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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
