LinDA: linear models for differential abundance analysis of microbiome compositional data View Full Text


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Article Info

DATE

2022-04-14

AUTHORS

Huijuan Zhou, Kejun He, Jun Chen, Xianyang Zhang

ABSTRACT

Differential abundance analysis is at the core of statistical analysis of microbiome data. The compositional nature of microbiome sequencing data makes false positive control challenging. Here, we show that the compositional effects can be addressed by a simple, yet highly flexible and scalable, approach. The proposed method, LinDA, only requires fitting linear regression models on the centered log-ratio transformed data, and correcting the bias due to compositional effects. We show that LinDA enjoys asymptotic FDR control and can be extended to mixed-effect models for correlated microbiome data. Using simulations and real examples, we demonstrate the effectiveness of LinDA. More... »

PAGES

95

References to SciGraph publications

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  • 2016-11-25. Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies in MICROBIOME
  • 2010-03-02. A scaling normalization method for differential expression analysis of RNA-seq data in GENOME BIOLOGY
  • 2020-09-04. Gut microbiota in human metabolic health and disease in NATURE REVIEWS MICROBIOLOGY
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  • 1986. The Statistical Analysis of Compositional Data in NONE
  • 2017-03-03. Normalization and microbial differential abundance strategies depend upon data characteristics in MICROBIOME
  • 2014-12-05. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 in GENOME BIOLOGY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13059-022-02655-5

    DOI

    http://dx.doi.org/10.1186/s13059-022-02655-5

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1147122194

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/35421994


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