Measuring associations between the microbiota and repeated measures of continuous clinical variables using a lasso-penalized generalized linear mixed model
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Sze MA, Dimitriu PA, Suzuki M, McDonough JE, Campbell JD, Brothers JF, et al. The Host Response to the Lung Microbiome in Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med. 2015; Available from: https://doi.org/10.1164/rccm.201502-0223OC
Pérez-Losada M, Castro-Nallar E, Bendall ML, Freishtat RJ, Crandall KA. Dual transcriptomic profiling of host and microbiota during health and disease in pediatric asthma. PLoS One. 2015;10:e0131819. Available from: http://dx.doi.org/10.1371%2Fjournal.pone.0131819
Garcia-Nunez M, Millares L, Pomares X, Ferrari R, Perez-Brocal V, Gallego M, et al. Severity-related changes of bronchial microbiome in chronic obstructive pulmonary disease. J Clin Microbiol. 2014;52:4217–23.
Bittinger K, Charlson ES, Loy E, Shirley DJ, Haas AR, Laughlin A, et al. Improved characterization of medically relevant fungi in the human respiratory tract using next-generation sequencing. Genome Biol. 2014;15:487. Available from: http://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0487-y
McCafferty J, Muhlbauer M, Gharaibeh RZ, Arthur JC, Perez-Chanona E, Sha W, et al. Stochastic changes over time and not founder effects drive cage effects in microbial community assembly in a mouse model. ISME J. 2013;7:2116–25. Available from: https://doi.org/10.1038/ismej.2013.106
Romero R, Hassan SS, Gajer P, Tarca AL, Fadrosh DW, Nikita L, et al. The composition and stability of the vaginal microbiota of normal pregnant women is different from that of non-pregnant women. Microbiome. 2014;2:4. Available from: http://www.microbiomejournal.com/content/2/1/4
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol Evol. 2009;24:127–35.
Waldron L, Pintilie M, Tsao M-S, Shepherd FA, Huttenhower C, Jurisica I. Optimized application of penalized regression methods to diverse genomic data. Bioinformatics. 2011;27:3399–406. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22156367
Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B Stat Methodol. 2005;67:301–20. Available from: http://doi.wiley.com/10.1111/j.1467-9868.2005.00503.x . [cited 4 Jul 2016]
Groll A, Tutz G. Variable selection for generalized linear mixed models by L 1-penalized estimation. Stat Comput. 2014;24:137–54.
Schelldorfer J, Meier L, GLMMLasso BP. An algorithm for high-dimensional generalized linear mixed models using ℓ 1 -penalization. J Comput Graph Stat. 2014;23:460–77. Available from: http://www.tandfonline.com/doi/abs/10.1080/10618600.2013.773239
Morris A, Beck JM, Schloss PD, Campbell TB, Crothers K, Curtis JL, et al. Comparison of the respiratory microbiome in healthy nonsmokers and smokers. Am J Respir Crit Care Med. 2013;187:1067–75. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3734620/
Cui L, Lucht L, Tipton L, Rogers MB, Fitch A, Kessinger C, et al. Topographic diversity of the respiratory tract Mycobiome and alteration in HIV and lung disease. Am J Respir Crit Care Med. 2015;191:932–42. Available from: https://doi.org/10.1164/rccm.201409-1583OC
Dollive S, Peterfreund GL, Sherrill-Mix S, Bittinger K, Sinha R, Hoffmann C, et al. A tool kit for quantifying eukaryotic rRNA gene sequences from human microbiome samples. Genome Biol. 2012;13:R60. Available from: http://genomebiology.biomedcentral.com/articles/10.1186/gb-2012-13-7-r60
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6. Available from: https://doi.org/10.1038/nmeth.f.303
Dannemiller KC, Reeves D, Bibby K, Yamamoto N, Peccia J. Fungal high-throughput taxonomic identification tool for use with next-generation sequencing (FHiTINGS). J Basic Microbiol. 2014;54:315–21. Available from: https://doi.org/10.1002/jobm.201200507
Bohnen N, Degenaar CP, Jolles J. Influence of age and sex on 19 blood variables in healthy subjects. Z Gerontol. 1992;25:339–45.
Appay V, Sauce D. Immune activation and inflammation in HIV-1 infection: causes and consequences. J Pathol. 2008;214:231–41.
Yuan M, Lin Y. Model selection and estimation in regression with grouped variables. J R Stat Soc Ser B (Stat Methodol). 2006;68:49–67.
Groll A. glmmLasso: Variable selection for generalized linear mixed models by L1-penalized estimation. 2014.
Bates D, Mächler M, Bolker BM, Walker SC. Fitting linear mixed-effects models using lme4. ArXIV e-print; Press. J Stat Softw. 2015; Available from: http://arxiv.org/abs/1406.5823
Nakagawa S, Schielzeth H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol Evol. 2013;4:133–42.
Wilcox HE, Farrar MD, Cunliffe WJ, Holland KT, Ingham E. Resolution of inflammatory acne vulgaris may involve regulation of CD4+ T-cell responses to Propionibacterium acnes. Br J Dermatol. 2007;156:460–5.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B. 1995:289–300. Available from: http://www.jstor.org/stable/2346101
Shmueli G. To explain or to predict? Stat Sci. 2010;25:289–310. Available from: https://projecteuclid.org/download/pdfview_1/euclid.ss/1294167961
Tickle T, L W, Lu Y, Huttenhower C. Multivariate association of microbial communities with rich metadata in high-dimensional studies. Prog. 2016;
Chen EZ, Li H. A two-part mixed-effects model for analyzing longitudinal microbiome compositional data. Bioinformatics. 2016;32:2611–7.