Powerful and robust non-parametric association testing for microbiome data via a zero-inflated quantile approach (ZINQ).

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Bibliographic Details
Title:	Powerful and robust non-parametric association testing for microbiome data via a zero-inflated quantile approach (ZINQ).
Authors:	Ling W; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, 98109, USA., Zhao N; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St, Baltimore, 21205, USA., Plantinga AM; Department of Mathematics and Statistics, Williams College, 18 Hoxsey St., Williamstown, 01267, USA., Launer LJ; Laboratory of Epidemiology and Population Science, NIA, NIH, 7201 Wisconsin Ave, Bethesda, 20814, USA., Fodor AA; Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, 28223, USA., Meyer KA; Nutrition Research Institute and Department of Nutrition, University of North Carolina, 500 Laureate Way, Kannapolis, 28081, USA., Wu MC; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, 98109, USA. mcwu@fredhutch.org.
Source:	Microbiome [Microbiome] 2021 Sep 02; Vol. 9 (1), pp. 181. Date of Electronic Publication: 2021 Sep 02.
Publication Type:	Journal Article; Research Support, N.I.H., Extramural; Research Support, N.I.H., Intramural; Research Support, Non-U.S. Gov't; Video-Audio Media
Journal Info:	Publisher: BioMed Central Country of Publication: England NLM ID: 101615147 Publication Model: Electronic Cited Medium: Internet ISSN: 2049-2618 (Electronic) Linking ISSN: 20492618 NLM ISO Abbreviation: Microbiome Subsets: MEDLINE
Database:	MEDLINE Ultimate

Description
ISSN:	2049-2618
DOI:	10.1186/s40168-021-01129-3