Biology subjects

Satten, G. A.

Publications and source records attributed to Satten, G. A..

14 recordsLinked to original sources

bioRxiv2026

LOCOM2: Robust Differential Abundance Analysis for Microbiome Data

BackgroundNumerous methods have been developed for differential abundance analysis of microbiome data; however, many fail to adequately control error rates, contributing to the reproducibility crisis in microbiome research. Moreover, new challenges have emerged, including large-scale studies, differential library size distributions, unbalanced case-control designs, and the increasing availability of only relative-abundance data rather than read counts. MethodsWe propose LOCOM2 to address these challenges. The method refines the weighting scheme in LOCOM to eliminate confounding by library size while accommodating relative abundance data. It incorporates a series of adjustments to ensure stable and reliable estimation, even under extreme conditions such as very rare taxa and highly unbalanced case-control designs. In addition, LOCOM2 replaces the computationally intensive permutation procedure in LOCOM with a Wald-type test, substantially improving computational efficiency. To evaluate performance, we conducted extensive simulation studies using the MIDASim simulator and three data templates representing diverse body sites. We benchmarked LOCOM2 against state-of-the-art methods, including LOCOM, LinDA, ANCOM-BC2, MaAsLin2, and MaAsLin3. This benchmarking effort provides an essential foundation for the next stage of microbiome research. ResultsLOCOM2 achieved accurate control of the false discovery rate across all simulation scenarios, whereas none of the other methods consistently did so. LOCOM2 also demonstrated the highest sensitivity for detecting true signals. Applications of these methods to three real microbiome datasets further corroborated these findings.

bioinformatics↗

bioRxiv2025

CAFT: A Compositional Log-Linear Model for Microbiome Data with Zero Cells

BackgroundDifferential abundance analysis is fundamental to microbiome research and provides valuable insights into host-microbe interactions. However, microbiome data are compositional, highly sparse (with many zero counts), and influenced by differential experimental biases across taxa. Standard statistical methods often overlook these features. Many approaches analyze relative abundances without accounting for compositionality or rely on pseudocounts, potentially leading to spurious associations and inadequate false discovery rate (FDR) control. MethodsWe introduce a novel framework for differential abundance analysis of microbiome data: the Compositional Accelerated Failure Time (CAFT) model. CAFT addresses zero read counts by treating them as censored observations that are below a detection limit. This approach is inherently resistant to multiplicative technical bias, eliminates the need for pseudocounts, and addresses compositional bias through the establishment of appropriate score test procedures. ResultsExtensive simulations show that CAFT outperforms competing compositional differential abundance methods, including LOCOM, LinDA, ANCOM-BC2, its robust variant, and LDM-clr by offering more robust type I error and FDR control with or without technical bias. Additionally, we applied CAFT to microbiome data on inflammatory bowel disease (IBD) and the upper respiratory tract (URT) to identify differentially abundant gut microbial taxa between IBD patients and healthy controls, as well as URT taxa distinguishing smokers from non-smokers. ConclusionWe present CAFT, a powerful, robust, and efficient approach for compositional differential abundance analysis. CAFT effectively controls Type I error and maintains FDR control, while demonstrating enhanced power in statistical testing. These capabilities render CAFT a useful tool for compositional microbiome data analysis. Availability and implementationThe R package and Vignette are available at https://github.com/mli171/CAFT.

bioinformatics↗

bioRxiv2024

Identifying unmeasured heterogeneity in microbiome data via quantile thresholding (QuanT)

BackgroundMicrobiome data, like other high-throughput data, suffer from technical heterogeneity stemming from differential experimental designs and processing. In addition to measured artifacts such as batch effects, there is heterogeneity due to unknown or unmeasured factors, which lead to spurious conclusions if unaccounted for. With the advent of large-scale multi-center microbiome studies and the increasing availability of public datasets, this issue becomes more pronounced. Current approaches for addressing unmeasured heterogeneity in high-throughput data were developed for microarray and/or RNA sequencing data. They cannot accommodate the unique characteristics of microbiome data such as sparsity and over-dispersion. ResultsHere, we introduce Quantile Thresholding (QuanT), a novel non-parametric approach for identifying unmeasured heterogeneity tailored to microbiome data. QuanT applies quantile regression across multiple quantile levels to threshold the microbiome abundance data and uncovers latent heterogeneity using thresholded binary residual matrices. We validated QuanT using both synthetic and real microbiome datasets, demonstrating its superiority in capturing and mitigating heterogeneity and improving the accuracy of downstream analyses, such as prediction analysis, differential abundance tests, and community-level diversity evaluations. ConclusionsWe present QuanT, a novel tool for comprehensive identification of unmeasured heterogeneity in microbiome data. QuanTs distinct non-parametric method markedly enhances downstream analyses, serving as a valuable tool for data integration and comprehensive analysis in microbiome research.

bioinformatics↗

bioRxiv2023

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.

bioinformatics↗

bioRxiv2023

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

SummaryThere 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 ImplementationLDM-clr has been added to the R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM. Contactyijuan.hu@emory.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2023

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.

bioinformatics↗

bioRxiv2022

Testing microbiome associations with censored survival outcomes at both the community and individual taxon levels

BackgroundFinding 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. MethodsWe have previously developed the linear decomposition model (LDM) 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. ResultsUsing 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. Availability and ImplementationThe new methods described here have been added to our R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM.

bioinformatics↗

bioRxiv2022

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

The use of Monte-Carlo (MC) 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-values near the threshold of significance, which require a larger number of MC replicates than p-values that are far from the threshold. As a result, the list of detections can vary when different MC replicates are used, resulting in lack of reproducibility. The method of Gandy and Hahn (GH) (2014; 2016; 2017) is the only method that has directly addressed this problem, defining a Monte-Carlo error rate (MCER) to be the probability that any decisions on accepting or rejecting a hypothesis based on MC p-values are different from decisions based on ideal p-values, and then making decisions that control the MCER. Unfortunately, GH 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 demonstrated that MERIT controlled the MCER and substantially improved the sensitivity and specificity of detections compared to GH. We also illustrated our method by an analysis of gene expression data from a prostate cancer study.

bioinformatics↗

bioRxiv2022

Integrative analysis of relative abundance data and presence-absence data of the microbiome using the LDM

SummaryWe previously developed LDM for testing hypotheses about the microbiome that performs the test at both the community level and the individual taxon level. LDM can be applied to relative abundance data and presence-absence data separately, which work well when associated taxa are abundant and rare, respectively. Here we propose an omnibus test based on LDM that allows simultaneous consideration of data at different scales, thus offering optimal power across scenarios with different association mechanisms. The omnibus test is available for the wide range of data types and analyses that are supported by LDM. Availability and ImplementationThe omnibus test has been added to the R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM. Contactyijuan.hu@emory.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

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

MotivationCompositional 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 of compositional analysis such as ANCOM or ANCOM-BC 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, Willis and Callahan [1] have recently proposed a model for how these biases affect relative abundance data. MethodsMotivated by [1], we show that the (log) odds ratios in a logistic regression comparing counts in two taxa are invariant to experimental biases. With this motivation, we propose LOCOM, a robust logistic regression approach to compositional analysis, that does not require pseudocounts. We use a Firth bias-corrected estimating function to account for sparse data. Inference is based on permutation to account for overdispersion and small sample sizes. Traits can be either binary or continuous, and adjustment for continuous and/or discrete confounding covariates is supported. ResultsOur simulations indicate that LOCOM always preserved FDR and had much improved sensitivity over existing methods. In contrast, ANCOM often had inflated FDR; ANCOM-BC largely controlled FDR but still had modest inflation occasionally; ALDEx2 generally had low sensitivity. LOCOM and ANCOM were robust to experimental biases in every situation, while ANCOM-BC and ALDEx2 had elevated FDR when biases at causal and non-causal taxa were differentially distributed. The flexibility of our method for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. Availability and implementationOur R package LOCOM is available on GitHub at https://github.com/yijuanhu/LOCOM in formats appropriate for Macintosh or Windows.

bioinformatics↗

bioRxiv2021

Efficient Estimation of Indirect Effects in Case-Control Studies Using a Unified Likelihood Framework

Mediation models are a set of statistical techniques that investigate the mechanisms that produce an observed relationship between an exposure variable and an outcome variable in order to deduce the extent to which the relationship is influenced by intermediate mediator variables. For a case-control study, the most common mediation analysis strategy employs a counterfactual framework that permits estimation of indirect and direct effects on the odds ratio scale for dichotomous outcomes, assuming either binary or continuous mediators. While this framework has become an important tool for mediation analysis, we demonstrate that we can embed this approach in a unified likelihood framework for mediation analysis in case-control studies that leverages more features of the data (in particular, the relationship between exposure and mediator) to improve efficiency of indirect effect estimates. One important feature of our likelihood approach is that it naturally incorporates cases within the exposure-mediator model to improve efficiency. Our approach does not require knowledge of disease prevalence and can model confounders and exposure-mediator interactions, and is straightforward to implement in standard statistical software. We illustrate our approach using both simulated data and real data from a case-control genetic study of lung cancer.

genetics↗

bioRxiv2020

Testing Presence-Absence Associations in the Microbiome using the LDM and PERMANOVA

BackgroundMany methods for testing association between the microbiome and covariates of interest (e.g., clinical outcomes, environmental factors) assume that these associations are driven by changes in the relative abundance of taxa. However, these associations may also result from changes in which taxa are present and which are absent. Analyses of such presence-absence associations face a unique challenge: confounding by library size (total sample read count), which occurs when library size is associated with covariates in the analysis. It is known that rarefaction (subsampling to a common library size) controls this bias, but at the potential cost of information loss as well as the introduction of a stochastic component into the analysis. Currently, there is a need for robust and efficient methods for testing presence-absence associations in the presence of such confounding, both at the community level and at the individual-taxon level, that avoid the drawbacks of rarefaction. MethodsWe have previously developed the linear decomposition model (LDM) that unifies the community-level and taxon-level tests into one framework. Here we present an extension of the LDM for testing presence-absence associations. The extended LDM is a non-stochastic approach that repeatedly applies the LDM to all rarefied taxa count tables, averages the residual sum-of-squares (RSS) terms over the rarefaction replicates, and then forms an F-statistic based on these average RSS terms. We show that this approach compares favorably to averaging the F-statistic from R rarefaction replicates, which can only be calculated stochastically. The flexible nature of the LDM allows discrete or continuous traits or interactions to be tested while allowing confounding covariates to be adjusted for. ResultsOur simulations indicate that our proposed method is robust to any systematic differences in library size and has better power than alternative approaches. We illustrate our method using an analysis of data on inflammatory bowel disease (IBD) in which case samples have systematically smaller library sizes than controls. ConclusionsThe rarefaction-based extension of the LDM performs well for testing presenceabsence associations and should be adopted even when there is no obvious systematic variation in library size.

bioinformatics↗

bioRxiv2020

Analyzing matched sets of microbiome data using the LDM and PERMANOVA

BackgroundMatched-set data arise frequently in microbiome studies. For example, we may collect pre- and post-treatment samples from a set of individuals, or use important confounding variables to match data from case participants to one or more control participants. Thus, there is a need for statistical methods for data comprised of matched sets, to test hypotheses against traits of interest (e.g., clinical outcomes or environmental factors) at the community level and/or the OTU (operational taxonomic unit) level. Optimally, these methods should accommodate complex data such as those with unequal sample sizes cross sets, confounders varying within sets, as well as continuous traits of interest. MethodsPERMANOVA is a commonly used distance-based method for testing hypotheses at the community level. We have also developed the linear decomposition model (LDM) that unifies the community-level and OTU-level tests into one framework. Here we present a strategy that can be used with both PERMANOVA and the LDM for analyzing matched-set data. We propose to include an indicator variable for each set as covariates, so as to constrain comparisons between samples within a set, and also permute traits within each set, which can account for exchangeable sample correlations. The flexible nature of PERMANOVA and the LDM allows discrete or continuous traits or interactions to be tested, within-set confounders to be adjusted, and unbalanced data to be fully exploited. ResultsOur simulations indicate that our proposed strategy outperformed alternative strategies in a wide range of scenarios. Using simulation, we also explored optimal designs for matched-set studies. The flexibility of PERMANOVA and the LDM for a variety of matched-set microbiome data is illustrated by the analysis of data from two real studies. ConclusionsIncluding set indicator variables and permuting within sets when analyzing matched-set data with PERMANOVA or the LDM is a strategy that performs well and is capable of handling the complex data structures that frequently occur in microbiome studies.

bioinformatics↗

bioRxiv2017

Testing hypotheses about the microbiome using an ordination-based linear decomposition model

MotivationMethods for analyzing microbiome data generally fall into one of two groups: tests of the global hypothesis of any microbiome effect, which do not provide any information on the contribution of individual operational taxonomic units (OTUs); and tests for individual OTUs, which do not typically provide a global test of microbiome effect. Without a unified approach, the findings of a global test may be hard to resolve with the findings at the individual OTU level. Further, many tests of individual OTU effects do not preserve the false discovery rate (FDR). ResultsWe introduce the linear decomposition model (LDM), that provides a single analysis path that includes global tests of any effect of the microbiome, tests of the effects of individual OTUs while accounting for multiple testing by controlling the FDR, and a connection to distance-based ordination. The LDM accommodates both continuous and discrete variables (e.g., clinical outcomes, environmental factors) as well as interaction terms to be tested either singly or in combination, allows for adjustment of confounding covariates, and uses permutation-based p-values that can control for correlation. The LDM can also be applied to transformed data, and an "omnibus" test can easily combine results from analyses conducted on different transformation scales. We also provide a new implementation of PERMANOVA based on our approach. For global testing, our simulations indicate the LDM provided correct type I error and can have comparable power to existing distance-based methods. For testing individual OTUs, our simulations indicate the LDM controlled the FDR well. In contrast, DESeq2 often had inflated FDR; MetagenomeSeq generally had the lowest sensitivity. The flexibility of the LDM for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. We also show that our implementation of PERMANOVA can outperform existing implementations.

bioinformatics↗