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Title: A novel normalization and differential abundance test framework for microbiome data Authors:  Hongmei Jiang - Northwestern University (United States) [presenting]
Abstract: Microbial communities have been proved to have close relationship with many diseases. The identification of differentially abundant microbial species is clinically meaningful for finding disease-related pathogenic or probiotic bacteria. However, certain characteristics of microbiome data have hurdled the accuracy and effectiveness of differential abundance analysis. We develop a novel framework for differential abundance analysis on sparse high-dimensional marker gene microbiome data. The methodology relies on a network-based normalization technique and a two stage zero-inflated mixture count regression model (RioNorm2). Our novel network-based normalization method aims to find a group of relatively invariant species across samples and environments in order to construct size factors. It does not make any assumption on count distributions. Our testing approach can take into consideration under-sampling and over-dispersion with flexibility by separating microbiome species into different subgroups and model them separately. Through comprehensive simulation studies, the performance of our method is consistently powerful and robust across different settings with different sample sizes, library sizes and effect sizes. We also demonstrate the effectiveness of our novel framework using a published dataset of Metastatic Melanoma and find biological insights from the results.