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Reader reaction on the fast small‐sample kernel independence test for microbiome community‐level association analysis

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  • Bin Guo
  • Baolin Wu

Abstract

Zhan et al. () presented a kernel RV coefficient (KRV) test to evaluate the overall association between host gene expression and microbiome composition, and showed its competitive performance compared to existing methods. In this article, we clarify the close relation of KRV to the existing generalized RV (GRV) coefficient, and show that KRV and GRV have very similar performance. Although the KRV test could control the type I error rate well at 1% and 5% levels, we show that it could largely underestimate p‐values at small significance levels leading to significantly inflated type I errors. As a partial remedy, we propose an alternative p‐value calculation, which is efficient and more accurate than KRV p‐value at small significance levels. We recommend that small KRV test p‐values should always be accompanied and verified by the permutation p‐value in practice. In addition, we analytically show that KRV can be written as a form of correlation coefficient, which can dramatically expedite its computation and make permutation p‐value calculation more efficient.

Suggested Citation

  • Bin Guo & Baolin Wu, 2018. "Reader reaction on the fast small‐sample kernel independence test for microbiome community‐level association analysis," Biometrics, The International Biometric Society, vol. 74(3), pages 1120-1124, September.
    • Handle: RePEc:bla:biomet:v:74:y:2018:i:3:p:1120-1124
    DOI: 10.1111/biom.12823
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    References listed on IDEAS

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    1. Xiang Zhan & Anna Plantinga & Ni Zhao & Michael C. Wu, 2017. "A fast small‐sample kernel independence test for microbiome community‐level association analysis," Biometrics, The International Biometric Society, vol. 73(4), pages 1453-1463, December.
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