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

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, app...

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Vydáno v:Genome Biology Ročník 23; číslo 1; s. 95
Hlavní autoři: Zhou, Huijuan, He, Kejun, Chen, Jun, Zhang, Xianyang
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 14.04.2022
Springer Nature B.V
BMC
Témata:
Animal Genetics and Genomics
Animals
Bias
Bioinformatics
Biomedical and Life Sciences
Coleoptera
Compositional effect
Differential abundance analysis
Evolutionary Biology
False discovery rate
genome
Human Genetics
Life Sciences
Linear Models
Method
Methods
Microbial Genetics and Genomics
microbiome
Microbiomes
Microbiota
Microorganisms
Multiple testing
Normal distribution
Numerical analysis
Performance evaluation
Plant Genetics and Genomics
Regression analysis
Research Design
Statistical analysis
Taxonomy
False discovery rate
Differential abundance analysis
Multiple testing
Compositional effect
ISSN:1474-760X, 1474-7596, 1474-760X
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-022-02655-5