A0387
Title: QuanT: Identifying unmeasured heterogeneity in microbiome data via quantile thresholding
Authors: Ni Zhao - Johns Hopkins University (United States) [presenting]
Abstract: Unmeasured technical and biomedical heterogeneity in microbiome data can arise from differential processing and design. Uncorrected for, they can lead to spurious results. The quantile thresholding (QuanT) approach is proposed, a comprehensive non-parametric hidden variable inference method that accommodates the complex distributions of microbial read counts. QuanT is applied to synthetic and real data sets and demonstrates its ability to identify unmeasured heterogeneity and improve downstream analysis.
