Composite quantile regression approach to batch effect correction in microbiome data.

ABSTRACT:

Background

Batch effects refer to data variations that arise from non-biological factors such as experimental conditions, equipment, and external factors. These effects are considered significant issues in the analysis of biological data since they can compromise data consistency and distort actual biological differences, which can severely skew the results of downstream analyses.

Method

In this study, we introduce a new approach that comprehensively addresses two types of batch effects: "systematic batch effects" which are consistent across all samples in a batch, and "nonsystematic batch effects" which vary depending on the variability of operational taxonomic units (OTUs) within each sample in the same batch. To address systematic batch effects, we apply a negative binomi

SUBMITTER: Park J 

PROVIDER: S-EPMC11893821 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature