High-dimensional log-error-in-variable regression with applications to microbial compositional data analysis

Log contrast models for experiments with mixtures

Pixu Shi, Yuchen Zhou and Anru R Zhang

Biometrika, 2022, vol. 109, issue 2, 405-420

Abstract: SummaryIn microbiome and genomic studies, the regression of compositional data has been a crucial tool for identifying microbial taxa or genes that are associated with clinical phenotypes. To account for the variation in sequencing depth, the classic log-contrast model is often used where read counts are normalized into compositions. However, zero read counts and the randomness in covariates remain critical issues. We introduce a surprisingly simple, interpretable and efficient method for the estimation of compositional data regression through the lens of a novel high-dimensional log-error-in-variable regression model. The proposed method provides corrections on sequencing data with possible overdispersion and simultaneously avoids any subjective imputation of zero read counts. We provide theoretical justifications with matching upper and lower bounds for the estimation error. The merit of the procedure is illustrated through real data analysis and simulation studies.

Keywords: Compositional data; Error-in-variable; High-dimensional regression; Microbiome study (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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