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

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The kernel RV (KRV) test for gene expression and microbiome association shows similar performance to the generalized RV (GRV) coefficient. However, KRV may inflate type I errors at small significance levels, necessitating alternative p-value calculations.

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Area of Science:

  • Microbiome Research
  • Genomics
  • Statistical Bioinformatics

Background:

  • The kernel RV (KRV) coefficient test assesses associations between host gene expression and microbiome composition.
  • Existing methods for evaluating these complex biological interactions are continually being refined.

Purpose of the Study:

  • To clarify the relationship between the kernel RV (KRV) coefficient and the generalized RV (GRV) coefficient.
  • To evaluate the performance and statistical properties of the KRV test, particularly its type I error control.
  • To propose improvements for accurate p-value calculation in KRV testing, especially at stringent significance levels.

Main Methods:

  • Comparative analysis of KRV and generalized RV (GRV) coefficients.
  • Statistical evaluation of KRV test's type I error rate at various significance levels (1% and 5%).
  • Development and validation of an alternative p-value calculation method for KRV.
  • Analytical derivation of KRV as a correlation coefficient for computational efficiency.

Main Results:

  • KRV demonstrates performance highly similar to GRV.
  • The KRV test controls type I errors well at 1% and 5% significance levels but can underestimate p-values at smaller levels, leading to inflated type I errors.
  • An alternative p-value calculation method provides more accurate results at small significance levels.
  • KRV can be computationally expedited by expressing it as a correlation coefficient.

Conclusions:

  • While KRV is a viable method for analyzing host gene expression and microbiome associations, its p-values require careful interpretation, especially at stringent thresholds.
  • The proposed alternative p-value calculation enhances accuracy and reliability for small p-values.
  • Verification using permutation p-values is recommended for small KRV test p-values in practical applications.