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coda4microbiome: compositional data analysis for microbiome studies

View ORCID ProfileM.Luz Calle, View ORCID ProfileAntoni Susin
doi: https://doi.org/10.1101/2022.06.09.495511
M.Luz Calle
1Biosciences Department, Faculty of Sciences, Technology and Engineering, University of Vic - Central University of Catalonia, Vic, Spain
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  • For correspondence: malu.calle@uvic.cat
Antoni Susin
2Mathematical Department, UPC-Barcelona Tech, Barcelona, Spain
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Abstract

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.cat

Supplementary information coda4microbiome project website: https://malucalle.github.io/coda4mi-crobiome/.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://malucalle.github.io/coda4microbiome/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted June 11, 2022.
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coda4microbiome: compositional data analysis for microbiome studies
M.Luz Calle, Antoni Susin
bioRxiv 2022.06.09.495511; doi: https://doi.org/10.1101/2022.06.09.495511
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coda4microbiome: compositional data analysis for microbiome studies
M.Luz Calle, Antoni Susin
bioRxiv 2022.06.09.495511; doi: https://doi.org/10.1101/2022.06.09.495511

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