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Title: Set testing methods based on zero-inflated models for microbiome data Authors:  Chong Wang - Iowa State University (United States) [presenting]
Abstract: With advances in sequencing methods, the study of the microbiome has greatly increased. Microbiome data, in the form of an OTU or ASV count table, can be used to identify specific ASVs that function differently across treatment conditions. Such analysis is deemed differential abundance analysis. ASVs are grouped by their taxonomic rank, and ASVs sharing the same rank have similar biological traits. By studying groups or sets of ASVs, and identifying if the set is differentially abundant, the biological interpretation of a microbiome study is enhanced. We review current approaches in set testing methods and apply them to a microbiome data set from a 2017 study. We propose a new set testing method based on an existing Poisson hurdle model and compare performance across all methods through a simulation study. We find that our proposed model outperforms existing approaches with zero-inflated observations.