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Variable selection for sparse Dirichlet-multinomial regression with an application to microbiome data analysis
Open accessAbstract
With the development of next generation sequencing technology, researchers have now been able to study the microbiome composition using direct sequencing, whose output are bacterial taxa counts for each microbiome sample. One goal of microbiome study is to associate the microbiome composition with environmental covariates. We propose to model the taxa counts using a Dirichlet-multinomial (DM) regression model in order to account for overdispersion of observed counts. The DM regression model can be used for testing the association between taxa composition and covariates using the likelihood ratio test. However, when the number of the covariates is large, multiple testing can lead to loss of power. To deal with the high dimensionality of the problem, we develop a penalized likelihood approach to estimate the regression parameters and to select the variables by imposing a sparse group [Formula: see text] penalty to encourage both group-level and within-group sparsity. Such a variable selection procedure can lead to selection of the relevant covariates and their associated bacterial taxa. An efficient block-coordinate descent algorithm is developed to solve the optimization problem. We present extensive simulations to demonstrate that the sparse DM regression can result in better identification of the microbiome-associated covariates than models that ignore overdispersion or only consider the proportions. We demonstrate the power of our method in an analysis of a data set evaluating the effects of nutrient intake on human gut microbiome composition. Our results have clearly shown that the nutrient intake is strongly associated with the human gut microbiome.
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“While both the multinomial Dirichlet model and the multinomial logistic normal model can handle over-dispersed count data70,71, the logistic normal model also allows for both positive and negative covariation between microbial features69.”
From Longitudinal gut microbiome changes in immune checkpoint blockade-treated advanced melanoma (2024) · cites this paper as [70]
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Items 101–191 of 191
Tian, X., Wang, X., & Chen, J. (2014) · Cancer Informatics · doi:10.4137/cin.s17686 · Cited by 12
Self-citationUnivariate and Multivariate Statistical Analysis of Microbiome Data: An Overview
Aldirawi, H., & Morales, F. G. (2023) · Applied Microbiology · doi:10.3390/applmicrobiol3020023 · Cited by 12
Splitting models for multivariate count data
Peyhardi, J., Fernique, P., & Durand, J.-B. (2020) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2020.104677 · Cited by 11
“(2013) and Chen and Li (2013) studied the microbiome composition (whose output are J bacterial taxa counts) and Zhang et al. (2017) studied the expression count of J exon sets. However, themultinomial andDirichlet multinomial distributions are not appropriate for multivariate count data because of their support: the d…”
BackgroundInfluentialTracking the Evolution of Literary Style via Dirichlet–Multinomial Change Point Regression
Ross, G. J. (2019) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12492 · Cited by 11
“2, we model this type of parameter drift by using a set logistic regression models with a linear drift specification (Chen and Li, 2013), which in the case of the Dirichlet–multinomial distribution can be written as αi,j = exp.β0j + iβ1j / .2/ where αi,j again denotes the jth element of αi for the…”
MethodPrediction Analysis for Microbiome Sequencing Data
Wang, T., Yang, C., & Zhao, H. (2019) · Biometrics · doi:10.1111/biom.13061 · Cited by 11
“Since ∈zy p−1 , two popular priors for zy are the Dirichlet distribution (Chen and Li, 2013) and the additive logistic normal distribution (Xia et al., 2013).”
BackgroundMethodSufficient dimension reduction for compositional data
Tomassi, D., Forzani, L., Duarte, S., & Pfeiffer, R. M. (2019) · Biostatistics · doi:10.1093/biostatistics/kxz060 · Cited by 11
“Other approaches to analyze microbiome data in relation to outcomes include sparse canonical correlation analysis using relative abundances (Chen and others, 2013), linear models with linear constraints on the regression coefficients for continuous outcomes (Shi and others, 2016), and extensions of multinomial regress…”
BackgroundMethodFan, Z., Kernan, K. F., Sriram, A., Benos, P. V., Canna, S., Carcillo, J. A., Kim, S., & Park, H. J. (2022) · GigaScience · doi:10.1093/gigascience/giad044 · Cited by 10
“In particular, the nutrient v alues wer e normalized using the residual method to adjust for caloric intake and then standardized [ 118 ].”
Statistical and Computational Methods in Microbiome and Metagenomics
Li, H. (2019) · doi:10.1002/9781119487845.ch35 · Cited by 10
Self-citationXu, X., Lubomski, M., Holmes, A. J., Sue, C. M., Davis, R. L., Müller, S., & Yang, J. Y. H. (2023) · Microbiome · doi:10.1186/s40168-023-01475-4 · Cited by 10
Scalable Bayesian variable selection regression models for count data
Miao, Y., Kook, J. H., Lu, Y., et al. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-815862-3.00015-9 · Cited by 8
“We considered the squared root transformed values of taxa abundance, similarly as in [9].”
MethodResultInfluentialSubject-specific Dirichlet-multinomial regression for multi-district microbiota data analysis
Pedone, M., Amedei, A., & Stingo, F. C. (2023) · The Annals of Applied Statistics · doi:10.1214/22-aoas1641 · Cited by 7
Shi, Y., Zhang, L., Do, K.-A., Jenq, R. R., & Peterson, C. B. (2023) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1093/jrsssc/qlac002 · Cited by 7
A Dirichlet-Multinomial Bayes Classifier for Disease Diagnosis with Microbial Compositions
Gao, X., Lin, H., & Dong, Q. (2017) · mSphere · doi:10.1128/mspheredirect.00536-17 · Cited by 7
“The DM distribution (8, 9, 18) is defined as where T is the total number of microbial taxa (i.e., the number of categories) identified from the sequencing, N is the total number of sequence reads (i.e., N independent trials); s (cid:2) ( s 1 , . . . , s t ), representing the number of sequence reads…”
BackgroundA Multivariate Mixture Regression Model for Constrained Responses
Ascari, R., Brisco, A. M. D., Migliorati, S., et al. (2023) · Bayesian Analysis · doi:10.1214/22-ba1359 · Cited by 6
Omic-style statistical clustering reveals old and new patterns in the Gulf of Maine ecosystem
Record, N. R., O’Brien, J., Stamieszkin, K., et al. (2016) · Canadian Journal of Fisheries and Aquatic Sciences · doi:10.1139/cjfas-2016-0151 · Cited by 6
“The past few years have seen an assortment of39 different approaches for analyzing these data, including regression (Chen et al 2012, Chen & Li 2013),40 co-occurrence networks (Biswas et al 2015), and ecological consortia inferral (Holmes et al 2012).41 These methods have proven essential to…”
MethodBayesian Sparse Multivariate Regression with Asymmetric Nonlocal Priors for Microbiome Data Analysis
Shuler, K., Sison-Mangus, M. P., & Lee, J. (2019) · Bayesian Analysis · doi:10.1214/19-ba1164 · Cited by 6
“Chen and Li (2013) also used a sparse group (cid:2) 1 penalty with a MD regression model.”
BackgroundMethodTesting hypotheses about the microbiome using the linear decomposition model (LDM)Preprint
Hu, Y.-J., & Satten, G. A. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/229831 · Cited by 6
Methodology for microbiome data analysis: An overview
Creus-Martí, I., Moyá, A., & Gómez, F. J. S. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110157 · Cited by 9
Principal Amalgamation Analysis for Microbiome Data
Li, Y., Li, G., & Chen, K. (2022) · Genes · doi:10.3390/genes13071139 · Cited by 5
“The selection approach [31,32], which only keeps a subset of “dominant” compositions, often ignores intrinsic relations due to compositionality and can be vulnerable to the extremely low abundances of many OTUs.”
multiMiAT: an optimal microbiome-based association test for multicategory phenotypes
Sun, H. L., Wang, Y., Xiao, Z. Z., Huang, X., Wang, H., He, T., & Jiang, X. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad012 · Cited by 7
Tvedebrink, T., Eriksen, P. S., & Morling, N. (2015) · Theoretical Population Biology · doi:10.1016/j.tpb.2015.08.004 · Cited by 5
Logistic Normal Multinomial Factor Analyzers for Clustering Microbiome Data
Tu, W., & Subedi, S. D. (2023) · Journal of Classification · doi:10.1007/s00357-023-09452-0 · Cited by 5
“A Dirichlet-multinomial model has been widely used for modelling microbiome data (La Rosa et al., 2012; Chen & Li, 2013; Wadsworth et al., 2017; Koslovsky & Vannucci, 2020).”
MethodStructure-adaptive canonical correlation analysis for microbiome multi-omics data
Deng, L., Tang, Y., Zhang, X., & Chen, J. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2024.1489694 · Cited by 4
“We followed Chen (2013) to pre-process data.”
InfluentialSelf-citationHigh-dimensional statistical inference: Theoretical development to data analytics
Ayyala, D. N. (2020) · Handbook of statistics · doi:10.1016/bs.host.2020.02.003 · Cited by 4
“Chen and Li [21] developed a `1-penalized parameter estimation for variable selection in the DirMult model.”
MethodSubedi, S. D., & Dang, U. J. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.03.017 · Cited by 4
“Thus, Dirichlet-multinomial distribution-based approaches have been widely used to cluster microbiome data [7,52,23] However, the limited number of parameters in the DM model may not adequately capture the complex covariance pattern observed in the microbiome data [54].”
Likelihood ratios for categorical count data with applications in digital forensics
Longjohn, R., Smyth, P., & Stern, H. S. (2022) · Law Probability and Risk · doi:10.1093/lpr/mgac016 · Cited by 4
Personalized treatment selection via product partition models with covariates
Pedone, M., Argiento, R., & Stingo, F. C. (2024) · Biometrics · doi:10.1093/biomtc/ujad003 · Cited by 4
“1) is robust with respect to overdispersion (Corsini and Viroli, 2022), which is usually observed in multivariate categorical data (Chen and Li, 2013).”
BackgroundIFAA: Robust Association Identification and Inference for Absolute Abundance in Microbiome Analyses
Li, Z., Tian, L., O’Malley, A. J., Karagas, M. R., Hoen, A. G., Christensen, B. C., Madan, J. C., Wu, Q., Gharaibeh, R. Z., Jobin, C., et al. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1860770 · Cited by 7
Self-citationLivestock market data for modeling disease spread among US cattlePreprint
Carroll, I. T., & Bansal, S. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/021980 · Cited by 4
A Bayesian joint model for compositional mediation effect selection in microbiome data
Fu, J., Koslovsky, M. D., Neophytou, A. M., & Vannucci, M. (2023) · Statistics in Medicine · doi:10.1002/sim.9764 · Cited by 3
“Examples of recent developments include sparsity-induced univariate and multivariate count regression models to identify exposures that characterize the composition of the microbiome (Chen and Li, 2013; Jiang et al., 2021; Koslovsky and Vannucci, 2020; Liu et al., 2021; Wadsworth et al., 2017; Xu et al., 2015; Zhang a…”
BackgroundPursuing Homogeneity and Sparsity in Simultaneous Quantile Regression
Zeng, Z., & Yao, W. (2025) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2025.2460600 · Cited by 3
“These results suggest that PHISQ-AL is very effective for identifying predictors of homogeneity and heterogeneity. that LAT and INDUS are not significant at any quantile level, a conclusion also reached by Chen and Li (2013).”
A GLM-based zero-inflated generalized Poisson factor model for analyzing microbiome data
Chi, J., Ye, J., & Zhou, Y. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1394204 · Cited by 3
Two-step mixed-type multivariate Bayesian sparse variable selection with shrinkage priors
Wang, S.-H., Bai, R., & Huang, H.-H. (2022) · Electronic Journal of Statistics · doi:10.1214/25-ejs2343 · Cited by 12
“However, this scenario is plausible, for example, in microbiome differential abundance analysis in which there are large number of environmental outcomes [14, 5].”
Functional response regression model on correlated longitudinal microbiome sequencing data
Chen, B., & Xu, W. (2021) · Statistical Methods in Medical Research · doi:10.1177/09622802211061634 · Cited by 3
Clustering multivariate count data via Dirichlet-multinomial network fusion
Zhao, X. Y., Zhang, J., & Lin, W. C. (2022) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2022.107634 · Cited by 3
Generally Altered, Inflated, Truncated and Deflated Regression
Yee, T. W., & Ma, C. (2024) · Statistical Science · doi:10.1214/24-sts925 · Cited by 3
Rios, N., Xue, L., & Zhan, X. (2024) · The Annals of Applied Statistics · doi:10.1214/24-aoas1935 · Cited by 3
A New Dirichlet‐Multinomial Mixture Regression Model for the Analysis of Microbiome Data
Ascari, R., Migliorati, S., & Ongaro, A. (2025) · Statistics in Medicine · doi:10.1002/sim.70220 · Cited by 2
“Although this additional parameter greatly improves the fit of the model (e.g., see Chen and Li [19] and Wadsworth et al. [20]), the DM model is still often too rigid to deal with real complex datasets, as we shall show.”
InfluentialShen, Y., Rajaram, S. V., Wang, W., & Zeng, E. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.21.670443 · Cited by 3
“Dirichlet-multinomial regression models [11], gamma-hypergeometric models such as MetaSPARSim [12], and hierarchical frameworks like SparseDOSSA [13] impose fixed count distributions or hierarchical priors that may not reflect real-world variability.”
A flexible mixed-membership model for community and enterotype detection for microbiome data
Giampino, A., Ascari, R., & Migliorati, S. (2025) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2025.108181 · Cited by 2
MB-DDPM: microbiome simulation based on the denoising diffusion probabilistic model
Huo, B., Tan, Y., Zhu, R., et al. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103880 · Cited by 2
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A modified multinomial baseline logit model with logit functions having different covariates
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Networks for Compositional Data
Ma, J., Yue, K., & Shojaie, A. (2021) · Frontiers in probability and the statistical sciences · doi:10.1007/978-3-030-73351-3_13 · Cited by 2
A hierarchical Bayesian approach for detecting global microbiome associations
Hatami, F., Beamish, E. L., Davies, A. H., et al. (2021) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2021-0047 · Cited by 2
“Regression-based methods tend to treat the count data as following either a negative binomial (Zhang et al. 2017) or Dirichlet-multinomial distribution (Chen and Li 2013).”
MethodInfluentialHajihosseini, M., Amini, P., Saidi‐Mehrabad, A., Hajizadeh, N., Kozyrskyj, A. L., & Dinu, I. A. (2024) · Statistics in Biosciences · doi:10.1007/s12561-024-09435-8 · Cited by 2
asmbPLS: biomarker identification and patient survival prediction with multi-omics data
Zhang, R., & Datta, S. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2024.1444054 · Cited by 2
Koslovsky, M. D. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06078-4 · Cited by 2
A nonparametric spatial test to identify factors that shape a microbiome
Singh, S. P., Staicu, A., Dunn, R. R., Fierer, N., & Reich, B. J. (2019) · The Annals of Applied Statistics · doi:10.1214/19-aoas1262 · Cited by 2
“Additionally, the proposed methods in Chen and Li (2013) and Wang and Zhao (2017) rely on optimization routines that may not be suitable for problems with thousands of sample locations and tens of thousands of taxa.”
MethodBayesian variable selection for high‐dimensional rank data
Cui, C., Singh, S. P., Staicu, A., et al. (2021) · Environmetrics · doi:10.1002/env.2682 · Cited by 2
A finite mixture model for X‐chromosome association with an emphasis on microbiome data analysis
Espin‐Garcia, O., Croitoru, K., & Xu, W. (2019) · Genetic Epidemiology · doi:10.1002/gepi.22190 · Cited by 2
“Furthermore, analysis at microbial community‐level is sometimes desirable (Chen & Li, 2013; La Rosa et al., 2012; Tang, Chen, Alekseyenko, & Li, 2017; Xu, Paterson, & Xu, 2017) although it exacerbates the aforementioned challenges.”
BackgroundRen, S., Kang, E. L., & Lu, J. L. (2019) · Statistics and Computing · doi:10.1007/s11222-019-09880-2 · Cited by 2
Statistical Methods for Analyzing Tree-Structured Microbiome Data
Wang, T., & Zhao, H. (2021) · Frontiers in probability and the statistical sciences · doi:10.1007/978-3-030-73351-3_8 · Cited by 2
Logic-Driven Traffic Big Data Analytics: An Introduction
Zhong, S., & Sun, D. (2022) · Springer eBooks · doi:10.1007/978-981-16-8016-8_1 · Cited by 2
Chekouo, T., Stingo, F. C., Mohammed, S., Rao, A. U. K., & Baladandayuthapani, V. (2023) · The Annals of Applied Statistics · doi:10.1214/23-aoas1749 · Cited by 2
GMPR: A novel normalization method for microbiome sequencing dataPreprint
Chen, L., & Chen, J. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/112565 · Cited by 2
“Briefly, we first generate zeroinflated count data based on a Dirichlet-multinomial model with known library sizes (Chen and Li, 2013).”
MethodSelf-citationMulti-omics Integration of Microbiota Transplant Therapy in Children with Autism Spectrum DisordersPreprint
Mallick, H., Nirmalkar, K., Adams, J. B., & Krajmalnik‐Brown, R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.10.681677 · Cited by 3
Statistical models and computational algorithms for discovering relationships in microbiome data
Shaikh, M. R., & Beyene, J. (2016) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2015-0096 · Cited by 1
Dirichlet-Multinomial Regression Models with Bayesian Variable Selection for Microbiome Data
Koslovsky, M. D., & Vannucci, M. (2021) · Frontiers in probability and the statistical sciences · doi:10.1007/978-3-030-73351-3_10 · Cited by 1
Tree Pólya Splitting distributions for multivariate count data
Valiquette, S., Peyhardi, J., Marchand, É., et al. (2025) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2025.105507 · Cited by 1
Choi, J. T., Yoon, I., Jung, Y., et al. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag195 · Cited by 1
“DMR extends latent Dirichlet allocation by incorporating external metadata, such as publication year, allowing topic distributions to be conditioned on temporal trends [44–47].”
baldan, M., Baruzzo, G., Mariotto, P., Rossato, A., Cappellato, M., & CAMILLO, B. D. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2026.1783447 · Cited by 2
“…address the above simulation challenges using ad hoc statistical modeling such as Multinomial (McMurdie and Holmes, 2014), Dirichlet-Multinomial (Chen and Li, 2013), Gamma-Multivariate Hypergeometric (Patuzzi et al., 2019; Baruzzo et al., 2019) and large hierarchical models (Ma et al., 2021a).”
Movement dynamics in elite female soccer athletes: the quantile cube approach
Thomas, K. L., & Hannig, J. (2026) · Journal of Quantitative Analysis in Sports · doi:10.1515/jqas-2025-0014 · Cited by 1
Statistical Challenges for Human Microbiome Analysis
the MetaHIV-Pheno Study Group, Rivera-Pinto, J., Estany, C., et al. (2017) · Trends in mathematics · doi:10.1007/978-3-319-55639-0_8 · Cited by 1
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Innovated scalable efficient inference for ultra-large graphical models
Zhou, J., Zheng, Z., Zhou, H., et al. (2021) · Statistics & Probability Letters · doi:10.1016/j.spl.2021.109085 · Cited by 1
Fan, Z., Kernan, K. F., Benos, P. V., Canna, S., Carcillo, J. A., Kim, S., & Park, H. J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.17.452800 · Cited by 2
“Following (18), 0s are replaced with 0.”
MethodZhang, R., & Datta, S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.03.535442 · Cited by 1
“The microbiome data is simulated using the Dirichlet-multinomial (DM) distribution [27] to 99”
MethodEmerging Statistical Methodologies in the Field of Microbiome Studies
Mandal, S. (2017) · Handbook of statistics · doi:10.1016/bs.host.2017.09.004
Okui, T., & Nakaji, S. (2018) · Japanese Journal of Biometrics · doi:10.5691/jjb.39.37
Bayesian Nonparametric Topic Model Using an Outcome Variable For Microbial Data
Okui, T. (2019) · Ouyou toukeigaku · doi:10.5023/jappstat.48.1
A Bayesian Multi-Task Approach for Detecting Global Microbiome AssociationsPreprint
Hatami, F., Beamish, E. L., Rigby, R. J., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.08.897538
“, 2017) or Dirichlet-multinomial distribution (Chen and Li, 2013).”
MethodInfluentialVariational Bayesian inference for association over phylogenetic trees for microorganisms
Hao, X., Eskridge, K. M., & Wang, D. (2020) · Journal of Applied Statistics · doi:10.1080/02664763.2020.1854200
“Examples include PERMANOVA [2], Kernel regression [21], Dirichlet multinomial regression [6], quasi-conditional association test [19], among others.”
MethodLovell, D. R. (2021) · Springer eBooks · doi:10.1007/978-3-030-71175-7_3
Joint Models for Repeatedly Measured Compositional and Normally Distributed Outcomes
Martin, I., Uh, H., & Houwing‐Duistermaat, J. J. (2021) · Frontiers in probability and the statistical sciences · doi:10.1007/978-3-030-73351-3_6
Learning to increase the power of conditional randomization tests
Shaer, S., & Romano, Y. (2023) · Machine Learning · doi:10.1007/s10994-023-06302-3
“This information, in turn, could be leveraged to develop diets that improve human health (Chen & Li, 2013).”
BackgroundA New Regression Model for the Analysis of Microbiome Data
Ascari, R., & Migliorati, S. (2023) · Studies in classification, data analysis, and knowledge organization · doi:10.1007/978-3-031-09034-9_5
Decoding the Microbiome-Metabolome Nexus: A Systematic Benchmark of Integrative StrategiesPreprint
Mangnier, L., Bodein, A., Mariaz, M., Scott‐Boyer, M., Mathieu, A., Vashist, N., Bramble, M. S., & Droit, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.26.577441
Datta, J., & Bandyopadhyay, D. (2024) · Journal of the Indian Society for Probability and Statistics · doi:10.1007/s41096-024-00194-9
“Specifically, we cast our microbiome regression framework into a Dirichlet-Multinomial (D-M) specification (Chen and Li 2013) under a Bayesian paradigm, and explore the performance of the G-L priors, specifically, the horseshoe (Carvalho et al. 2010) and horseshoe+ (Bhadra Journal of the Indian…”
MethodDetecting Association in Microbiome Compositional Data: A Novel Approach
Monti, G. S., & Pelagatti, M. M. (2025) · Springer eBooks · doi:10.1007/978-3-031-64350-7_18
Variational inference for microbiome survey data with application to global ocean data
Mishra, A. K., McNichol, J. C., Fuhrman, J. A., Blei, D. M., & Müller, C. L. (2025) · ISME Communications · doi:10.1093/ismeco/ycaf062
“Thecomplexityandinterconnected nature of microbial ecosystems would, however, benefit from integrative approaches that go beyond estimating individual statistical associations or taxon–taxon associations.”
Cluster analysis of longitudinal profiles for compositional count data
Duan, C., & Jiang, Y. (2025) · The Annals of Applied Statistics · doi:10.1214/24-aoas2001
Debiased group lasso for multiple compositional data
Lee, S., & Jung, S. (2025) · Annals of the Institute of Statistical Mathematics · doi:10.1007/s10463-025-00956-7
“Existing methodologies, such as the linear log-contrast models (Aitchison, 1982; Aitchison and Bacon-Shone, 1984; Lin et al., 2014; Chen and Li, 2013; Bates and Tibshirani, 2019; Susin et al., 2020), are well-suited for analyzing single compositional datasets.”
Sakamaki, Y. (2025) · Journal of Information Processing · doi:10.2197/ipsjjip.33.776
Metagenomics: Concepts and scope
Tyagi, S., Kanchan, S., Kesheri, M., & Katara, P. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-21980-1.00004-8
Yang, L., & Chen, J. (2022) · Figshare · doi:10.6084/m9.figshare.20521398
Self-citationA Bayesian factor analysis model for high-dimensional microbiome count data
Ba, I., Turgeon, M., Veniamin, S., et al. (2026) · AStA Advances in Statistical Analysis · doi:10.1007/s10182-026-00569-2
“It is worth pointing out that the Dirichlet multinomial model (Mosi-mann, 1962; Chen and Li, 2013) assumes a Dirichlet distribution for modeling the taxa composition, which helps address over-dispersion, often observed in microbiome studies.”
Additional file 1 of MIDASim: a fast and simple simulator for realistic microbiome data
He, M., Zhao, N., & Satten, G. (2024) · Figshare · doi:10.6084/m9.figshare.26752399
NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis
Jun, S.-H., Halushka, M. K., & McCall, M. N. (2026) · Biostatistics · doi:10.1093/biostatistics/kxag012 · Cited by 3
“A closely related alternative is Dirichlet–multinomial (DM) regression, which replaces the negative binomial with a multinomial likelihood (La Rosa et al. 2012; Chen and Li 2013; Li 2015).”
A critical comparison of handling zeros in high-dimensional compositional count data
Tang, W., Fačevicová, K., Nordhausen, K., et al. (2026) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2026.105821
“This Dirichlet-based construction followed by scaling is consistent with simulation strategies used in microbiome studies, including Chen and Li (2013).”
Phenotypic heterogeneity during sperm capacitation: a compositional analysis
Brisard, B. M., Halder, A. P., Charles, A. C., et al. (2026) · Biology of Reproduction · doi:10.1093/biolre/ioag207
Metadata
- Authors
- Chen, J., & Li, H.
- Affiliations
- University of Pennsylvania (US)
- Venue
- The Annals of Applied Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 2013-03-01
- Volume
- 7
- Issue
- 1
- Pages
- 418-442
- ISSN
- 1932-6157 (ISSN-L)
- 1941-7330
- PMC
- PMC3846354
- OpenAlex
- W2124053577
- Cited by
- 191
- References
- 21
- Citations by year
- 2026: 7 · 2025: 18 · 2024: 14 · 2023: 15 · 2022: 14 · 2021: 20 · 2020: 24 · 2019: 23 · 2018: 13 · 2017: 19 · 2016: 8 · 2015: 9 · 2014: 5 · 2013: 2
- FWCI
- 7.07
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- The Annals of Applied Statistics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Project Euclid (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Cancer Institute (R01 CA127334)
- National Institute of General Medical Sciences (R01GM097505)
- National Institute of Diabetes and Digestive and Kidney Diseases (UH2 DK083981)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- feature selection, microbiota composition, overdispersion, high-dimensional regression, block coordinate descent, nutrient intake, gut microbiota, multiple comparisons
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