A broken promise: microbiome differential abundance methods do not control the false discovery rate

The Human Microbiome Project Consortium, 2012, Structure, function and diversity of the healthy human microbiome, Nature, 486, 207, 10.1038/nature11234

Sekirov, 2009, The role of the intestinal microbiota in enteric infection, J Physiol, 587, 4159, 10.1113/jphysiol.2009.172742

Ivanov, 2009, Induction of intestinal Th17 cells by segmented filamentous bacteria, Cell, 139, 485, 10.1016/j.cell.2009.09.033

Ivanov, 2010, Segmented filamentous bacteria take the stage, Mucosal Immunol, 3, 209, 10.1038/mi.2010.3

Ravel, 2011, Vaginal microbiome of reproductive-age women, Proc Natl Acad Sci USA, 108, 4680, 10.1073/pnas.1002611107

Kahrstrom, 2012, Microbiome: Gut microbiome as a marker for diabetes, Nat Rev Micro, 10, 733, 10.1038/nrmicro2903

Kostic, 2015, The dynamics of the human infant gut microbiome in development and in progression towards type 1 diabetes, Cell Host Microbe, 17, 260, 10.1016/j.chom.2015.01.001

Scher, 2012, Periodontal disease and the oral microbiota in new-onset rheumatoid arthritis, Arthritis Rheum, 64, 3083, 10.1002/art.34539

Janda, 2007, 16S rRNA gene sequencing for bacterial identification in the diagnostic laboratory: pluses, perils, and pitfalls, J Clin Microbiol, 45, 2761, 10.1128/JCM.01228-07

Morgan, 2012, Chapter 12: human microbiome analysis, PLoS Comput Biol, 8, e1002808., 10.1371/journal.pcbi.1002808

Paulson, 2013, Robust methods for differential abundance analysis in marker gene surveys, Nat Methods, 10, 1200, 10.1038/nmeth.2658

McMurdie, 2014, Waste not, want not: why rarefying microbiome data is inadmissible, PLoS Comput Biol, 10, e1003531, 10.1371/journal.pcbi.1003531

Anders, 2010, Differential expression analysis for sequence count data, Genome Biol, 11, R106, 10.1186/gb-2010-11-10-r106

Robinson, 2010, A scaling normalization method for differential expression analysis of RNA-seq data, Genome Biol, 11, R25, 10.1186/gb-2010-11-3-r25

Bullard, 2010, Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments, BMC Bioinformatics, 11, 94, 10.1186/1471-2105-11-94

Li, 2012, Normalization, testing, and false discovery rate estimation for RNA-sequencing data, Biostatistics, 13, 523, 10.1093/biostatistics/kxr031

Mandal, 2015, Analysis of composition of microbiomes: a novel method for studying microbial composition, Microb Ecol Health Dis, 26, 27663

Fernandes, 2014, Unifying the analysis of highthroughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis, Microbiome, 2, 15, 10.1186/2049-2618-2-15

Paulson, 2011, Metastats: an improved statistical method for analysis of metagenomic data, Genome Biol, 12, P17, 10.1186/1465-6906-12-S1-P17

Zeller, 2014, Potential of fecal microbiota for early-stage detection of colorectal cancer, Mol Syst Biol, 10, 766, 10.15252/msb.20145645

Segata, 2011, Metagenomic biomarker discovery and explanation, Genome Biol, 12, R60, 10.1186/gb-2011-12-6-r60

Benjamini, 1995, Controlling the false discovery rate: a practical and powerful approach to multiple testing, J R Stat Soc Series B Methodol, 57, 289, 10.1111/j.2517-6161.1995.tb02031.x

Nookaew, 2012, A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae, Nucleic Acids Res, 40, 10084, 10.1093/nar/gks804

Rigaill, 2016, Synthetic data sets for the identification of key ingredients for RNA-seq differential analysis, Brief Bioinform, 17, 1

Robinson, 2007, Moderated statistical tests for assessing differences in tag abundance, Bioinformatics, 23, 2881, 10.1093/bioinformatics/btm453

Law, 2014, voom: precision weights unlock linear model analysis tools for RNA-seq read counts, Genome Biol, 15, R29, 10.1186/gb-2014-15-2-r29

Benjamini, 2001, The control of the false discovery rate in multiple testing under dependency, Ann Statist, 29, 1165, 10.1214/aos/1013699998

Efron, 2008, Microarrays, empirical bayes and the two-groups model, Statist Sci, 23, 1, 10.1214/07-STS236

Benidt, 2015, SimSeq: a nonparametric approach to simulation of RNA-sequence datasets, Bioinformatics, 31, 2131, 10.1093/bioinformatics/btv124

Reeb, 2013, Evaluating statistical analysis models for RNA sequencing experiments, Front Genet, 4, 178, 10.3389/fgene.2013.00178

Kvam, 2012, A comparison of statistical methods for detecting differentially expressed genes from RNAseq data, Am J Bot, 99, 248, 10.3732/ajb.1100340

Love, 2014, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2, Genome Biol, 15, 550, 10.1186/s13059-014-0550-8

The NIH HMP Working Group, 2009, The NIH human microbiome project, Genome Res, 19, 2317, 10.1101/gr.096651.109

2015

Soneson, 2013, A comparison of methods for differential expression analysis of RNA-seq data, BMC Bioinformatics, 14, 91–91, 10.1186/1471-2105-14-91

Jonsson, 2016, Statistical evaluation of methods for identification of differentially abundant genes in comparative metagenomics, BMC Genomics, 17, 78, 10.1186/s12864-016-2386-y

Seyednasrollah, 2013, Comparison of software packages for detecting differential expression in RNA-seq studies, Brief Bioinform, 16, 59, 10.1093/bib/bbt086

Burden, 2014, Error estimates for the analysis of differential expression from RNA-seq count data, PeerJ, 2, e576, 10.7717/peerj.576

Kurtz, 2015, Sparse and compositionally robust inference of microbial ecological networks, PLoS Comput Biol, 11, e1004226, 10.1371/journal.pcbi.1004226

Danaher, 1988, Parameter estimation for the Dirichlet-multinomial distribution using supplementary beta-binomial data, Commun Stat Theory Methods, 17, 1777, 10.1080/03610928808829713

Kostic, 2014, The microbiome in inammatory bowel diseases: current status and the future ahead, Gastroenterology, 146, 1489, 10.1053/j.gastro.2014.02.009

Looft, 2012, Infeed antibiotic effects on the swine intestinal microbiome, Proc Natl Acad Sci USA, 109, 1691, 10.1073/pnas.1120238109

Markle, 2013, Sex differences in the gut microbiome drive hormone-dependent regulation of autoimmunity, Science, 339, 1084, 10.1126/science.1233521

White, 2009, Statistical methods for detecting differentially abundant features in clinical metagenomic samples, PLoS Comput Biol, 5, e1000352, 10.1371/journal.pcbi.1000352

Rapaport, 2013, Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data, Genome Biol, 14, R95, 10.1186/gb-2013-14-9-r95

Marietta, 2013, Low incidence of spontaneous type 1 diabetes in non-obese diabetic mice raised on gluten-free diets is associated with changes in the intestinal microbiome, PLoS One, 8, e78687, 10.1371/journal.pone.0078687

Singh, 2016, Impact of age and sex on the composition and abundance of the intestinal microbiota in individuals with and without enteric infections, Ann Epidemiol, 26, 380, 10.1016/j.annepidem.2016.03.007

Koren, 2012, Host remodeling of the gut microbiome and metabolic changes during pregnancy, Cell, 150, 470, 10.1016/j.cell.2012.07.008

Fortenberry, 2013, The uses of race and ethnicity in human microbiome research, Trends Microbiol, 21, 165, 10.1016/j.tim.2013.01.001

Larsen, 2010, Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults, PLoS One, 5, e9085, 10.1371/journal.pone.0009085

McMurdie, 2013, phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data, PLoS One, 8, e61217, 10.1371/journal.pone.0061217

Strimmer, 2008, A unified approach to false discovery rate estimation, BMC Bioinformatics, 9, 303, 10.1186/1471-2105-9-303

Li, 2013, Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data, Stat Methods Med Res, 22, 519, 10.1177/0962280211428386

La Rosa, 2012, Hypothesis testing and power calculations for taxonomic-based human microbiome data, PLoS One, 7, e52078, 10.1371/journal.pone.0052078

Zhou, 2014, Robustly detecting differential expression in RNA sequencing data using observation weights, Nucleic Acids Res, 42, e91, 10.1093/nar/gku310

Dillies, 2013, A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis, Brief Bioinform, 14, 671, 10.1093/bib/bbs046

Schurch, 2015, How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?, arXiv, 43, 10

Ching, 2014, Power analysis and sample size estimation for RNA-Seq differential expression, RNA, 20, 1684, 10.1261/rna.046011.114