Reader reaction on the fast small‐sample kernel independence test for microbiome community‐level association analysis
Bin Guo and
Biometrics, 2018, vol. 74, issue 3, 1120-1124
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.
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bla:biomet:v:74:y:2018:i:3:p:1120-1124
Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0006-341X
Access Statistics for this article
More articles in Biometrics from The International Biometric Society
Bibliographic data for series maintained by Wiley Content Delivery ().