping samples, and differential library sizes pose tremendous challenges when combining the two datasets. Currently, researchers either discard one dataset entirely or use different datasets for different objectives. In this article, we introduce the first method of this kind, named Com-2seq, that combines the two sequencing datasets for the objective of testing differential abundance at the genus and community levels while overcoming these difficulties. We demonstrate that Com-2seq substantially improves statistical efficiency over analysis of either dataset alone and works better than two ad hoc approaches.

HIV, asymptomatic STI, and the rectal mucosal immune environment among young men who have sex with men.

Young men who have sex with men (YMSM) are disproportionately affected by HIV and bacterial sexually transmitted infections (STI) including gonorrhea, chlamydia, and syphilis; yet research into the immunologic effects of these infections is typically pursued in siloes. Here, we employed a syndemic approach to understand potential interactions of these infections on the rectal mucosal immune environment among YMSM. We enrolled YMSM aged 18-29 years with and without HIV and/or asymptomatic bacterial STI and collected blood, rectal secretions, and rectal tissue biopsies. YMSM with HIV were on suppressive antiretroviral therapy (ART) with preserved blood CD4 cell counts. We defined 7 innate and 19 adaptive immune cell subsets by flow cytometry, the rectal mucosal transcriptome by RNAseq, and the rectal mucosal microbiome by 16S rRNA sequencing and examined the effects of HIV and STI and their interactions. We measured tissue HIV RNA viral loads among YMSM with HIV and HIV replication in rectal explant challenge experiments among YMSM without HIV. HIV, but not asymptomatic STI, was associated with profound alterations in the cellular composition of the rectal mucosa. We did not detect a difference in the microbiome composition associated with HIV, but asymptomatic bacterial STI was associated with a higher probability of presence of potentially pathogenic taxa. When examining the rectal mucosal transcriptome, there was evidence of statistical interaction; asymptomatic bacterial STI was associated with upregulation of numerous inflammatory genes and enrichment for immune response pathways among YMSM with HIV, but not YMSM without HIV. Asymptomatic bacterial STI was not associated with differences in tissue HIV RNA viral loads or in HIV replication in explant challenge experiments. Our results suggest that asymptomatic bacterial STI may contribute to inflammation particularly among YMSM with HIV, and that future research should examine potential harms and interventions to reduce the health impact of these syndemic infections.

Impact of experimental bias on compositional analysis of microbiome data.

Microbiome data are subject to experimental bias that is caused by DNA extraction, PCR amplification among other sources, but this important feature is often ignored when developing statistical methods for analyzing microbiome data. McLaren, Willis and Callahan (2019) proposed a model for how such bias affects the observed taxonomic profiles, which assumes main effects of bias without taxon-taxon interactions. Our newly developed method, LOCOM (logistic regression for compositional analysis) for testing differential abundance of taxa, is the first method that accounted for experimental bias and is robust to the main effect biases. However, there is also evidence for taxon-taxon interactions. In this report, we formulated a model for interaction biases and used simulations based on this model to evaluate the impact of interaction biases on the performance of LOCOM as well as other available compositional analysis methods. Our simulation results indicated that LOCOM remained robust to a reasonable range of interaction biases. The other methods tended to have inflated FDR even when there were only main effect biases. LOCOM maintained the highest sensitivity even when the other methods cannot control the FDR. We thus conclude that LOCOM outperforms the other methods for compositional analysis of microbiome data considered here.

Oral microbiome of electronic cigarette users: A cross-sectional exploration.

OBJECTIVE: Electronic cigarettes have increased in popularity globally. Vaping may be associated with oral symptoms and pathologies including dental and periodontal damage, both of which have an underlying microbial etiology. The primary aim of this pilot study, therefore, was to compare the oral microbiome of vapers and non-vapers. SUBJECTS AND METHODS: This secondary data analysis had a cross-sectional comparative descriptive design and included data for 36 adults. Bacterial 16S rRNA genes were extracted and amplified from soft tissue oral swab specimens and taxonomically classified using the Human Oral Microbiome Database. RESULTS: Data for 18 vapers and 18 non-vapers were included in this study. Almost 56% of the vapers also smoked conventional cigarettes. Beta diversity differences were identified between vapers and non-vapers. Vapers had a significantly higher relative abundance of an unclassified species of Veillonella compared with non-vapers. Dual users had higher alpha diversity compared with exclusive vapers. Beta diversity was also associated with dual use. Multiple OTUs were identified to be associated with dual use of e-cigarettes and conventional cigarettes. CONCLUSIONS: Vapers exhibit an altered oral microbiome. Dual use of electronic cigarettes and conventional cigarettes is associated with the presence of several known pathogenic microbes.

The rectal mucosal immune environment and HIV susceptibility among young men who have sex with men.

Young men who have sex with men (YMSM) represent a particularly high-risk group for HIV acquisition in the US, despite similarly reported rates of sexual activity as older, adult MSM (AMSM). Increased rates of HIV infection among YMSM compared to AMSM could be partially attributable to differences within the rectal mucosal (RM) immune environment associated with earlier sexual debut and less lifetime exposure to receptive anal intercourse. Using an ex vivo explant HIV challenge model, we found that rectal tissues from YMSM supported higher levels of p24 at peak viral replication timepoints compared to AMSM. Among YMSM, the RM was characterized by increased CD4+ T cell proliferation, as well as lower frequencies of tissue resident CD8+ T cells and pro-inflammatory cytokine producing CD4+ and CD8+ T cells. In addition, the microbiome composition of YMSM was enriched for anaerobic taxa that have previously been associated with HIV acquisition risk, including Prevotella, Peptostreptococcus, and Peptoniphilus. These distinct immunologic and microbiome characteristics were found to be associated with higher HIV replication following ex vivo challenge of rectal explants, suggesting the RM microenvironment of YMSM may be uniquely conducive to HIV infection.

Obesity and patient-reported sexual health outcomes in gynecologic cancer survivors: A systematic review.

As obesity prevalence among gynecologic cancer (GC) survivors is expected to increase, the role of obesity in sexual health needs to be understood. This systematic review examined the impact of obesity on patient-reported sexual health outcomes (SHOs) in this population. PubMed, Embase, Web of Science, CINAHL, and PsycINFO were searched for original studies published between 2015 and 2020 following the Preferred Reporting Items for Systematic Review and Meta-Analyses guideline. We performed a narrative synthesis of findings via cancer type, cancer treatment, sexual health measures, and countries. Eleven observational studies were included. Most were conducted in European countries (n = 7), reported on endometrial cancer survivors (n = 7), and defined obesity as body mass index ≥30 kg/m2 (n = 10). Studies about cervical cancer survivors reported negative effects of obesity on sexual activity and body image while studies about endometrial cancer survivors reported positive effects of obesity on vaginal/sexual symptoms. Findings suggested interaction effects of radiotherapy and obesity on SHOs. Sexual functioning measured by the Female Sexual Function Index was less likely to be associated with obesity than other SHOs. A positive effect of obesity on SHOs was only found in studies conducted in European countries. Current evidence on the association between obesity and sexual health in GC survivors lacks in both quantity and quality. To better understand the effect of obesity on SHOs in the population, more studies are needed with critical evaluations of obesity and sexual health measures, careful considerations of cancer type and treatment, and a focus on the cultural context of obesity.

Use of high-resolution metabolomics to assess the biological perturbations associated with maternal exposure to Bisphenol A and Bisphenol F among pregnant African American women.

BACKGROUND: Human and animal exposure to bisphenol A (BPA) has been associated with adverse developmental and reproductive effects. The molecular mechanisms by which BPA exposure exerts its effects are not well-understood, even less known about its analogues bisphenol F (BPF). To address these knowledge gaps, we conducted an untargeted metabolome-wide association study (MWAS) to identify metabolic perturbations associated with BPA/BPF exposures in a pregnant African American cohort. METHODS: From a subset of study participants enrolled in the Atlanta African American Maternal-Child cohort, we collected both urine samples, for targeted exposure assessment of BPA (N = 230) and BPF (N = 48), and serum samples, for high-resolution metabolomics (HRM) profiling (N = 230), during early pregnancy (8-14 weeks' gestation). Using an established untargeted HRM workflow consisting of MWAS modeling, pathway enrichment analysis, and chemical annotation and confirmation, we investigated the potential metabolic pathways and features associated with BPA/BPF exposures. RESULTS: The geometric mean creatinine-adjusted concentrations of urinary BPA and BPF were 0.85 ± 2.58 and 0.70 ± 4.71 µg/g creatinine, respectively. After false positive discovery rate correction at 20 % level, 264 and 733 unique metabolic features were significantly associated with urinary BPA and BPF concentrations, representing 10 and 12 metabolic pathways, respectively. Three metabolic pathways, including steroid hormones biosynthesis, lysine and lipoate metabolism, were significantly associated with both BPA and BPF exposure. Using chemical standards, we have confirmed the chemical identity of 16 metabolites significantly associated with BPA or BPF exposure. CONCLUSIONS: Our findings support that exposure to BPA and BPF in pregnant women is associated with the perturbation of aromatic amino acid metabolism, xenobiotics metabolism, steroid biosynthesis, and other amino acid metabolism closely linked to stress responses, inflammation, neural development, reproduction, and weight regulation.

A robust and transformation-free joint model with matching and regularization for metagenomic trajectory and disease onset.

BACKGROUND: To identify operational taxonomy units (OTUs) signaling disease onset in an observational study, a powerful strategy was selecting participants by matched sets and profiling temporal metagenomes, followed by trajectory analysis. Existing trajectory analyses modeled individual OTU or microbial community without adjusting for the within-community correlation and matched-set-specific latent factors. RESULTS: We proposed a joint model with matching and regularization (JMR) to detect OTU-specific trajectory predictive of host disease status. The between- and within-matched-sets heterogeneity in OTU relative abundance and disease risk were modeled by nested random effects. The inherent negative correlation in microbiota composition was adjusted by incorporating and regularizing the top-correlated taxa as longitudinal covariate, pre-selected by Bray-Curtis distance and elastic net regression. We designed a simulation pipeline to generate true biomarkers for disease onset and the pseudo biomarkers caused by compositionality. We demonstrated that JMR effectively controlled the false discovery and pseudo biomarkers in a simulation study generating temporal high-dimensional metagenomic counts with random intercept or slope. Application of the competing methods in the simulated data and the TEDDY cohort showed that JMR outperformed the other methods and identified important taxa in infants' fecal samples with dynamics preceding host disease status. CONCLUSION: Our method JMR is a robust framework that models taxon-specific trajectory and host disease status for matched participants without transformation of relative abundance, improving the power of detecting disease-associated microbial features in certain scenarios. JMR is available in R package mtradeR at https://github.com/qianli10000/mtradeR.

Testing microbiome associations with survival times at both the community and individual taxon levels.

BACKGROUND: Finding microbiome associations with possibly censored survival times is an important problem, especially as specific taxa could serve as biomarkers for disease prognosis or as targets for therapeutic interventions. The two existing methods for survival outcomes, MiRKAT-S and OMiSA, are restricted to testing associations at the community level and do not provide results at the individual taxon level. An ad hoc approach testing each taxon with a survival outcome using the Cox proportional hazard model may not perform well in the microbiome setting with sparse count data and small sample sizes. METHODS: We have previously developed the linear decomposition model (LDM) for testing continuous or discrete outcomes that unifies community-level and taxon-level tests into one framework. Here we extend the LDM to test survival outcomes. We propose to use the Martingale residuals or the deviance residuals obtained from the Cox model as continuous covariates in the LDM. We further construct tests that combine the results of analyzing each set of residuals separately. Finally, we extend PERMANOVA, the most commonly used distance-based method for testing community-level hypotheses, to handle survival outcomes in a similar manner. RESULTS: Using simulated data, we showed that the LDM-based tests preserved the false discovery rate for testing individual taxa and had good sensitivity. The LDM-based community-level tests and PERMANOVA-based tests had comparable or better power than MiRKAT-S and OMiSA. An analysis of data on the association of the gut microbiome and the time to acute graft-versus-host disease revealed several dozen associated taxa that would not have been achievable by any community-level test, as well as improved community-level tests by the LDM and PERMANOVA over those obtained using MiRKAT-S and OMiSA. CONCLUSIONS: Unlike existing methods, our new methods are capable of discovering individual taxa that are associated with survival times, which could be of important use in clinical settings.

A Bottom-up Approach to Testing Hypotheses That Have a Branching Tree Dependence Structure, with Error Rate Control.

Modern statistical analyses often involve testing large numbers of hypotheses. In many situations, these hypotheses may have an underlying tree structure that both helps determine the order that tests should be conducted but also imposes a dependency between tests that must be accounted for. Our motivating example comes from testing the association between a trait of interest and groups of microbes that have been organized into operational taxonomic units (OTUs) or amplicon sequence variants (ASVs). Given p-values from association tests for each individual OTU or ASV, we would like to know if we can declare a certain species, genus, or higher taxonomic group to be associated with the trait. For this problem, a bottom-up testing algorithm that starts at the lowest level of the tree (OTUs or ASVs) and proceeds upward through successively higher taxonomic groupings (species, genus, family etc.) is required. We develop such a bottom-up testing algorithm that controls a novel error rate that we call the false selection rate. By simulation, we also show that our approach is better at finding driver taxa, the highest level taxa below which there are dense association signals. We illustrate our approach using data from a study of the microbiome among patients with ulcerative colitis and healthy controls.

LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control.

Compositional analysis is based on the premise that a relatively small proportion of taxa are differentially abundant, while the ratios of the relative abundances of the remaining taxa remain unchanged. Most existing methods use log-transformed data, but log-transformation of data with pervasive zero counts is problematic, and these methods cannot always control the false discovery rate (FDR). Further, high-throughput microbiome data such as 16S amplicon or metagenomic sequencing are subject to experimental biases that are introduced in every step of the experimental workflow. McLaren et al. [eLife 8, e46923 (2019)] have recently proposed a model for how these biases affect relative abundance data. Motivated by this model, we show that the odds ratios in a logistic regression comparing counts in two taxa are invariant to experimental biases. With this motivation, we propose logistic compositional analysis (LOCOM), a robust logistic regression approach to compositional analysis, that does not require pseudocounts. Inference is based on permutation to account for overdispersion and small sample sizes. Traits can be either binary or continuous, and adjustment for confounders is supported. Our simulations indicate that LOCOM always preserved FDR and had much improved sensitivity over existing methods. In contrast, analysis of composition of microbiomes (ANCOM) and ANCOM with bias correction (ANCOM-BC)/ANOVA-Like Differential Expression tool (ALDEx2) had inflated FDR when the effect sizes were small and large, respectively. Only LOCOM was robust to experimental biases in every situation. The flexibility of our method for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. Our R package LOCOM is publicly available.