Evaluating supervised and unsupervised background noise correction in human gut microbiome data.

ABSTRACT: The ability to predict human phenotypes and identify biomarkers of disease from metagenomic data is crucial for the development of therapeutics for microbiome-associated diseases. However, metagenomic data is commonly affected by technical variables unrelated to the phenotype of interest, such as sequencing protocol, which can make it difficult to predict phenotype and find biomarkers of disease. Supervised methods to correct for background noise, originally designed for gene expression and RNA-seq data, are commonly applied to microbiome data but may be limited because they cannot account for unmeasured sources of variation. Unsupervised approaches address this issue, but current methods are limited because they are ill-equipped to deal with the unique aspects of microbiome data, which is

SUBMITTER: Briscoe L 

PROVIDER: S-EPMC8853548 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature