Biology subjects

Hu, Y.-J.

Publications and source records attributed to Hu, Y.-J..

12 recordsLinked to original sources

bioRxiv2026

TestNet-PA: Sampling-Zero-Aware Inference of Microbial Presence-Absence Networks

Microbial presence-absence networks, also referred to as co-occurrence networks, characterize patterns of coexistence and incompatibility among taxa. However, sequencing-based presence--absence analysis can be compromised by pervasive 'sampling zeros' in microbiome data, causing widely used methods such as Fisher's exact test to identify spurious co-occurrence dependence. To our knowledge, no existing method is designed to account for sampling zeros in this setting. We introduce TestNet-PA, a method for testing pairwise microbial co-occurrence dependence. TestNet-PA models each observed zero as arising from a mixture of 'structural' and 'sampling' zeros, with the mixing probabilities governed by library size and relative abundance. Because the relative abundance underlying an observed zero is unobserved, we impute it by borrowing information from other samples. We then construct a quasi-likelihood and develop an EM algorithm to infer dependence between the underlying true presence--absence statuses. Extensive simulations based on real gut, vaginal, and oral microbiome data templates showed that TestNet-PA consistently controlled the FDR while maintaining good sensitivity. By contrast, Fisher's exact test generally yielded inflated FDR. Analyses of the three real microbiome datasets further illustrated how accounting for sampling zeros can affect the inferred co-occurrence networks.

bioinformatics↗

bioRxiv2026

SMS: Symmetric Mediation Statistics for Powerful High-Dimensional Mediation Analysis

BackgroundMediation analysis of high-dimensional features, particularly molecular-level omics features, provides important opportunities to uncover biological mechanisms underlying human health and disease. However, two central statistical challenges remain: testing the composite-null hypothesis and maintaining power when the exposure-mediator and mediator-outcome associations differ substantially in statistical significance. Existing methods typically rely on accurate estimation of the proportions of the three null types or on the maximum of the two association p-values, and may not always control the FDR well and may have limited power under imbalanced significance. MethodsWe propose SMS, a new statistical framework based on symmetric mediation statistics. By exploiting symmetry, SMS calibrates the composite null distribution as a whole for FDR control. It also allows flexible combinations of the two association p-values, including the maximum, and then enables construction of an omnibus test. Moreover, it permits direct use of effect-size estimates, bypassing the need to compute p-values. ResultsSMS controlled the FDR across a wide range of simulation scenarios while achieving a substantial sensitivity gain, often around 20 percentage points, over existing methods including HDMT, DACT, and DEI-B. Applications to a metabolomics dataset and a DNA methylation dataset further corroborated these findings. Notably, SMS discovered five plausible mediators in the metabolomics dataset that were missed by all existing methods considered.

bioinformatics↗

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↗

bioRxiv2023

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 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. AUTHOR SUMMARYYoung men who have sex with men (YMSM) are disproportionately affected by HIV and asymptomatic bacterial sexually transmitted infections (STI) including gonorrhea, chlamydia, and syphilis. However, the health effects of these infections are not typically studied together. In this study, we enrolled YMSM ages 18-29 with and without HIV and/or asymptomatic bacterial STI to study the immunologic effects of these infections, and their interactions, on the rectal mucosa. We found that HIV was associated with differences in the cellular make-up of the rectal tissues, and that STI was associated with an increase in the detection of potentially dangerous bacteria in the rectum. When we examined tissue gene expression, we found that STI was associated with inflammation only among YMSM with HIV, but not those without HIV. We did not see an effect of STI on differences in tissue viral loads among YMSM with HIV or in HIV replication in rectal explant experiments in YMSM without HIV. 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.

immunology↗

bioRxiv2022

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

BackgroundTo 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. ResultsWe proposed a joint model with matching and regularization (JMR) to detect OTU-specific compositional trajectory predictive of host disease status, using nested random effects and covariate taxa pre-selected by Bray-Curtis distance and elastic net regression. The inherent negative correlation in microbiota composition was adjusted by incorporating the top-correlated taxa as covariate. We designed a simulation pipeline to generate true biomarkers for disease onset and the pseudo biomarkers caused by compositionality or latent noises. We demonstrated that JMR effectively controlled the false discovery and pseudo biomarkers in a simulation study that generated 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. ConclusionOur method JMR is a robust framework that models taxon-specific compositional trajectory and host disease status in the matched participants, improving the power of detecting disease-predictive microbial features in certain scenarios.

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

A New Approach to Testing Mediation of the Microbiome using the LDM

BackgroundUnderstanding whether and which microbes played a mediating role between an exposure and a disease outcome are essential for researchers to develop clinical interventions to treat the disease by modulating the microbes. Existing methods for mediation analysis of the microbiome are often limited to a global test of community-level mediation or selection of mediating microbes without control of the false discovery rate (FDR). Further, while the null hypothesis of no mediation at each microbe is a composite null that consists of three types of null (no exposure-microbe association, no microbe-outcome association given the exposure, or neither), most existing methods for the global test such as MedTest and MODIMA treat the microbes as if they are all under the same type of null. MethodsWe propose a new approach based on inverse regression that regresses the (possibly transformed) relative abundance of each taxon on the exposure and the exposure-adjusted outcome to assess the exposure-taxon and taxon-outcome associations simultaneously. Then the association p-values are used to test mediation at both the community and individual taxon levels. This approach fits nicely into our Linear Decomposition Model (LDM) frame-work, so our new method is implemented in the LDM and enjoys all the features of the LDM, i.e., allowing an arbitrary number of taxa to be tested, supporting continuous, discrete, or multivariate exposures and outcomes as well as adjustment of confounding covariates, accom-modating clustered data, and offering analysis at the relative abundance or presence-absence scale. We refer to this new method as LDM-med. ResultsUsing extensive simulations, we showed that LDM-med always controlled the type I error of the global test and had compelling power over existing methods; LDM-med always preserved the FDR of testing individual taxa and had much better sensitivity than alternative approaches. In contrast, MedTest and MODIMA had severely inflated type I error when different taxa were under different types of null. The flexibility of LDM-med for a variety of mediation analyses is illustrated by the application to a murine microbiome dataset. Availability and ImplementationOur new method has been added to our R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM.

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↗

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↗

bioRxiv2019

Effect of Exogenous Amino Acids on Yield and Quality of Tartary Buckwheat in Non-saline and Saline-alkali Soil

Disclaimer StatementThe authors have withdrawn this manuscript (BIORXIV/2019/887778) entitled "Effect of Exogenous Amino Acids on Yield and Quality of Tartary Buckwheat in Non-saline and Saline-alkali Soil" because it requires adding new data and further modifications. Therefore, the authors do not wish this work to be cited (https://www.biorxiv.org/content/10.1101/2019.12.24.887778v5) as reference for the project. If you have any questions, please contact the corresponding author.

physiology↗

bioRxiv2017

Identifying tagging SNPs for African specific genetic variation from the African Diaspora Genome

A primary goal of The Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) is to develop an African Diaspora Power Chip (ADPC), a genotyping array consisting of tagging SNPs, useful in comprehensively identifying African specific genetic variation. This array is designed based on the novel variation identified in 642 CAAPA samples of African ancestry with high coverage whole genome sequence data (~30x depth). This novel variation extends the pattern of variation catalogued in the 1000 Genomes and Exome Sequencing Projects to a spectrum of populations representing the wide range of West African genomic diversity. These individuals from CAAPA also comprise a large swath of the African Diaspora population and incorporate historical genetic diversity covering nearly the entire Atlantic coast of the Americas. Here we show the results of designing and producing such a microchip array. This novel array covers African specific variation far better than other commercially available arrays, and will enable better GWAS analyses for researchers with individuals of African descent in their study populations. A recent study1 cataloging variation in continental African populations suggests this type of African-specific genotyping array is both necessary and valuable for facilitating large-scale GWAS in populations of African ancestry.

genomics↗