RT Journal Article
SR Electronic
T1 coda4microbiome: compositional data analysis for microbiome studies
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.06.09.495511
DO 10.1101/2022.06.09.495511
A1 Calle, M.Luz
A1 Susin, Antoni
YR 2022
UL http://biorxiv.org/content/early/2022/06/11/2022.06.09.495511.abstract
AB Motivation One of the main challenges of microbiome analysis is its compositional nature that if ig-nored can lead to spurious results. This is especially critical when dealing with microbiome variable selection since classical differential abundance tests are known to provide large false positive rates.Results We developed coda4microbiome, a new R package for analyzing microbiome data within the Compositional Data Analysis (CoDA) framework in both, cross-sectional and longitudinal studies. The core functions of the library are aimed at the identification of microbial signatures and involve variable selection in generalized linear models with compositional covariates. All algorithms are accompanied by meaningful graphical representations that allow a better interpretation of the results.Availability coda4microbiome is implemented as an R package and is available at CRAN https://cran.r-project.org/web/packages/coda4microbiome/index.html.Contact malu.calle@uvic.catSupplementary information coda4microbiome project website: https://malucalle.github.io/coda4mi-crobiome/.Competing Interest StatementThe authors have declared no competing interest.