Dataset Information

DICC: distance-based intraclass correlation coefficient for metagenomic reproducibility studies.

ABSTRACT:

Summary

Due to the sparsity and high dimensionality, microbiome data are routinely summarized into pairwise distances capturing the compositional differences. Many biological insights can be gained by analyzing the distance matrix in relation to some covariates. A microbiome sampling method that characterizes the inter-sample relationship more reproducibly is expected to yield higher statistical power. Traditionally, the intraclass correlation coefficient (ICC) has been used to quantify the degree of reproducibility for a univariate measurement using technical replicates. In this work, we extend the traditional ICC to distance measures and propose a distance-based ICC (dICC). We derive the asymptotic distribution of the sample-based dICC to facilitate statistical inference. We illustrate dICC using a real dataset from a metagenomic reproducibility study.

Availability and implementation

dICC is implemented in the R CRAN package GUniFrac.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Chen J

PROVIDER: S-EPMC9801959 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Publications

dICC: distance-based intraclass correlation coefficient for metagenomic reproducibility studies.

Chen Jun J Zhang Xianyang X

Bioinformatics (Oxford, England) 20221001 21

<h4>Summary</h4>Due to the sparsity and high dimensionality, microbiome data are routinely summarized into pairwise distances capturing the compositional differences. Many biological insights can be gained by analyzing the distance matrix in relation to some covariates. A microbiome sampling method that characterizes the inter-sample relationship more reproducibly is expected to yield higher statistical power. Traditionally, the intraclass correlation coefficient (ICC) has been used to quantify ...[more]

PMID: 36083005

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