A fast small-sample kernel independence test for microbiome community-level association analysis

Xiang Zhan, Anna Plantinga, Ni Zhao, Michael C. Wu

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

To fully understand the role of microbiome in human health and diseases, researchers are increasingly interested in assessing the relationship between microbiome composition and host genomic data. The dimensionality of the data as well as complex relationships between microbiota and host genomics pose considerable challenges for analysis. In this article, we apply a kernel RV coefficient (KRV) test to evaluate the overall association between host gene expression and microbiome composition. The KRV statistic can capture nonlinear correlations and complex relationships among the individual data types and between gene expression and microbiome composition through measuring general dependency. Testing proceeds via a similar route as existing tests of the generalized RV coefficients and allows for rapid p-value calculation. Strategies to allow adjustment for confounding effects, which is crucial for avoiding misleading results, and to alleviate the problem of selecting the most favorable kernel are considered. Simulation studies show that KRV is useful in testing statistical independence with finite samples given the kernels are appropriately chosen, and can powerfully identify existing associations between microbiome composition and host genomic data while protecting type I error. We apply the KRV to a microbiome study examining the relationship between host transcriptome and microbiome composition within the context of inflammatory bowel disease and are able to derive new biological insights and provide formal inference on prior qualitative observations.

Original languageEnglish (US)
Pages (from-to)1453-1463
Number of pages11
JournalBiometrics
Volume73
Issue number4
DOIs
StatePublished - Jan 1 2017

Fingerprint

Independence Test
Microbiota
Small Sample
Association reactions
kernel
seeds
Chemical analysis
Genomics
Coefficient
Gene expression
testing
sampling
Gene Expression
genomics
Statistical Independence
Testing
Confounding
Type I error
p-Value
gene expression

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Cite this

Zhan, Xiang ; Plantinga, Anna ; Zhao, Ni ; Wu, Michael C. / A fast small-sample kernel independence test for microbiome community-level association analysis. In: Biometrics. 2017 ; Vol. 73, No. 4. pp. 1453-1463.
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A fast small-sample kernel independence test for microbiome community-level association analysis. / Zhan, Xiang; Plantinga, Anna; Zhao, Ni; Wu, Michael C.

In: Biometrics, Vol. 73, No. 4, 01.01.2017, p. 1453-1463.

Research output: Contribution to journalArticle

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