EconPapers    
Economics at your fingertips  
 

Assessing the reproducibility of microbiome measurements based on concordance correlation coefficients

Ying Cui, Limin Peng, Yijuan Hu and HuiChuan J. Lai

Journal of the Royal Statistical Society Series C, 2021, vol. 70, issue 4, 1027-1048

Abstract: Evaluating the reproducibility or agreement of microbiome measurements is often a crucial step to ensure rigorous downstream analyses in microbiome studies. In this paper, we address this need by developing adaptations of Lin’s concordance correlation coefficient (CCC) tailored to microbiome studies. We introduce a general formulation of the new CCC measures upon the use of a distance function appropriately characterizing the discrepancy between microbiome compositional measurements. We thoroughly study the special cases that adopt the Euclidean distance and Aitchison distance. Our proposals appropriately account for the unique features of microbiome compositional data, including high‐dimensionality, dependency among individual relative abundances and the presence of many zeros. We further investigate a practical compound approach to help better understand the sources of data inconsistency. Extensive simulation studies are conducted to evaluate the utility of the proposed methods in realistic scenarios. We also apply the proposed methods to a microbiome validation data set from the Feeding Infants Right.. from the STart (FIRST) study. Our analyses offer useful insight about the extent of data variations resulted from two different experiment procedures as well as their heterogeneous patterns across genera.

Date: 2021
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://doi.org/10.1111/rssc.12497

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:70:y:2021:i:4:p:1027-1048

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().

 
Page updated 2021-08-08
Handle: RePEc:bla:jorssc:v:70:y:2021:i:4:p:1027-1048