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...

Full description

Saved in:

Bibliographic Details
Published in:	Genome Biology Vol. 23; no. 1; p. 95
Main Authors:	Zhou, Huijuan, He, Kejun, Chen, Jun, Zhang, Xianyang
Format:	Journal Article
Language:	English
Published:	London BioMed Central 14.04.2022 Springer Nature B.V BMC
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!