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Activity Number: 244 - Statistical methods for microbiome data analysis and beyond
Type: Contributed
Date/Time: Wednesday, August 11, 2021 : 10:00 AM to 11:50 AM
Sponsor: WNAR
Abstract #317845
Title: TimeNorm: A Novel Normalization Method for Time Course Microbiome Data
Author(s): Qianwen Luo* and Meng Lu and Nicholas Lytal and Lingling An
Companies: University of Arizona and University of Arizona and University of Arizona and University of Arizona
Keywords: microbiome; normalization; longitudinal

Next-generation sequencing technology has been widely used in recent years. Metagenomic time-series studies provide insights to investigate the dynamics of the microbial systems. With the increased interest and the reduction of the cost of sequencing, there are more data available and call for statistical analysis methods. Normalizing the microbial data is a very common but critical preprocessing step before performing downstream analysis. Considering the compositional property and time dependency in time course microbiome data we propose a novel normalization method, TimeNorm. As both the within and across time point normalizations are taken into account the proposed method surpasses the existing normalization methods.

Authors who are presenting talks have a * after their name.

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