The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
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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Cites
QIIME allows analysis of high-throughput community sequencing data
Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., et al. (2010) · Nature Methods · doi:10.1038/nmeth.f.303 · Cited by 34,887 · cited 2×
“…are either mapped to an existing phylogenetic tree in a taxonomic dependent way [e.g., Mat-sen, Kodner and Armbrust (2010)] or clustered into operational taxonomic units (OTUs) at a certain similarity level in a taxonomic independent way [e.g., Caporaso et al. (2010), Schloss et al. (2009)].”
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H., Robinson, C. J., et al. (2009) · Applied and Environmental Microbiology · doi:10.1128/aem.01541-09 · Cited by 19,549 · cited 1×
“…are either mapped to an existing phylogenetic tree in a taxonomic dependent way [e.g., Mat-sen, Kodner and Armbrust (2010)] or clustered into operational taxonomic units (OTUs) at a certain similarity level in a taxonomic independent way [e.g., Caporaso et al. (2010), Schloss et al. (2009)].”
Linking long-term dietary patterns with gut microbial enterotypes
Wu, G. D., Chen, J., Hoffmann, C., Bittinger, K., Chen, Y.-Y., Keilbaugh, S. A., Bewtra, M., Knights, D., Walters, W. A., Knight, R., et al. (2011) · Science · doi:10.1126/science.1208344 · Cited by 6,513 · cited 10×
“Details of the study and the data set can be found in Section 6 and in Wu et al. (2011).”
BackgroundMethodResultInfluentialSelf-citationHost-Bacterial Mutualism in the Human Intestine
Bäckhed, F., Ley, R. E., Sonnenburg, J. L., Peterson, D. A., & Gordon, J. I. (2005) · Science · doi:10.1126/science.1104816 · Cited by 5,277 · cited 1×
“The collective genomes of these microbes constitute an extended human genome that provides us with genetic and metabolic capabilities that we do not inherently possess [B¨ackhed et al. (2005)].”
BackgroundSokol, H., Pigneur, B., Watterlot, L., Lakhdari, O., Bermúdez-Humarán, L. G., Gratadoux, J.-J., Blugeon, S., Bridonneau, C., Furet, J.-P., Corthier, G., et al. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0804812105 · Cited by 4,413 · cited 1×
“For example, we found that Naringenin (flavanone) was positively associated with Faecalibacterium, an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn’s disease patients [Sokol et al. (2008)].”
BackgroundFITTING MULTIVARIATE MODELS TO COMMUNITY DATA: A COMMENT ON DISTANCE-BASED REDUNDANCY ANALYSIS
McArdle, B. H., & Anderson, M. J. (2001) · Ecology · doi:10.1890/0012-9658(2001)082[0290:fmmtcd]2.0.co;2 · Cited by 4,001 · cited 1×
“en calculated and the signifi- cance is then evaluated by permutation test. Alternatively, one can define a distance between the samples and then use a PERMANOVA procedure to test for covariate effects [McArdle (2001)]. It is easy to show that when the distance is Euclidean, these two procedures are equivalent. In this p…”
MethodThe Statistical Analysis of Compositional Data
Aitchison, J. (1982) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1982.tb01195.x · Cited by 3,457
The Group Lasso for Logistic Regression
Meier, L., van de Geer, S., & Bühlmann, P. (2008) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2007.00627.x · Cited by 1,681 · cited 3×
“· Minimization of (11) with respective to d ¯ Sk can be performed in a similar fashion as in Meier, van de Geer and B¨uhlmann (2008) for the group ℓ 1 penalty.”
InfluentialBenson, A. K., Kelly, S. A., Legge, R., Ma, F., Low, S. J., Kim, J., Zhang, M., Oh, P. L., Nehrenberg, D., Hua, K., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1007028107 · Cited by 1,314 · cited 1×
“Benson et al. (2010) demonstrated that genetic variants are associated with the mouse gut microbiome.”
Matsen, F. A., IV, Kodner, R. B., & Armbrust, E. V. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-538 · Cited by 1,058
Research works printed but not linked: 1
- Legendre, P. and Legendre, L. (2002). Numerical Ecology, 2nd ed. Elsevier, Amsterdam.
Cited by
Sorted by citations
Normalization and microbial differential abundance strategies depend upon data characteristics
Weiss, S. J., Xu, Z. Z., Peddada, S. D., Amir, A., Bittinger, K. L., González, A., Lozupone, C., Zaneveld, J., Vázquez‐Baeza, Y., Birmingham, A., et al. (2017) · Microbiome · doi:10.1186/s40168-017-0237-y · Cited by 2,231
“[50, 67, 68], we include those distributions in our simulation study for purely comparative purposes.”
MethodAnalysis of composition of microbiomes: a novel method for studying microbial composition
Mandal, S., Van Treuren, W., White, R. A., Eggesbø, M., Knight, R., & Peddada, S. D. (2015) · Microbial Ecology in Health & Disease · doi:10.3402/mehd.v26.27663 · Cited by 1,942
“As typically done (13), to avoid sparsely observed OTUs,”
BackgroundSparse and Compositionally Robust Inference of Microbial Ecological Networks
Kurtz, Z. D., Müller, C. L., Miraldi, E. R., Littman, D. R., Blaser, M. J., & Bonneau, R. A. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004226 · Cited by 1,799
“Examples include a novel statistical regression framework for relating microbiome compositions and covariates in the context of nutrient intake [8], observations that microbiome compositions strongly correlate with disease status in new-onset Crohn’s disease [9], and the connections between helminth infection and the…”
BackgroundDai, Z., Coker, O. O., Nakatsu, G., Wu, W. K. K., Zhao, L., Chen, Z., Chan, F. K. L., Kristiansen, K., Sung, J. J. Y., Wong, S. H., et al. (2018) · Microbiome · doi:10.1186/s40168-018-0451-2 · Cited by 545
“To evaluate the statistical power of rank sum metaanalysis method, we simulated microbial composition data following Dirichlet-multinomial distribution [46].”
Modeling microbial abundances and dysbiosis with beta-binomial regression
Martin, B. D., Witten, D., & Willis, A. D. (2020) · The Annals of Applied Statistics · doi:10.1214/19-aoas1283 · Cited by 379
“nd others, 2015; Sankaran and Holmes, 2017). Hierarchical models of absolute abundances are often used to constrain the number of parameters (e.g. La Rosa and others (2012); Holmes and others (2012); Chen and Li (2013); Sankaran and Holmes (2017); Cao and others (2017)). However, modeling the variance structure is cha…”
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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