Key points are not available for this paper at this time.
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 binomial regression model and correct for consistent batch influences by excluding fixed batch effects. Additionally, to handle nonsystematic batch effects, we employ composite quantile regression. By adjusting the distribution of OTUs to be similar based on a reference batch selected using the Kruskal-Walis test method, we consider the variability at the OTU level. Results: The performance of the model is evaluated and compared with existing methods using PERMANOVA R-squared values, Principal Coordinates Analysis (PCoA) plots and Average Silhouette Coefficient calculated with diverse distance-based metrics. The model is applied to three real microbiome datasets: Metagenomic urine control data, Human Immunodeficiency Virus Re-analysis Consortium data, and Men and Women Offering Understanding of Throat HPV study data. The results demonstrate that the model effectively corrects for batch effects across all datasets.
Park et al. (Tue,) studied this question.