Discrete false-discovery rate improves identification of differentially abundant microbes

mSystems

Volumn 2, Issue 6, 2017, Pages

  Jiang, Lingjing ^a,b   Amir, Amnon ^a   Morton, James T ^a,c   Heller, Ruth ^d   Arias Castro, Ery ^e   Knight, Rob ^a,c,f  

^a UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d TEL AVIV UNIVERSITY   (Israel)

^e UNIVERSITY OF CALIFORNIA   (United States)

^f UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

Author keywords

Differential abundance; Discrete test statistics; FDR; High dimension; Microbiome; Multiple comparison; Multiple testing; Sparse; Statistics

Indexed keywords

EID: 85041536449     PISSN: None     EISSN: 23795077     Source Type: Journal    
DOI: 10.1128/mSystems.00092-17     Document Type: Article

References (21)

1. Weiss S, Xu ZZ, Peddada S, Amir A, Bittinger K, Gonzalez A, Lozupone C, Zaneveld JR, Vázquez-Baeza Y, Birmingham A, Hyde ER, Knight R. 2017. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5:27. https://doi.org/10.1186/ s40168-017-0237-y.
2. Wilkinson L. 1999. Statistical methods in psychology journals. Am Psychol 54:594-604.
3. Benjamini Y, Hochberg Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc 57: 289-300.
4. Heyse J. 2011. A false discovery rate procedure for categorical data, p 43-58. In Bhattacharjee M, Dhar SK, Subramanian S (ed), Recent advances in biostatistics, vol 4. False discovery rates, survival analysis, and related topics. World Scientific Publishing, Hackensack, NJ.
5. Li J, Tibshirani R. 2013. Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data. Stat Methods Med Res 22:519-536. https://doi.org/10.1177/0962280211428386.
6. Gilbert PB. 2005. A modified false discovery rate multiple comparisons procedure for discrete data, applied to human immunodeficiency virus genetics. Appl Stat Series C 54:143-158. https://doi.org/10.1111/j.1467-9876.2005.00475.x.
7. Vázquez-Baeza Y, Hyde ER, Suchodolski JS, Knight R. 2016. Dog and human inflammatory bowel disease rely on overlapping yet distinct dysbiosis networks. Nat Microbiol 1:16177. https://doi.org/10.1038/nmicrobiol .2016.177.
8. Charlson ES, Chen J, Custers-Allen R, Bittinger K, Li H, Sinha R, Hwang J, Bushman FD, Collman RG. 2010. Disordered microbial communities in the upper respiratory tract of cigarette smokers. PLoS One 5:e15216. https://doi.org/10.1371/journal.pone.0015216.
9. Giloteaux L, Goodrich JK, Walters WA, Levine SM, Ley RE, Hanson MR. 2016. Reduced diversity and altered composition of the gut microbiome in individuals with myalgic encephalomyelitis/chronic fatigue syndrome. Microbiome 4:30. https://doi.org/10.1186/s40168-016-0171-4.
10. Vijay-Kumar M, Aitken JD, Carvalho FA, Cullender TC, Mwangi S, Srinivasan S, Sitaraman SV, Knight R, Ley RE, Gewirtz AT. 2010. Metabolic syndrome and altered gut microbiota in mice lacking Toll-like receptor 5. Science 328:228-231. https://doi.org/10.1126/science.1179721.
11. Dominguez-Bello MG, Costello EK, Contreras M, Magris M, Hidalgo G, Fierer N, Knight R. 2010. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc Natl Acad Sci U S A 107:11971-11975. https://doi.org/10.1073/pnas.1002601107.
12. Gevers D, Kugathasan S, Denson LA, Vázquez-Baeza Y, Van Treuren W, Ren B, Schwager E, Knights D, Song SJ, Yassour M, Morgan XC, Kostic AD, Luo C, González A, McDonald D, Haberman Y, Walters T, Baker S, Rosh J, Stephens M, Heyman M, Markowitz J, Baldassano R, Griffiths A, Sylvester F, Mack D, Kim S, Crandall W, Hyams J, Huttenhower C, Knight R, Xavier RJ. 2014. The treatment-naive microbiome in new-onset Crohn's disease. Cell Host Microbe 15:382-392. https://doi.org/10.1016/j.chom.2014.02 .005.
13. Beaumont M, Goodrich JK, Jackson MA, Yet I, Davenport ER, Vieira-Silva S, Debelius J, Pallister T, Mangino M, Raes J, Knight R, Clark AG, Ley RE, Spector TD, Bell JT. 2016. Heritable components of the human fecal microbiome are associated with visceral fat. Genome Biol 17:189. https:// doi.org/10.1186/s13059-016-1052-7.
14. Sankaran K, Holmes S. 2014. structSSI: simultaneous and selective inference for grouped or hierarchically structured data. J Stat Softw 59:1-21. https://doi.org/10.18637/jss.v059.i13.
15. Xiao J, Cao H, Chen J. 2017. False discovery rate control incorporating phylogenetic tree increases detection power in microbiome-wide multiple testing. Bioinformatics 33:2873-2881. https://doi.org/10.1093/ bioinformatics/btx311.
16. Morton JT, Sanders J, Quinn RA, McDonald D, Gonzalez A, Vázquez-Baeza Y, Navas-Molina JA, Song SJ, Metcalf JL, Hyde ER, Lladser M, Dorrestein PC, Knight R. 2017. Balance trees reveal microbial niche differentiation. mSystems 2:e00162-16. https://doi.org/10.1128/mSystems .00162-16.
17. Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. 2015. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis 26:27663. https://doi.org/10 .3402/mehd.v26.27663.
18. Friedman J, Alm EJ. 2012. Inferring correlation networks from genomic survey data. PLoS Comput Biol 8:e1002687. https://doi.org/10.1371/ journal.pcbi.1002687.
19. Benjamini Y, Krieger AM, Yekutieli D. 2006. Adaptive linear step-up procedures that control the false discovery rate. Biometrika 93:491-507. https://doi.org/10.1093/biomet/93.3.491.
20. Kulinskaya E, Lewin A. 2009. On fuzzy familywise error rate and false discovery rate procedures for discrete distributions. Biometrika 96: 201-211. https://doi.org/10.1093/biomet/asn061.
21. Tarone RE. 1990. A modified Bonferroni method for discrete data. Biometrics 46:515-522. https://doi.org/10.2307/2531456.