An Adaptive Multivariate Two-Sample Test With Application to Microbiome Differential Abundance Analysis
Tóm tắt
Từ khóa
Tài liệu tham khảo
Ainsworth, 2017, k-slam: accurate and ultra-fast taxonomic classification and gene identification for large metagenomic data sets, Nucleic Acids Res., 45, 1649, 10.1093/nar/gkw1248
Aitchison, 1982, The statistical analysis of compositional data, J. R. Stat. Soc. Ser. B, 44, 139, 10.1111/j.2517-6161.1982.tb01195.x
Anders, 2010, Differential expression analysis for sequence count data, Genome Biol., 11, R106, 10.1186/gb-2010-11-10-r106
Atchison, 1980, Logistic-normal distributions: Some properties and uses, Biometrika, 67, 261, 10.1093/biomet/67.2.261
Bai, 1996, Effect of high dimension: by an example of a two sample problem, Stat. Sin., 6, 311
Barber, 2015, Controlling the false discovery rate via knockoffs, Ann. Stat., 43, 2055, 10.1214/15-AOS1337
Benjamini, 1995, Controlling the false discovery rate: a practical and powerful approach to multiple testing, J. R. Stat. Soc. Ser., 57, 289, 10.1111/j.2517-6161.1995.tb02031.x
Benjamini, 2001, The control of the false discovery rate in multiple testing under dependency, Ann. Stat., 29, 1165, 10.1214/aos/1013699998
Cai, 2012, Identifying genetic marker sets associated with phenotypes via an efficient adaptive score test, Biostatistics, 13, 776, 10.1093/biostatistics/kxs015
Cai, 2014, Two-sample test of high dimensional means under dependence, J. R. Stat. Soc., 76, 349, 10.1111/rssb.12034
Candes, 2018, Panning for gold: model–X knockoffs for high dimensional controlled variable selection, J. R. Stat. Soc., 80, 551, 10.1111/rssb.12265
Cao, 2017, Two-sample tests of high-dimensional means for compositional data, Biometrika, 105, 115, 10.1093/biomet/asx060
Chen, 2016, Small sample kernel association tests for human genetic and microbiome association studies, Genet. Epidemiol., 40, 5, 10.1002/gepi.21934
Chen, 2017, An omnibus test for differential distribution analysis of microbiome sequencing data, Bioinformatics, 34, 643, 10.1093/bioinformatics/btx650
Chen, 2010, A two-sample test for high-dimensional data with applications to gene-set testing, Ann. Stat., 38, 808, 10.1214/09-AOS716
Gretton, 2007, A kernel method for the two-sample problem, NIPS, 520
Gretton, 2012, A kernel two-sample test, J. Mach. Learn. Res., 13, 723
Hawinkel, 2017, A broken promise: microbiome differential abundance methods do not control the false discovery rate, Brief. Bioinform., 20, 210, 10.1093/bib/bbx104
Hicks, 2018, Oral microbiome activity in children with autism spectrum disorder, Aut. Res., 11, 1286, 10.1002/aur.1972
Koh, 2017, A powerful microbiome-based association test and a microbial taxa discovery framework for comprehensive association mapping, Microbiome, 5, 45, 10.1186/s40168-017-0262-x
Li, 2015, Microbiome, metagenomics and high-dimensional compositional data analysis, Ann. Rev. Stat. Appl., 2, 73, 10.1146/annurev-statistics-010814-020351
Louis, 2014, The gut microbiota, bacterial metabolites and colorectal cancer, Nat. Rev. Microbiol., 12, 661, 10.1038/nrmicro3344
McArdle, 2001, Fitting multivariate models to community data: a comment on distance-based redundancy analysis, Ecology, 82, 290, 10.1890/0012-9658(2001)082<0290:FMMTCD>2.0.CO;2
McMurdie, 2014, Waste not, want not: why rarefying microbiome data is inadmissible, PLoS Comp. Biol., 10, e1003531, 10.1371/journal.pcbi.1003531
Mitchell, 2017, Vaginal microbiota and genitourinary menopausal symptoms: a cross-sectional analysis, Menopause, 24, 1160, 10.1097/GME.0000000000000904
Morgan, 2015, Associations between host gene expression, the mucosal microbiome, and clinical outcome in the pelvic pouch of patients with inflammatory bowel disease, Gen. Biol., 16, 67, 10.1186/s13059-015-0637-x
Pan, 2014, A powerful and adaptive association test for rare variants, Genetics, 197, 1081, 10.1534/genetics.114.165035
Pan, 2015, A powerful pathway-based adaptive test for genetic association with common or rare variants, Am. J. Hum. Genet., 97, 86, 10.1016/j.ajhg.2015.05.018
Plantinga, 2017, Mirkat-s: a community-level test of association between the microbiota and survival times, Microbiome, 5, 17, 10.1186/s40168-017-0239-9
Qin, 2012, A metagenome-wide association study of gut microbiota in type 2 diabetes, Nature, 490, 55, 10.1038/nature11450
Robinson, 2010, edger: a bioconductor package for differential expression analysis of digital gene expression data, Bioinformatics, 26, 139, 10.1093/bioinformatics/btp616
Sohn, 2015, A robust approach for identifying differentially abundant features in metagenomic samples, Bioinformatics, 31, 2269, 10.1093/bioinformatics/btv165
Tang, 2016, Permanova-s: association test for microbial community composition that accommodates confounders and multiple distances, Bioinformatics, 32, 2618, 10.1093/bioinformatics/btw311
Tang, 2017, A general framework for association analysis of microbial communities on a taxonomic tree, Bioinformatics, 33, 1278, 10.1093/bioinformatics/btw804
Tibshirani, 1996, Regression shrinkage and selection via the lasso, J. R. Stat. Soc. Ser., 58, 267, 10.1111/j.2517-6161.1996.tb02080.x
Turnbaugh, 2009, A core gut microbiome in obese and lean twins, Nature, 457, 480, 10.1038/nature07540
Wang, 2016, Metagenome-wide association studies: fine-mining the microbiome, Nat. Rev. Microbiol., 14, 508, 10.1038/nrmicro.2016.83
Weiss, 2017, Normalization and microbial differential abundance strategies depend upon data characteristics, Microbiome, 5, 27, 10.1186/s40168-017-0237-y
Wu, 2016, An adaptive association test for microbiome data, Gen. Med., 8, 56, 10.1186/s13073-016-0302-3
Zhan, 2015, An adaptive genetic association test using double kernel machines, Stat. Biosci., 7, 262, 10.1007/s12561-014-9116-2
Zhan, , A fast small-sample kernel independence test for microbiome community-level association analysis, Biometrics, 73, 1453, 10.1111/biom.12684
Zhan, , A small-sample multivariate kernel machine test for microbiome association studies, Gen. Epidemiol., 41, 210, 10.1002/gepi.22030
Zhan, 2018, A small-sample kernel association test for correlated data with application to microbiome association studies, Gen. Epidemiol., 42, 772, 10.1002/gepi.22160
Zhang, 2016, Zero-inflated negative binomial regression for differential abundance testing in microbiome studies, J. Bioinform. Genom., 2, 1, 10.18454/jbg.2016.2.2.1
Zhang, 2015, The oral and gut microbiomes are perturbed in rheumatoid arthritis and partly normalized after treatment, Nat. Med., 21, 895, 10.1038/nm.3914
Zhao, 2015, Testing in microbiome-profiling studies with mirkat, the microbiome regression-based kernel association test, Am. J. Hum. Gen., 96, 797, 10.1016/j.ajhg.2015.04.003