Phylogenetic Association Analysis with Conditional Rank Correlation

Shulei Wang, Bo Yuan, T Tony Cai, Hongzhe Li

Research output: Contribution to journalArticlepeer-review

Abstract

Phylogenetic association analysis plays a crucial role in investigating the correlation between microbial compositions and specific outcomes of interest in microbiome studies. However, existing methods for testing such associations have limitations related to the assumption of a linear association in high-dimensional settings and the handling of confounding effects. Hence, there is a need for methods capable of characterizing complex associations, including nonmonotonic relationships. This article introduces a novel phylogenetic association analysis framework and associated tests to address these challenges by employing conditional rank correlation as a measure of association. The proposed tests account for confounders in a fully nonparametric manner, ensuring robustness against outliers and the ability to detect diverse dependencies. The proposed framework aggregates conditional rank correlations for subtrees using weighted sum and maximum approaches to capture both dense and sparse signals. The significance level of the test statistics is determined by calibration through a nearest-neighbour bootstrapping method, which is straightforward to implement and can accommodate additional datasets when these are available. The practical advantages of the proposed framework are demonstrated through numerical experiments using both simulated and real microbiome datasets.

Original languageEnglish (US)
Article numberasad075
Pages (from-to)881-902
Number of pages22
JournalBiometrika
Volume111
Issue number3
Early online dateDec 1 2023
DOIs
StatePublished - Sep 2024

Keywords

  • Association analysis
  • Compositional data
  • Conditional independence test
  • Covariate adjustment
  • Phylogenetic tree
  • Rank correlation

ASJC Scopus subject areas

  • Statistics and Probability
  • General Mathematics
  • Agricultural and Biological Sciences (miscellaneous)
  • General Agricultural and Biological Sciences
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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