dICC: distance-based intraclass correlation coefficient for metagenomic reproducibility studies
dICC: distance-based intraclass correlation coefficient for metagenomic reproducibility studies
复制标题
dICC：用于宏基因组重现性研究的基于距离的组内相关系数
DOI：
10.1093/bioinformatics/btac618
复制
发表时间：
2022
期刊：
影响因子：
5.8
通讯作者：
Schwartz, ed., Russell
中科院分区：
文献类型：
--
作者：
Chen, Jun;Zhang, Xianyang;Schwartz, ed., Russell
摘要由于稀疏性和高维性，微生物组数据通常被总结为成对距离，以捕获组成差异。通过分析与某些协变量相关的距离矩阵，可以获得许多生物学见解。更可重复地表征样本间关系的微生物组采样方法预计将产生更高的统计功效。传统上，组内相关系数（ICC）被用来量化使用技术重复的单变量测量的再现性程度。在这项工作中，我们将传统的 ICC 扩展到距离测量，并提出了基于距离的 ICC (dICC)。我们推导了基于样本的 dICC 的渐近分布，以方便统计推断。我们使用来自宏基因组再现性研究的真实数据集来说明 dICC。可用性和实现dICC 在 R CRAN 包 GUniFrac 中实现。补充信息补充数据可在生物信息学在线获取。
SummaryDue 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 implementationdICC is implemented in the R CRAN packageGUniFrac.Supplementary informationSupplementary data are available atBioinformaticsonline.