A logistic normal multinomial regression model for microbiome compositional data analysis

Biometrics

Volumn 69, Issue 4, 2013, Pages 1053-1063

  Xia, Fan ^a   Chen, Jun ^b   Fung, Wing Kam ^a   Li, Hongzhe ^b  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Hierarchical model; Markov chain Monte Carlo; Over dispersion; Regularization; Variable selection

Indexed keywords

DATA HANDLING; DISPERSIONS; HIERARCHICAL SYSTEMS; IMAGE SEGMENTATION; INFORMATION ANALYSIS; MARKOV PROCESSES; MAXIMUM PRINCIPLE; MONTE CARLO METHODS; REGRESSION ANALYSIS;

COVARIATES; HIERARCHICAL MODEL; MARKOV CHAIN MONTE CARLO; MARKOV CHAIN MONTE-CARLO; MICROBIOME; MULTINOMIALS; OVER DISPERSION; REGRESSION MODELLING; REGULARISATION; VARIABLES SELECTIONS;

NUTRIENTS;

BACTERIUM; BIOME; COMMUNITY COMPOSITION; CULTIVATION; DATA SET; IDENTIFICATION METHOD; MICROBIAL ACTIVITY; NUMERICAL MODEL; REGRESSION ANALYSIS; TAXONOMY;

BACTERIA (MICROORGANISMS);

ARTICLE; BACTERIUM; CLASSIFICATION; COMPUTER SIMULATION; GENETICS; HIERARCHICAL MODEL; HUMAN; INTESTINE; ISOLATION AND PURIFICATION; MARKOV CHAIN MONTE CARLO; MICROBIOLOGY; MICROFLORA; OVER-DISPERSION; REGRESSION ANALYSIS; REGULARIZATION; STATISTICAL ANALYSIS; STATISTICAL MODEL; VARIABLE SELECTION;

HIERARCHICAL MODEL; MARKOV CHAIN MONTE CARLO; OVER-DISPERSION; REGULARIZATION; VARIABLE SELECTION;

BACTERIA; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HUMANS; INTESTINES; LOGISTIC MODELS; MICROBIOTA; REGRESSION ANALYSIS;

EID: 84890309776     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/biom.12079     Document Type: Article

References (16)

1. Aitchison, J. (1982). The statistical analysis of compositional data. Journal of the Royal Statistical Society Series B 44, 139-177.
2. Aitchison, J. (1986). The Statistical Analysis of Compositional Data. London, UK: Chapman & Hall, Ltd. here is the complete list of all authors:
3. Arumugam, M., Raes, J., Pelletier, E., Le Paslier, D., Yamada, T., Mende, D. R., Fernandes, G. R., Tap, J., Bruls, T., Batto, J. M., Bertalan, M., Borruel, N., Casellas, F., Fernandez, L., Gautier, L., Hansen, T., Hattori, M., Hayashi, T., Kleerebezem, M., Kurokawa, K., Leclerc, M., Levenez, F., Manichanh, C., Nielsen, H. B., Nielsen, T., Pons, N., Poulain, J., Qin, J., Sicheritz-Ponten, T., Tims, S., Torrents, D., Ugarte, E., Zoetendal, E. G., Wang, J., Guarner, F., Pedersen, O., de Vos, W. M., Brunak, S., Doré, J.; MetaHIT Consortium, Antolín, M., Artiguenave, F., Blottiere, H. M., Almeida, M., Brechot, C., Cara, C., Chervaux, C., Cultrone, A., Delorme, C., Denariaz, G., Dervyn, R., Foerstner, K. U., Friss, C., van de Guchte, M., Guedon, E., Haimet, F., Huber, W., van Hylckama-Vlieg, J., Jamet, A., Juste, C., Kaci, G., Knol, J., Lakhdari, O., Layec, S., Le Roux, K., Maguin, E., Mérieux, A., Melo Minardi, R., M'rini, C., Muller, J., Oozeer, R., Parkhill, J., Renault, P., Rescigno, M., Sanchez, N., Sunagawa, S., Torrejon, A., Turner, K., Vandemeulebrouck, G., Varela, E., Winogradsky, Y., Zeller, G., Weissenbach, J., Ehrlich, S. D., and Bork, P. (2011). Enterotypes of the human gut microbiome. Nature 4, 550-553.
4. Billheimer, D., Guttorp, P., and Fagan, W. F. (2001). Statistical interpretation of species composition. Journal of the American Statistical Association 96, 1205-1214.
5. Caporaso, J., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F., Costello, E., et al. (2010). Qiime allows analysis of high-throughput community sequencing data. Nature Methods 7, 335-336.
6. Chaffron, S., Rehrauer, H., Pernthaler, J., and von Mering, C. (2010). A global network of coexisting microbes from environmental and whole-genome sequence data. Genome Research 20, 947-59.
7. Chen, J. and Li, H. (2013). Variable selection for sparse Dirichlet-multinomial regression with an application to microbiome data analysis. Annals of Applied Statistics, 7, 418-442.
8. Claesson, M. J., Jeffery, I. B., Conde, S., Power, S. E., O'Connor, E. M., Cusack, S., et al. (2012). Gut microbiota composition correlates with diet and health in the elderly. Nature 11319, in press.
9. Cole, J. R., Wang, Q., Cardenas, E., Fish, J., Chai, B., Farris, R. J., et al. (2009). The ribosomal database project: Improved alignments and new tools for rrna analysis. Nucleic Acids Research 37, 141-145.
10. Kuczynski, J., Lauber, C. L., Walters, W. A., Parfrey, L. W., Clemente, J. C., Gevers, et al. (2012). Experimental and analytical tools for studying the human microbiome. Nature Review Genetics 13, 47-58.
11. Meier, L., van de Geer, S., and Buhlmann, P. (2008). The group lasso for logistic regression. Journal of The Royal Statistical Society Series B 70, 53-71.
12. Peng, J., Zhu, J., Bergamaschi, A., Han, W., Noh, D. Y., Pollack, J. R., et al. (2010). Regularized multivariate regression for identifying master predictors with application to integrative genomics study of breast cancer. Annals of Applied Statistics 4, 53-77.
13. The Human Microbiome Project Consortium (2012). Structure, function and diversity of the healthy human microbiome. Nature 486, 207-214.
14. Virgin, H. W. and Todd, J. A. (2011). Metagenomics and personalized medicine. Cell 147, 44-56.
15. Wu, G. D., Chen, J., Hoffmann, C., Bittinger, K., Chen, Y. Y., Keilbaugh, S. A., et al. (2011). Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105-108.
16. Yuan, M. and Lin, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society, Series B 68, 49-67.