Identifying unmeasured heterogeneity in microbiome data via quantile thresholding (QuanT).
Overview
abstract
Microbiome data exhibit technical and biomedical heterogeneity due to varied processing and experimental designs, which may lead to spurious results if uncorrected. Here, we introduce the Quantile Thresholding (QuanT) method, a comprehensive non-parametric hidden variable inference method that accommodates the complex distributions of microbial read counts and relative abundances. We apply QuanT to synthetic and real data sets and demonstrate its ability to identify unmeasured heterogeneity and improve downstream analysis.
